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Large-scale data stores are an increasingly important component of cloud datacenter services. However, cloud storage system usually experiences data loss, hindering data durability. Three-way random replication is commonly used to lead better data durability in cloud storage systems. However, three-way random replication cannot effectively handle correlated machine failures to prevent data loss. Although Copyset Replication and Tiered Replication can reduce data loss in correlated and independent failures, and enhance data durability, they fail to leverage different data popularities to substantially reduce the storage cost and bandwidth cost caused by replication. To address these issues, we present a popularity-aware multi-failure resilient and cost-effective replication (PMCR) scheme for high data durability in cloud storage. PMCR splits the cloud storage system into primary tier and backup tier, and classifies data into hot data, warm data and cold data based on data popularities. To handle both correlated and independent failures, PMCR stores the three replicas of the same data into one Copyset formed by two servers in the primary tier and one server in the backup tier. For the third replicas of warm data and cold data in the backup tier, PMCR uses the compression methods to reduce storage cost and bandwidth cost. Extensive numerical results based on trace parameters and experimental results from real-world Amazon S3 show that PMCR achieves high data durability, low probability of data loss, and low storage cost and bandwidth cost compared to previous replication schemes. [ABSTRACT FROM AUTHOR]

The planning and operation problems of parking lots of plug-in electric vehicles (PEVs) are studied in this paper. Herein, each distribution company (DISCO) allocates the parking lots to the electrical feeders to minimize the power loss and expected energy not supplied of the system, and consequently minimize the total cost of the planning problem over the given time horizon. In addition, the generation company (GENCO) manages the charging time of PEVs parked in the parking lots to defer the more expensive and pollutant generation units, and as a result maximize its daily profit. In both planning and operation problems, the behavioral model of PEVs’ drivers are modeled with respect to the value of incentive and their distance from the parking lots. To achieve the realistic results in the planning problem of each DISCO, several economic and technical factors including yearly inflation and interest rates, hourly and daily variations of the load demand, yearly load growth of the system, and yearly growth rate of PEVs’ application are considered. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing algorithm and genetic algorithm, respectively. It is demonstrated that the behavioral model of drivers, their driving patterns, and even the type of PEVs can remarkably affect the outcomes of planning and operation problems. It is shown that the optimal allocation of parking lots can minimize every DISCO’s planning cost and optimal charging management of PEVs can increase the GENCO’s daily profit. [ABSTRACT FROM AUTHOR]

In this paper, price-controlled energy management is investigated in a bi-level optimization framework, that is, energy scheduling problem of smart homes (SHs) and generation scheduling and unit commitment (UC) problems of a generation company (GENCO). SHs as the responsive customers (respect to the energy management) include a variety of sources such as photovoltaic (PV) panels, diesel generator, and battery as an energy storage. In addition, SHs are able to transact electricity with the GENCO through the power system. In this paper, the goal of GENCO is to design an optimal energy management scheme (optimal price of electricity) to maximize its daily profit. Herein, each SH reacts to the energy management scheme and reschedules its energy resources to minimize its daily operation cost. In this paper, a scenario-based stochastic optimization approach is applied in the energy scheduling problem of an SH to address the variability and uncertainty issues of the PV panels. Also, a combination of genetic algorithm (GA) and linear programming is applied as the optimization tool for the energy scheduling problem of an SH. Moreover, lambda-iteration economic dispatch and GA techniques are applied to solve the generation scheduling and UC problems of the GENCO, respectively. The numerical study demonstrates that in order to reach the maximum profit of GENCO, the energy management must be optimally designed and implemented; otherwise, the energy management scheme may result in detriment. Moreover, it is shown that each SH is able to get benefit from the energy management scheme and minimize its daily operation cost. [ABSTRACT FROM AUTHOR]

A capillary-based chemiluminescence immunoassay system using a charge-coupled device (CCD) camera as detector was established in this paper. The fused quartz capillary was easily activated in one step for immobilizing capture antibody, and the chemiluminescence immunoassay was carried out in the capillary in double-antibody sandwich format. Chemiluminescence signals were recorded by the portable imaging device which was installed with the CCD camera and the results were analyzed through gray intensity. The total cost time, which included not only the time for test but also the time for the preparation of experimental materials, was only 2 h. The immunoassay was performed without any complicated or expensive instruments. The consumption of the sample was only 0.8 μL in one test, which was significantly less than other methods. In this work, C-reactive protein (CRP), as a target, was quantitatively detected from 0.3 to 160.0 μg mL−1 with high specificity and low sample volume. The reproducibility and accuracy were tested in clinic human serum samples and shown good results. Thus, this rapid, easy preparation and using, portable immunoassay system indicated its usefulness as a novel technology platform.ᅟ [ABSTRACT FROM AUTHOR]

In vehicle delay tolerant networks (VDTNs), current routing algorithms select relay vehicles based on either vehicle encounter history or predicted future locations. The former method may fail to find relays that can encounter the target vehicle in a large-scale VDTN, while the latter method may not provide accurate location prediction due to traffic variance. Therefore, these methods cannot achieve high performance in terms of routing success rate and delay. In this paper, we aim to improve the routing performance in VDTNs. We first analyze vehicle network traces and observe that 1) each vehicle has only a few active subareas that it frequently visits, and 2) two frequently encountered vehicles usually encounter each other in their active subareas. We then propose an active area based routing method (AAR), which consists of two steps based on the two observations correspondingly. AAR first distributes a packet copy to each active subarea of the target vehicle using a traffic-aware shortest path spreading algorithm, and then in each subarea, each packet carrier tries to forward the packet to a vehicle that has high encounter frequency with the target vehicle. In addition to the basic AAR, we further propose an advanced AAR (AAAR). In the AAAR, we improve the routing efficiency in each subarea by exploiting spatio-temporal correlation and developing three strategies for calculating spatio-temporal correlation. Extensive trace-driven simulation demonstrates that AAR produces higher success rates and shorter delay in comparison with the state-of-the-art routing algorithms in VDTNs. Also, the simulation shows that our advanced AAAR has better performances than our AAR. [ABSTRACT FROM PUBLISHER]

In the cloud, multiple tenants share the resource of datacenters and their applications compete with each other for scarce network bandwidth. Current studies have shown that the lack of bandwidth guarantee causes unpredictable network performance, leading to poor application performance. To address this issue, several virtual network abstractions have been proposed which allow the tenants to reserve virtual clusters with specified bandwidth between the Virtual Machines (VMs) in the datacenters. However, all these existing proposals require the tenants to deterministically characterize the bandwidth demands in the abstractions, which can be difficult and result in inefficient bandwidth reservation due to the demand uncertainty. In this paper, we explore a virtual cluster abstraction with stochastic bandwidth characterization to address the bandwidth demand uncertainty. We propose Stochastic Virtual Cluster (SVC), which models the bandwidth demand between VMs in a probabilistic way. Based on SVC, we develop a stochastic framework for virtual cluster allocation, in which the admitted virtual cluster's bandwidth demands are satisfied with a high probability. Efficient VM allocation algorithms are proposed to implement the framework while reducing the possibility of link congestion through minimizing the maximum bandwidth occupancy of a virtual cluster on physical links. Using simulations, we show that SVC achieves the trade-off between the job concurrency and the average job running time, and demonstrate its effectiveness for accommodating cloud application workloads with highly volatile bandwidth demands and its improvement to work-conserving bandwidth enforcement. [ABSTRACT FROM AUTHOR]

Yahoo! Answers is an online platform where users can post questions and answer other users’ questions. Our previous work studied the online social network (OSN) of Yahoo! Answers by analyzing information from the profiles (including fans, contacts, and interests) of top contributors and their related users. Rather than using the static profile information from the top-contributor-centered dataset, in this paper, we particularly analyze the actual questioning and answering (Q/A) behaviors of normal users. We build a Q/A network that unidirectionally connects each asker to his/her answerers. We analyze the structural characteristics of the Q/A network, user Q/A activities, and knowledge base of all users. In addition to the observations similar to our previous study, which indicates that the OSN of Yahoo! Answers can reflect user Q/A activities to a certain extent, we additionally observe that: 1) a large portion of users only ask questions without answering others’ questions; 2) users are active in more knowledge categories than those indicated in their profiles; and 3) the knowledge categories of the top-contributor-related users cannot represent those of normal users. Finally, we analyze the characteristics of questions and answers in different knowledge categories. This paper not only provides an understanding of actual Q/A activities of users but also showcases the aspects of Q/A activities that the OSN of Yahoo! Answers can and cannot accurately reflect. Based on the insights gained from this paper, we propose a few methods to help improve the quality of service of Yahoo! Answers. [ABSTRACT FROM PUBLISHER]

MapReduce is a popular computing model for parallel data processing on large-scale datasets, which can vary from gigabytes to terabytes and petabytes. Though Hadoop MapReduce normally uses Hadoop Distributed File System (HDFS) local file system, it can be configured to use a remote file system. Then, an interesting question is raised: for a given application, which is the best running platform among the different combinations of scale-up and scale-out Hadoop with remote and local file systems. However, there has been no previous research on how different types of applications (e.g., CPU-intensive, data-intensive) with different characteristics (e.g., input data size) can benefit from the different platforms. Thus, in this paper, we conduct a comprehensive performance measurement of different applications on scale-up and scale-out clusters configured with HDFS and a remote file system (i.e., OFS), respectively. We identify and study how different job characteristics (e.g., input data size, the number of file reads/writes, and the amount of computations) affect the performance of different applications on the different platforms. Based on the measurement results, we also propose a performance prediction model to help users select the best platforms that lead to the minimum latency. Our evaluation using a Facebook workload trace demonstrates the effectiveness of our prediction model. This study is expected to provide a guidance for users to choose the best platform to run different applications with different characteristics in the environment that provides both remote and local storage, such as HPC cluster and cloud environment. [ABSTRACT FROM PUBLISHER]

It is important for cloud service brokers to provide a multi-cloud storage service to minimize their payment cost to cloud service providers (CSPs) while providing service level objective (SLO) guarantee to their customers. Many multi-cloud storage services have been proposed or payment cost minimization or SLO guarantee. However, no previous works fully leverage the current cloud pricing policies (such as resource reservation pricing) to reduce the payment cost. Also, few works achieve both cost minimization and SLO guarantee. In this paper, we propose a multi-cloud Economical and SLO-guaranteed Storage Service ($ES^3$ ), which determines data allocation and resource reservation schedules with payment cost minimization and SLO guarantee. $ES^3$ incorporates (1) a coordinated data allocation and resource reservation method, which allocates each data item to a datacenter and determines the resource reservation amount on datacenters by leveraging all the pricing policies; (2) a genetic algorithm based data allocation adjustment method, which reduce data Get/Put rate variance in each datacenter to maximize the reservation benefit. We also propose several algorithms to enhance the cost efficient and SLO guarantee performance of $ES^3$ including i) dynamic request redirection, ii) grouped Gets for cost reduction, iii) lazy update for cost-efficient Puts, and iv) concurrent requests for rigid Get SLO guarantee. Our trace-driven experiments on a supercomputing cluster and on real clouds (i.e., Amazon S3, Windows Azure Storage and Google Cloud Storage) show the superior performance of $ES^3$ in payment cost minimization and SLO guarantee in comparison with previous methods. [ABSTRACT FROM AUTHOR]

Background: Cerebral ischemia-reperfusion injury (CIRI) can cause irreversible brain damage and autophagy has been implicated in the pathophysiology. Increasing serum potassium (K+) levels reduces CIRI, but the relationship between its protective mechanism and autophagy is unclear. In this study, we aimed to find the optimal degree of raising serum (K+) and to investigate the relationship between high (K+) and autophagy and the underlying mechanisms in a cardiac arrest/cardiopulmonary resuscitation (CA/CPR) rat model. Methods: Sprague Dawley (SD) rats were divided into four groups: S group, N group, P group, and Q group. The rats S group and N group were administered saline. The rats P group and Q group were administered 640 mg/kg of potassium chloride (KCl) continuously pumped at 4 mL/h (21.3 mg/(kg·min) and divided according to the electrocardiogram (ECG) changes during the administration of KCl. After 24-h of resuscitation, neural damage was assessed by measuring neurological deficit score (NDS), oxidative stress markers, and pathological staining of the cerebral cortex. The level of autophagy and the expression of mTOR-ULK1-Beclin1 pathway-related proteins were evaluated using transmission electron microscopy (TEM), immunostaining, and western blotting. Results: Our results revealed that high (K+) improved NDS and decreased the oxidative stress markers. The autophagosomes, autolysosomes, and lysosomes were decreased following treatment KCl. Furthermore, the levels of micro-tubule-associated protein 1 light chain 3 (LC3) Ⅱ/Ⅰ, Unc-51-like kinase 1 (ULK1), and Beclin1 were decreased, whereas mTOR expression was increased in the cortex. Conclusion: The results demonstrated that moderate hyperkalemia could alleviate autophagy after CIRI via regulating the mTOR-ULK1-Beclin1 pathway. [ABSTRACT FROM AUTHOR]

In large-scale computing clusters, when the server storing a task's requested data does not have sufficient computing capacity for the task, current job schedulers either schedule the task to the closest server and transmit to it the requested data, or let the task wait until the server has sufficient computing capacity. The former solution generates network load while the latter solution increases task delay. To handle this problem, load balancing methods are needed to reduce the number of overloaded servers due to computing workloads. However, current load balancing methods do not aim to balance the computing load for the long term. Through trace analysis, we demonstrate the diversity of computing workloads of different tasks and the necessity of balancing the computing workloads among servers. Then, we propose a cost-efficient Computing load Aware and Long-View load balancing approach (CALV ). CALV is novel in that it achieves long-term computing load balance by migrating out an overloaded server data blocks contributing more computing workloads when the server is more overloaded and contribute less computing workloads when the server is more underloaded at different epochs during a time period. Based upon the task schedules, we further propose a task reassignment algorithm that reassigns tasks from an overloaded server to other data servers of the tasks to make it non-overloaded before CALV is conducted. The above methods are for the tasks whose submission times and execution latencies can be predicted. To handle unexpected tasks or insufficiently accurate predictions, we propose a dynamic load balancing method, in which an overloaded server dynamically redirects tasks to other data servers of the tasks, or replicates the tasks’ requested data to other servers and redirects the tasks to those servers in order to become non-overloaded. Finally, we propose a proximity-aware tree based distributed load balancing method to reduce the reallocation cost and improve the scalability of CALV. Trace-driven experiments in simulation and a real computing cluster show that CALV outperforms other methods in terms of balancing the computing workloads and cost efficiency. [ABSTRACT FROM AUTHOR]

Traditional web-based Question and Answer (Q&A) websites cannot easily solve non-factual questions to match askers’ preference. Recent research efforts begin to study social-based Q&A systems that rely on an asker's social friends to provide answers. However, this method cannot find answerers for a question not belonging to the asker's interests. To solve this problem, we propose a distributed Q&A system incorporating both social community intelligence and global collective intelligence, named as iASK. iASK improves the response latency and answer quality in both the social domain and global domain. It uses a neural network based friend ranking method to identify answerer candidates by considering social closeness and Q&A activities. To efficiently identify answerers in the global user base, iASK builds a virtual server tree that embeds the hierarchical structure of interests, and also maps users to the tree based on user interests. To accurately locate the cooperative experts, iASK has a fine-grained reputation system to evaluate user reputation based on their cooperativeness and expertise, and uses a reputation based reward strategy to encourage users to be cooperative. To further improve the performance of iASK, we propose a weak tie assisted social based potential answerer location algorithm and an interest coefficient based uncategorized question forwarding algorithm. To further improve the response quality and cooperativeness, we propose a reputation based reward strategy that motivates users to answer questions from unknown users. Experimental results from large-scale trace-driven simulation and real-world daily usages of the iASK prototype show the superior performance of iASK. It achieves high answer quality with 24 percent higher accuracy, short response latency with 53 percent less delay and effective cooperative incentives with 16 percent more answers compared to other social-based Q&A systems. The results also show the effectiveness of the enhancement algorithms in improving the performance of iASK. [ABSTRACT FROM AUTHOR]

In data intensive clusters, a large amount of files are stored, processed and transferred simultaneously. To increase the data availability, some file systems create and store three replicas for each file in randomly selected servers across different racks. However, they neglect the file heterogeneity and server heterogeneity, which can be leveraged to further enhance data availability and file system efficiency. As files have heterogeneous popularities, a rigid number of three replicas may not provide immediate response to an excessive number of read requests to hot files, and waste resources (including energy) for replicas of cold files that have few read requests. Also, servers are heterogeneous in network bandwidth, hardware configuration and capacity (i.e., the maximal number of service requests that can be supported simultaneously), it is crucial to select replica servers to ensure low replication delay and request response delay. In this paper, we propose an Energy-Efficient Adaptive File Replication System (EAFR), which incorporates three components. It is adaptive to time-varying file popularities to achieve a good tradeoff between data availability and efficiency. Higher popularity of a file leads to more replicas and vice versa. Also, to achieve energy efficiency, servers are classified into hot servers and cold servers with different energy consumption, and cold files are stored in cold servers. EAFR then selects a server with sufficient capacity (including network bandwidth and capacity) to hold a replica. To further improve the performance of EAFR, we propose a dynamic transmission rate adjustment strategy to prevent potential incast congestion when replicating a file to a server, a network-aware data node selection strategy to reduce file read latency, and a load-aware replica maintenance strategy to quickly create file replicas under replica node failures. Experimental results on a real-world cluster show the effectiveness of EAFR and proposed strategies in reducing file read latency, replication time, and power consumption in large clusters. [ABSTRACT FROM PUBLISHER]

The pay-as-you-go service model impels cloud customers to reduce the usage cost of bandwidth. Traffic Redundancy Elimination (TRE) has been shown to be an effective solution for reducing bandwidth costs, and thus has recently captured significant attention in the cloud environment. By studying the TRE techniques in a trace driven approach, we found that both short-term (time span of seconds) and long-term (time span of hours or days) data redundancy can concurrently appear in the traffic, and solely using either sender-based TRE or receiver-based TRE cannot simultaneously capture both types of traffic redundancy. Also, the efficiency of existing receiver-based TRE solution is susceptible to the data changes compared to the historical data in the cache. In this paper, we propose a Cooperative end-to-end TRE solution (CoRE) that can detect and remove both short-term and long-term redundancy through a two-layer TRE design with cooperative operations between layers. An adaptive prediction algorithm is further proposed to improve TRE efficiency through dynamically adjusting the prediction window size based on the hit ratio of historical predictions. Besides, we enhance CoRE to adapt to different traffic redundancy characteristics of cloud applications to improve its operation cost. Extensive evaluation with several real traces show that CoRE is capable of effectively identifying both short-term and long-term redundancy with low additional cost while ensuring TRE efficiency from data changes. [ABSTRACT FROM PUBLISHER]

Scale-up machines perform better for jobs with small and median (KB, MB) data sizes, while scale-out machines perform better for jobs with large (GB, TB) data size. Since a workload usually consists of jobs with different data size levels, we propose building a hybrid Hadoop architecture that includes both scale-up and scale-out machines, which however is not trivial. The first challenge is workload data storage. Thousands of small data size jobs in a workload may overload the limited local disks of scale-up machines. Jobs from scale-up and scale-out machines may both request the same set of data, which leads to data transmission between the machines. The second challenge is to automatically schedule jobs to either scale-up or scale-out cluster to achieve the best performance. We conduct a thorough performance measurement of different applications on scale-up and scale-out clusters, configured with Hadoop Distributed File System (HDFS) and a remote file system (i.e., OFS), respectively. We find that using OFS rather than HDFS can solve the data storage challenge. Also, we identify the factors that determine the performance differences on the scale-up and scale-out clusters and their cross points to make the choice. Accordingly, we design and implement the hybrid scale-up/out Hadoop architecture. Our trace-driven experimental results show that our hybrid architecture outperforms both the traditional Hadoop architecture with HDFS and with OFS in terms of job completion time, throughput and job failure rate. [ABSTRACT FROM PUBLISHER]

With the increasing popularity of Massively Multiplayer Online Game (MMOG) and fast growth of mobile gaming, cloud gaming exhibits great promises over the conventional MMOG gaming model as it frees players from the requirement of hardware and game installation on their local computers. However, as the graphics rendering is offloaded to the cloud, the data transmission between the end-users and the cloud significantly increases the response latency and limits the user coverage, thus preventing cloud gaming to achieve high user Quality of Service (QoS). To solve this problem, previous research suggested deploying more datacenters, but it comes at a prohibitive cost. We propose a lightweight system called CloudFog, which incorporates “fog” consisting of supernodes that are responsible for rendering game videos and streaming them to their nearby players. Fog enables the cloud to be only responsible for the intensive game state computation and sending update information to supernodes, which significantly reduce the traffic hence the latency and bandwidth consumption. To further enhance QoS, we propose the reputation based supernode selection strategy to assign each player with a suitable supernode that can provide satisfactory game video streaming service, the receiver-driven encoding rate adaptation strategy to increase the playback continuity, the social network based server assignment strategy to avoid the communication interaction between servers in a datacenter to reduce latency, and the dynamic supernode provisioning strategy to deal with user churns. Experimental results from PeerSim and PlanetLab show the effectiveness and efficiency of CloudFog and our individual strategies in increasing user coverage, reducing response latency and bandwidth consumption. [ABSTRACT FROM PUBLISHER]

In many countries, the Internet is under stringent censorship for political or religious reasons which severely undermines the free flow of information. A censorship-resistant web browsing system must be scalable, blocking resistant, and tracing resistant. However, current censorship-resistant web browsing systems, which use a group of dedicated proxies to bypass censorship, fail to meet these requirements. To tackle these challenges, we propose Freeweb, which relies on widely-distributed peer-to-peer (P2P) nodes in a decentralized manner rather than specified proxies in a centralized manner. We also proposed enhancement methods to reduce file access delay and avoid node overloads in Freeweb. Freeweb is built on top of a Distributed Hash Table (DHT)-based P2P network, where nodes not under censorship help nodes under censorship to access blocked webpages. Freeweb has a web browser front-end whose user interface resembles existing web browsers. The underlying complex process of retrieving blocked webpages is therefore hidden from users. We implemented an open-sourced Freeweb and conducted extensive real-world experiments on PlanetLab. The experimental results show that Freeweb has a high success rate and reasonable browsing latency, and its enhancement reduces much network load and file access latency, and avoids node overloads. [ABSTRACT FROM PUBLISHER]

It is imperative for cloud storage systems to be able to provide deadline guaranteed services according to service level agreements (SLAs) for online services. In spite of many previous works on deadline aware solutions, most of them focus on scheduling work flows or resource reservation in datacenter networks but neglect the server overload problem in cloud storage systems that prevents providing the deadline guaranteed services. In this paper, we introduce a new form of SLAs, which enables each tenant to specify a percentage of its requests it wishes to serve within a specified deadline. We first identify the multiple objectives (i.e., traffic and latency minimization, resource utilization maximization) in developing schemes to satisfy the SLAs. To satisfy the SLAs while achieving the multi-objectives, we propose a Parallel Deadline Guaranteed (PDG) scheme, which schedules data reallocation (through load re-assignment and data replication) using a tree-based bottom-up parallel process. The observation from our model also motivates our deadline strictness clustered data allocation algorithm that maps tenants with the similar SLA strictness into the same server to enhance SLA guarantees. We further enhance PDG in supplying SLA guaranteed services through two algorithms: i) a prioritized data reallocation algorithm that deals with request arrival rate variation, and ii) an adaptive request retransmission algorithm that deals with SLA requirement variation. Our trace-driven experiments on a simulator and Amazon EC2 show the effectiveness of our schemes for guaranteeing the SLAs while achieving the multi-objectives. [ABSTRACT FROM AUTHOR]

For predictable application performance or fairness in network sharing in clouds, many bandwidth allocation policies have been proposed. However, with these policies, tenants are not incentivized to use idle bandwidth or prevent link congestion, and may even take advantage of the policies to gain unfair bandwidth allocation. Increasing network utilization while avoiding congestion not only benefits cloud provider but also the tenants by improving application performance. In this paper, we propose a new pricing model that sets different unit prices for reserved bandwidth, the bandwidth on congested links and on uncongested links, and makes the unit price for congested links proportional to their congestion degrees. We use game theory model to analyze tenants’ behaviors in our model and the current pricing models, which shows the effectiveness of our model in providing the incentives. With the pricing model, we propose a network sharing policy to achieve both min-guarantee and proportionality, while prevent tenants from earning unfair bandwidth. We further propose methods for each virtual machine to arrange its traffic to reduce its unsatisfied demand and maximize its utility, while increase network utilization. As a result, our solution creates a win-win situation, where tenants strive to increase their benefits in bandwidth sharing, which also concurrently increases the utilities of cloud provider and other tenants. Our simulation and trace-driven experimental results show the effectiveness of our solutions in creating the win-win situation. [ABSTRACT FROM PUBLISHER]

Though the new OSN model, which deploys datacenters globally, helps reduce service latency, it causes higher inter-datacenter communication load. In Facebook, each datacenter has a full copy of all data, and the master datacenter updates all other datacenters, generating tremendous load in this new model. Distributed data storage, which only stores a user's data to his/her geographically closest datacenters mitigates the problem. However, frequent interactions between distant users lead to frequent inter-datacenter communication and hence long service latencies. In this paper, we aim to reduce inter-datacenter communications while still achieving low service latency. We first verify the benefits of the new model and present OSN typical properties that underlie the basis of our design. We then propose Selective Data replication mechanism in Distributed Datacenters (SD3). Since replicas need inter-datacenter data updates, datacenters in SD3 jointly consider update rates and visit rates to select user data for replication; furthermore, SD3 atomizes users’ different types of data (e.g., status update, friend post, music) for replication, ensuring that a replica always reduces inter-datacenter communication. SD3 also incorporates three strategies to further enhance its performance: locality-aware multicast update tree, replica deactivation, and datacenter congestion control. The results of trace-driven experiments on the real-world PlanetLab testbed demonstrate the higher efficiency and effectiveness of SD3 in comparison to other replication methods and the effectiveness of its three schemes. [ABSTRACT FROM AUTHOR]

Real-world applications, such as peer-to-peer (P2P) networks, e-commerce and social networks, usually employ reputation systems to provide guidance in selecting trustworthy node for high system reliability and security. A reputation system computes and publishes reputation score for each node based on a collection of opinions from others about the node. However, collusion behaviors impair the effectiveness of reputation systems in trustworthy node selection. Though many reputation calculation methods have been proposed to mitigate collusion's influence, little effort has been devoted to specifically tackling collusion. Based on the important collusion behavior characteristics in reputation evaluation and influence on reputation values, we propose a basic collusion detection method to specifically detect suspicious collusion behaviors in pairs. We further optimize the method by reducing the computing overhead. We also propose two pre-processing methods to firstly identify partial reputation raters of a node that are more likely to be colluders before applying the collusion detection method on them, thus reducing the collusion detection overhead. Extensive experimental results show that our proposed methods can significantly enhance the capability of existing reputation systems to detect collusion with low overhead. Also, the pre-processing methods are effective in reducing the collusion detection overhead without affecting the collusion detection accuracy. [ABSTRACT FROM AUTHOR]

Content Delivery Networks (CDNs) play a central role in today's Internet infrastructure and have seen a sharp increase in scale. More and more internet sites are armed with live contents, such as live sports game statistics, e-commerce, and online auctions, and they rely on CDNs to deliver such contents freshly at scale. However, the problem of maintaining consistency for live (dynamic) contents while achieving high scalability is non-trivial in CDNs. The large number of widely scattered replicas guarantees the QoS of end-users while substantially increasing the complexity of consistency maintenance under frequent updates. Current consistency maintenance infrastructures and methods cannot simultaneously satisfy both scalability and consistency. In this paper, we first analyze our crawled trace data of cached sports game content on thousands of content servers of a major CDN. We analyze the content consistency from different perspectives, from which we break down the reasons for inconsistency among content servers. We verify that the CDN uses unicast instead of multicast trees as the update infrastructure, which may not scale effectively. Then, we further evaluate the performance in consistency, scalability and overhead for different infrastructures with different update methods. We itemize the advantages and disadvantages of different methods and infrastructures in different scenarios through the evaluation. Based on this evaluation, we propose our hybrid and self-adaptive update method to reduce network load and improve scalability under the conditions recorded in the trace and prove its effectiveness through trace-driven experiments. We aim to give guidance for appropriate selections of consistency maintenance infrastructures and methods for a CDN, and for choosing a CDN service with different considerations. [ABSTRACT FROM AUTHOR]

Distributed file searching in delay tolerant networks formed by mobile devices can potentially support various useful applications. In such networks, nodes often present certain social network properties of their holders in terms of contents (i.e., interests) and contacts. However, current methods in DTNs only consider either content or contact for file searching or dissemination, which limits the file sharing efficiency. In this paper, we first analyze real traces to confirm the importance and necessity of considering both content and contact in file search. We then propose Cont2, a social-aware file search method that exploits both node contents and contact patterns. First, considering people with common interests tend to share files and gather together, Cont2 virtually groups common-interest nodes into a community to direct file search. Second, considering human mobility follows a certain pattern, Cont2 exploits nodes' contact frequencies with a community to expedite file searching. To further improve the searching efficiency, Cont2 also integrates sub-communities and parallel forwarding as optional components for file searching. Trace-driven experiments on the GENI testbed and NS-2 simulator show that Cont2 can effectively improve the search efficiency compared to current methods. [ABSTRACT FROM AUTHOR]

Though a cooperative broadcast scheme has been proposed for fading environments, it has two defects: First, it only handles a packet flow from a single source node in the network, but does not consider the scenario of multiple packet flows simultaneously broadcasted from different source nodes. Second, it only allows a single relay node to forward a packet in each time slot, though multiple relay nodes forwarding in a time slot can significantly reduce broadcast latency. In this paper, we aim achieve low-latency multi-flow broadcast in wireless multi-hop networks with fading channels. To describe the interference among the transmission in different flows, we incorporate the Rayleigh fading model to the signal to noise ratio (SNR) model. Then, we introduce a cooperative diversity scheme which allows multiple relays forwarding in a time slot to reduce broadcast latency. We then formulate an interesting problem: In a fading environment, what is the optimal relay allocation schedule to minimize the broadcast latency? We propose a warm up heuristic algorithm for single-flow cooperative broadcast, based on which, we further propose a heuristic algorithm for multi-flow cooperative broadcast. Simulation results demonstrate that the two algorithms achieve lower broadcast latency than a previous method. [ABSTRACT FROM PUBLISHER]

In peer-to-peer file sharing systems, file replication helps to avoid overloading file owners and improve file query efficiency. There exists a tradeoff between minimizing the number of replicas (i.e., replication overhead) and maximizing the replica hit rate (which reduces file querying latency). More replicas lead to increased replication overhead and higher replica hit rates and vice versa. An ideal replication method should generate a low overhead burden to the system while providing low query latency to the users. However, previous replication methods either achieve high hit rates at the cost of many replicas or produce low hit rates. To reduce replicas while guaranteeing high hit rate, this paper presents SWARM, a file replication mechanism based on swarm intelligence. Recognizing the power of collective behaviors, SWARM identifies node swarms with common node interests and close proximity. Unlike most earlier methods, SWARM determines the placement of a file replica based on the accumulated query rates of nodes in a swarm rather than a single node. Replicas are shared by the nodes in a swarm, leading to fewer replicas and high querying efficiency. In addition, SWARM has a novel consistency maintenance algorithm that propagates an update message between proximity-close nodes in a tree fashion from the top to the bottom. Experimental results from the real-world PlanetLab testbed and the PeerSim simulator demonstrate the effectiveness of the SWARM mechanism in comparison with other file replication and consistency maintenance methods. SWARM can reduce querying latency by 40-58 percent, reduce the number of replicas by 39-76 percent, and achieves more than 84 percent higher hit rates compared to previous methods. It also can reduce the consistency maintenance overhead by 49-99 percent compared to previous consistency maintenance methods. [ABSTRACT FROM AUTHOR]

A peer-to-peer (P2P) file sharing system provides a platform that enables a tremendous number of nodes to share their files. Retrieving desired files efficiently and trustworthily is critical in such a large and jumbled system. However, the issues of efficient searching and trustworthy searching have only been studied separately. Simply combining the methods to achieve the two goals doubles system overhead. In this paper, we first study trace data from Facebook and BitTorrent. Guided by the observations, we propose a system that integrates a social network into a P2P network, named Social-P2P, for simultaneous efficient and trustworthy file sharing. It incorporates three mechanisms: (1) interest/trust-based structure, (2) interest/trust-based file searching, and (3) trust relationship adjustment. By exploiting the social interests and relationships in the social network, the interest/trust-based structure groups common-multi-interest nodes into a cluster and further connects socially close nodes within a cluster. The comparably stable nodes in each cluster form a Distributed Hash Table (DHT) for inter-cluster file searching. In the interest/trust-based file searching mechanism, a file query is forwarded to the cluster of the file by the DHT routing first. Then, it is forwarded along constructed connections within a cluster, which achieves high hit rate and reliable routing. Moreover, sharing files among socially close friends discourages nodes from providing faulty files because people are unlikely to risk their reputation in the real-world. In the trust relationship adjustment mechanism, each node in a routing path adaptively decreases its trust on the node that has forwarded a faulty file in order to avoid routing queries towards misbehaving nodes later on. We conducted extensive trace-driven simulations and implemented a prototype on PlanetLab. Experimental results show that Social-P2P achieves highly efficient and trustworthy file sharing compared to current file sharing systems and trust management systems. [ABSTRACT FROM AUTHOR]

Current reputation systems for peer-to-peer (P2P) file sharing networks suffer from high overhead on reputation querying. Also, purely relying on a threshold to detect malicious nodes may make a high-reputed node be reluctant to further increase its reputation in these reputation systems. On the other side, the social network concept of ”friendship foster cooperation” can be utilized to alleviate the high overhead in reputation systems. However, the limited number of friends limits the availability of file resources in these approaches. To overcome the drawbacks, we propose a social network based reputation system, namely SocialTrust, that synergistically leverages the social network connections and traditional credit based reputation system to provide efficient reputation management for P2P file sharing. In SocialTrust, each node favors friends for service transactions, which are resulted from both real life acquaintances and online partnerships established between high-reputed and frequently-interacted nodes. When no friends are available for a request, a node chooses the server with the highest reputation. The benefits of friendship and partnership on file sharing and cost saving encourage nodes to be continuously cooperative. Further, SocialTrust considers the number of friends/partners and the reputation of a node in reputation rewarding/punishment in order to realize accurate reputation evaluation. SocialTrust can also prevent certain attacks such as deny of service and collusion. Extensive trace-driven simulation demonstrates the effectiveness of SocialTrust. [ABSTRACT FROM AUTHOR]

Question and Answer (Q&A) websites such as Yahoo! Answers provide a platform where users can post questions and receive answers. These systems take advantage of the collective intelligence of users to find information. In this paper, we analyze the online social network (OSN) in Yahoo! Answers. Based on a large amount of our collected data, we studied the OSN’s structural properties, which reveals strikingly distinct properties such as low link symmetry and weak correlation between indegree and outdegree. After studying the knowledge base and behaviors of the users, we find that a small number of top contributors answer most of the questions in the system. Also, each top contributor focuses only on a few knowledge categories. In addition, the knowledge categories of the users are highly clustered. We also study the knowledge base in a user’s social network, which reveals that the members in a user’s social network share only a few knowledge categories. Based on the findings, we provide guidance in the design of spammer detection algorithms and distributed Q&A systems. We also propose a friendship-knowledge oriented Q&A framework that synergistically combines current OSN-based Q&A and web Q&A. We believe that the results presented in this paper are crucial in understanding the collective intelligence in the web Q&A OSNs and lay a cornerstone for the evolution of next-generation Q&A systems. [ABSTRACT FROM PUBLISHER]

Efficient file query is important to the overall performance of peer-to-peer (P2P) file sharing systems. Clustering peers by their common interests can significantly enhance the efficiency of file query. Clustering peers by their physical proximity can also improve file query performance. However, few current works are able to cluster peers based on both peer interest and physical proximity. Although structured P2Ps provide higher file query efficiency than unstructured P2Ps, it is difficult to realize it due to their strictly defined topologies. In this work, we introduce a Proximity-Aware and Interest-clustered P2P file sharing System (PAIS) based on a structured P2P, which forms physically-close nodes into a cluster and further groups physically-close and common-interest nodes into a sub-cluster based on a hierarchical topology. PAIS uses an intelligent file replication algorithm to further enhance file query efficiency. It creates replicas of files that are frequently requested by a group of physically close nodes in their location. Moreover, PAIS enhances the intra-sub-cluster file searching through several approaches. First, it further classifies the interest of a sub-cluster to a number of sub-interests, and clusters common-sub-interest nodes into a group for file sharing. Second, PAIS builds an overlay for each group that connects lower capacity nodes to higher capacity nodes for distributed file querying while avoiding node overload. Third, to reduce file searching delay, PAIS uses proactive file information collection so that a file requester can know if its requested file is in its nearby nodes. Fourth, to reduce the overhead of the file information collection, PAIS uses bloom filter based file information collection and corresponding distributed file searching. Fifth, to improve the file sharing efficiency, PAIS ranks the bloom filter results in order. Sixth, considering that a recently visited file tends to be visited again, the bloom filter based approach is enhanced by only checking the newly added bloom filter information to reduce file searching delay. Trace-driven experimental results from the real-world PlanetLab testbed demonstrate that PAIS dramatically reduces overhead and enhances the efficiency of file sharing with and without churn. Further, the experimental results show the high effectiveness of the intra-sub-cluster file searching approaches in improving file searching efficiency. [ABSTRACT FROM PUBLISHER]

In Delay Tolerant Networks (DTNs), nodes meet opportunistically and exchange packets only when they meet with each other. Therefore, routing is usually conducted in a store-carry-forward manner to exploit the scarce communication opportunities. As a result, different packet routing strategies, i.e., which packet to be forwarded or stored with priority, can lead to different routing performance objectives, such as minimal average delay and maximal hit rate. On the other hand, incentive systems are necessary for DTNs since nodes may be selfish and may not be cooperative on packet forwarding/storage. However, current incentive systems for DTNs mainly focus on encouraging nodes to participate in packet forwarding/storage but fail to further encourage nodes to follow a certain packet routing strategy to realize a routing performance objective. We name the former as the first aspect of cooperation and the latter as the second aspect of cooperation in DTN routing. Therefore, in this paper, we first discuss the routing strategy that can realize different performance objectives when nodes are fully cooperative, i.e., are willing to follow both aspects of cooperation. We then propose Multicent, a game theoretical incentive scheme that can encourage nodes to follow the two aspects of cooperation even when they are selfish. Basically, Multicent assigns credits for packet forwarding/storage in proportional to the priorities specified in the routing strategy. Multicent also supports adjustable Quality of Service (QoS) for packet routing between specific sources and destinations. Extensive trace-driven experimental results verify the effectiveness of Multicent. [ABSTRACT FROM PUBLISHER]

File sharing applications in mobile ad hoc networks (MANETs) have attracted more and more attention in recent years. The efficiency of file querying suffers from the distinctive properties of such networks including node mobility and limited communication range and resource. An intuitive method to alleviate this problem is to create file replicas in the network. However, despite the efforts on file replication, no research has focused on the global optimal replica creation with minimum average querying delay. Specifically, current file replication protocols in mobile ad hoc networks have two shortcomings. First, they lack a rule to allocate limited resources to different files in order to minimize the average querying delay. Second, they simply consider storage as available resources for replicas, but neglect the fact that the file holders’ frequency of meeting other nodes also plays an important role in determining file availability. Actually, a node that has a higher meeting frequency with others provides higher availability to its files. This becomes even more evident in sparsely distributed MANETs, in which nodes meet disruptively. In this paper, we introduce a new concept of resource for file replication, which considers both node storage and meeting frequency. We theoretically study the influence of resource allocation on the average querying delay and derive a resource allocation rule to minimize the average querying delay. We further propose a distributed file replication protocol to realize the proposed rule. Extensive trace-driven experiments with synthesized traces and real traces show that our protocol can achieve shorter average querying delay at a lower cost than current replication protocols. [ABSTRACT FROM PUBLISHER]

A large-scale resource sharing system (e.g., collaborative cloud computing and grid computing) creates a virtual supercomputer by providing an infrastructure for sharing tremendous amounts of resources (e.g., computing, storage, and data) distributed over the Internet. A resource information service, which collects resource data and provides resource search functionality for locating desired resources, is a crucial component of the resource sharing system. In addition to resource discovery speed and cost (i.e., efficiency), the ability to accurately locate all satisfying resources (i.e., fidelity) is also an important metric for evaluating service quality. Previously, a number of resource information service systems have been proposed based on Distributed Hash Tables (DHTs) that offer scalable key-based lookup functions. However, these systems either achieve high fidelity at low efficiency, or high efficiency at low fidelity. Moreover, some systems have limited flexibility by only providing exact-matching services or by describing a resource using a pre-defined list of attributes. This paper presents a resource information service that offers high efficiency and fidelity without restricting resource expressiveness, while also providing a similar-matching service. Extensive simulation and PlanetLab experimental results show that the proposed service outperforms other services in terms of efficiency, fidelity, and flexibility; it dramatically reduces overhead and yields significant enhancements in efficiency and fidelity. [ABSTRACT FROM AUTHOR]

Advances in ubiquitous sensing, computing and wireless communication technologies are leading to the development of cyber-physical systems (CPS), which promise to revolutionize the way we interact with the physical world. CPS applications, such as healthcare monitoring, may involve many users and objects scattered over a wide area. One critical function of CPS is object search in the physical world through the cyber sphere that enables interaction between the cyber and physical spheres. Some of the previously proposed physical object search engines use RFID tracking, and others collect the information of object locations into a hierarchical centralized server. The difficulty of widely deploying RFID devices, the centralized search, and the need for periodical location information collection prevent CPS from achieving higher scalability and efficiency. To deal with this problem, we propose a Social-aware distributed Cyber-Physical human-centric Search engine (SCPS) that leverages the social network formed by wireless device users for object search. Without requiring periodical location information collection, SCPS locates objects held by users based on the routine user movement pattern. Moreover, using a social-aware Bayesian network, it can accurately predict the users’ locations even at the occurrence of exceptional (i.e., non-routine) events (e.g., raining) that break user movement pattern. Thus, SCPS is more advantageous than all previous social network based works which assume that user behaviors always follow a certain pattern. Further, SCPS conducts the search in a fully distributed manner by relying on a distributed hash table (DHT) structure. As a result, SCPS achieves high scalability, efficiency and location accuracy. Extensive real-trace driven simulation results show the superior performance of SCPS compared to other representative search methods including a hierarchical centralized method, a decentralized method, and two social network based methods. The results also show the effectiveness of different components of SCPS. [ABSTRACT FROM AUTHOR]

The explosive growth of unsolicited e-mails has prompted the development of numerous spam filter techniques. Bayesian spam filters are superior to static keyword-based spam filters in that they can continuously evolve to tackle new spam by learning keywords in new spam emails. However, Bayesian spam filters are easily poisoned by clever spammers who avoid spam keywords and add many innocuous words in their emails. Also, Bayesian spam filters need a significant amount of time to adapt to a new spam based on user feedback. Moreover, few current spam filters exploit social networks to assist in spam detection. In order to develop an accurate and user-friendly spam filter, we propose a SOcial network Aided Personalized and effective spam filter (SOAP) in this paper. In SOAP, each node connects to its social friends; i.e., nodes form a distributed overlay by directly using social network links as overlay links. Each node uses SOAP to collect information and check spam autonomously in a distributed manner. Unlike previous spam filters that focus on parsing keywords (e.g., Bayesian filters) or building blacklists, SOAP exploits the social relationships among email correspondents and their (dis)interests to detect spam adaptively and automatically. In each node, SOAP integrates four components into the basic Bayesian filter: social closeness-based spam filtering, social interest-based spam filtering, adaptive trust management, and friend notification. We have evaluated the performance of SOAP using simulation based on trace data from Facebook. We also have implemented a SOAP prototype for real-world experiments. Experimental results show that SOAP can greatly improve the performance of Bayesian spam filters in terms of accuracy, attack-resilience, and efficiency of spam detection. The performance of the Bayesian spam filter is SOAP’s lower bound. [ABSTRACT FROM AUTHOR]

Tremendous advances in pervasive networking have enabled wide-area distributed systems to connect distributed resources or users such as corporate data centers and high-performance computing centers. These distributed pervasive systems take advantage of resources and enhance collaborations worldwide. However, due to lack of central management, they are severely threatened by a variety of malicious users in today's Internet. Current reputation- and anonymity-based technologies for node communication enhance system trustworthiness. However, most of these technologies gain trustworthiness at the cost of efficiency degradation. This paper presents a P2P-based infrastructure for trustworthy and efficient node communication in wide-area distributed systems. It jointly addresses trustworthiness and efficiency in its operation in order to meet the high-performance requirements of a diversified wealth of distributed pervasive applications. The infrastructure includes two policies: trust/efficiency-oriented request routing and trust-based adaptive anonymous response forwarding. This infrastructure not only offers a trustworthy environment with anonymous communication but also enhances overall system efficiency through harmonious trustworthiness and efficiency trade-offs. Experimental results from simulations and the real-world PlanetLab testbed show the superior performance of the P2P-based infrastructure in achieving both high trustworthiness and high efficiency in comparison to other related approaches. [ABSTRACT FROM AUTHOR]

Video sharing has been an increasingly popular application in online social networks (OSNs). However, its sustainable development is severely hindered by the intrinsic limit of the client/server architecture deployed in current OSN video systems, which is not only costly in terms of server bandwidth and storage but also not scalable with the soaring amount of users and video content. The peer-assisted Video-on-Demand (VoD) technique, in which participating peers assist the server in delivering video content, has been proposed recently. Unfortunately, videos can only be disseminated through friends in OSNs. Therefore, current VoD works that explore clustering nodes with similar interests or close location for high performance are suboptimal, if not entirely inapplicable, in OSNs. Based on our long-term real-world measurement of over 1,000,000 users and 2,500 videos on Facebook, we propose SocialTube, a novel peer-assisted video sharing system that explores social relationship, interest similarity, and physical location between peers in OSNs. Specifically, SocialTube incorporates four algorithms: a social network (SN)-based P2P overlay construction algorithm, an SN-based chunk prefetching algorithm, chunk delivery, and scheduling algorithm, and a buffer management algorithm. Experimental results from a prototype on PlanetLab and an event-driven simulator show that SocialTube can improve the quality of user experience and system scalability over current P2P VoD techniques. [ABSTRACT FROM PUBLISHER]

Background: Anxiety may adversely impact mother and her newborn. Music listening is a safe and efficacious treatment that may to reduce perioperative anxiety. The effect on acute pain and pain catastrophizing scores remains unclear. We aimed to determine whether perioperative music listening reduces anxiety, acute pain, and pain catastrophizing scale (PCS) scores following elective cesarean delivery under spinal anesthesia. Methods: After randomization into music listening and control groups, baseline patient characteristics, visual analog scale-anxiety (VAS-A) scores, pain scores, PCS total and sub-scores, and music preferences were collected preoperatively. Before surgery, parturients in the experimental group listened to music of their own choice for 30 min. Music listening was continued during administration of spinal anesthesia and cesarean delivery, and for 30 min following surgery. Postoperative VAS-A score, acute pain score, PCS scores, music preferences, satisfaction score, and feedback were recorded. Results: We analyzed 108 parturients (music: n = 53; control: n = 55). Music listening was associated with reduced postoperative VAS-A (mean difference (MD) -1.43, 95%CI -0.63 to -2.22), PCS total score (MD -6.39, 95%CI -2.11 to -10.66), PCS sub-scores on rumination (MD -1.68, 95%CI -0.12 to -3.25), magnification (MD -1.53, 95%CI -0.45 to -2.62), and helplessness (MD -3.17, 95%CI -1.29 to -5.06) sub-scores. There was no significant difference in postoperative acute pain scores. The majority (> 95%) of parturients reported "excellent" and "good" satisfaction with music listening, and most provided positive feedback. Conclusion: Perioperative music listening was associated with reduced postoperative anxiety and lower pain catastrophizing. Based on the good patient satisfaction and positive feedback received, the use of music listening in the obstetric setting is recommended. Trial registration: This study was registered on Clinicaltrials.gov NCT03415620 on 30/01/2018. [ABSTRACT FROM AUTHOR]

Radio frequency identification (RFID) and wireless sensor networks (WSNs) have been popular in the industrial field, and both have undergone dramatic development. RFID and WSNs are well known for their abilities in identity identification and data transmission, respectively, and hence widely used in applications for environmental and health monitoring. Though the integration of a sensor and an RFID tag was proposed to gather both RFID tag and sensed information, few previous research efforts explore the integration of data transmission modes in the RFID and WSN systems to enhance the performance of the applications. In this paper, we propose a hybrid RFID and WSN system (HRW) that synergistically integrates the traditional RFID system and WSN system for efficient data collection. HRW has hybrid smart nodes that combine the function of RFID tags, the reduced function of RFID readers, and wireless sensors. Therefore, nodes can read each other's sensed data in tags, and all data can be quickly transmitted to an RFID reader through the node that first reaches it. The RFID readers transmit the collected data to the back-end servers for data processing and management. We also propose methods to improve data transmission efficiency and to protect data privacy and avoid malicious data selective forwarding in data transmission. Comprehensive simulation and trace-driven experimental results show the high performance of HRW in terms of the cost of deployment, transmission delay and capability, and tag capacity requirement. [ABSTRACT FROM AUTHOR]

This paper addresses the data search problem in large-scale highly mobile and dense wireless networks. Current wireless network data search systems are not suitable for large-scale highly mobile and dense wireless networks. This paper presents a scalable and mobility-resilient LOcality-based distRibuted Data search system (LORD) for large-scale wireless networks with high mobility and density. Taking advantage of the high density, rather than mapping data to a location point, LORD maps file metadata to a geographical region and stores it in multiple nodes in the region, thus enhancing mobility-resilience. LORD has a novel region-based geographic data routing protocol that does not rely on flooding or GPSs for data publishing and querying, and a coloring-based partial replication algorithm to reduce data replicas in a region while maintaining the querying efficiency. LORD also works for unbalanced wireless networks with sparse regions. Simulation results show the superior performance of LORD compared to representative data search systems in terms of scalability, overhead, and mobility resilience in a highly dense and mobile network. The results also show the high scalability and mobility-resilience of LORD in an unbalanced wireless network with sparse regions, and the effectiveness of its coloring-based partial replication algorithm. [ABSTRACT FROM AUTHOR]

Recently, emerging research efforts have been focused on question and answer (Q&A) systems based on social networks. The social-based Q&A systems can answer non-factual questions, which cannot be easily resolved by web search engines. These systems either rely on a centralized server for identifying friends based on social information or broadcast a user's questions to all of its friends. Mobile Q&A systems, where mobile nodes access the Q&A systems through Internet, are very promising considering the rapid increase of mobile users and the convenience of practical use. However, such systems cannot directly use the previous centralized methods or broadcasting methods, which generate high cost of mobile Internet access, node overload, and high server bandwidth cost with the tremendous number of mobile users. We propose a distributed Social-based mObile Q&A System (SOS) with low overhead and system cost as well as quick response to question askers. SOS enables mobile users to forward questions to potential answerers in their friend lists in a decentralized manner for a number of hops before resorting to the server. It leverages lightweight knowledge engineering techniques to accurately identify friends who are able to and willing to answer questions, thus reducing the search and computation costs of mobile nodes. The trace-driven simulation results show that SOS can achieve a high query precision and recall rate, a short response latency and low overhead. We have also deployed a pilot version of SOS for use in a small group in Clemson University. The feedback from the users shows that SOS can provide high-quality answers. [ABSTRACT FROM PUBLISHER]

Advancements in cloud computing are leading to a promising future for collaborative cloud computing (CCC), where globally-scattered distributed cloud resources belonging to different organizations or individuals (i.e., entities) are collectively used in a cooperative manner to provide services. Due to the autonomous features of entities in CCC, the issues of resource management and reputation management must be jointly addressed in order to ensure the successful deployment of CCC. However, these two issues have typically been addressed separately in previous research efforts, and simply combining the two systems generates double overhead. Also, previous resource and reputation management methods are not sufficiently efficient or effective. By providing a single reputation value for each node, the methods cannot reflect the reputation of a node in providing individual types of resources. By always selecting the highest-reputed nodes, the methods fail to exploit node reputation in resource selection to fully and fairly utilize resources in the system and to meet users’ diverse QoS demands. We propose a CCC platform, called Harmony, which integrates resource management and reputation management in a harmonious manner. Harmony incorporates three key innovations: integrated multi-faceted resource/reputation management, multi-QoS-oriented resource selection, and price-assisted resource/reputation control. The trace data we collected from an online trading platform implies the importance of multi-faceted reputation and the drawbacks of highest-reputed node selection. Simulations and trace-driven experiments on the real-world PlanetLab testbed show that Harmony outperforms existing resource management and reputation management systems in terms of QoS, efficiency and effectiveness. [ABSTRACT FROM PUBLISHER]

Peer-to-peer networks (P2Ps) use reputation systems to provide incentives for nodes to offer high quality of service (QoS) and thwart the intentions of dishonest or selfish nodes. Existing reputation systems have two problems. First, they directly regard node reputation as trust. Rather, reputation represents the opinions formed by others about a node's QoS behavior, while trust represents a node's honesty and willingness to cooperate. In addition to trust, factors such as node capacity and lifetime also influence reputation. Due to these factors' heterogeneity and variance over time, reputation cannot directly reflect a node's trust or current QoS. Second, existing reputation systems guide a node to select the server with the highest reputation, which may not actually select the highest QoS server and would overload the highest reputed nodes. This work aims to accurately reflect node trust and provide guidance for high-QoS server selection. Through experimental study, we find that node trust, available capacity, and lifetime positively affect node reputation. Based on this observation, we first propose a manual trust model and an automatic trust model that remove the influence of additional factors on reputation to truly reflect node trust. We then propose a high-QoS server selection algorithm that separately considers node trust, current available capacity, and lifetime. Extensive simulation results demonstrate the effectiveness of the trust models in accurate node trust reflection compared with an existing reputation system. Moreover, the server selection algorithm dramatically increases the success rate of service requests and avoids overloading nodes. [ABSTRACT FROM PUBLISHER]

File replication is widely used in structured P2P systems to avoid hot spots in servers and enhance file availability. The number of replicas and replication distance affect the file replication cost. These two elements and the replica update frequency determined in the file replication stage also affect the cost of subsequent consistency maintenance. However, most existing file replication protocols focus on improving file lookup efficiency without considering its cost and its subsequent influence on consistency maintenance. This paper studies the problem about how a server chooses files to replicate and where to replicate files to achieve low cost in both file replication and consistency maintenance stages without compromising the effectiveness of file replication. This paper presents a lightweight and Cooperative multifactOr considered file Replication Protocol (CORP) to achieve this goal. CORP simultaneously takes into account multiple factors including file popularity, update rate, node available capacity, file load, and node locality, aiming to minimize the number of replicas, update frequency, and replication distance. CORP also dynamically adjusts the number of replicas based on ever-changing file popularity and visit pattern. Extensive experimental results from simulation and PlanetLab real-world testbed demonstrate the efficiency and effectiveness of CORP in comparison with other file replication protocols. It dramatically reduces the overhead of both file replication and consistency maintenance. In addition, it exhibits high adaptiveness to skewed lookups and yields significant improvement in reducing overloaded nodes. Specifically, compared to the other replication protocols, CORP can reduce more than 71 percent of file replicas, 84 percent of overloaded nodes, 94 percent of consistency maintenance cost, and 72 percent of file replication and consistency maintenance latency. [ABSTRACT FROM AUTHOR]

File consistency maintenance in P2P systems is a technique for maintaining consistency between files and their replicas. Most previous consistency maintenance methods depend on either message spreading or structure-based pushing. Message spreading generates high overhead due to a large amount of messages; structure-based pushing methods reduce this overhead. However, both approaches cannot guarantee that every replica node receives an update in churn, because replica nodes passively wait for updates. As opposed to push-based methods that are not effective in high-churn and low-resource P2P systems, polling is churn resilient and generates low overhead. However, it is faced with a number of challenges: 1) ensuring a limited inconsistency; 2) realizing polling in a distributed manner; 3) considering physical proximity in polling; and 4) leveraging polling to further reduce polling overhead. To handle these challenges, this paper introduces a poll-based distributed file consistency maintenance method called geographically aware wave (GeWave). GeWave further reduces update overhead, enhances the fidelity of file consistency, and takes proximity into account. Using adaptive polling in a dynamic structure, GeWave avoids redundant file updates and ensures that every node receives an update in a limited time period even in churn. Furthermore, it propagates updates between geographically close nodes in a distributed manner. Extensive experimental results from the PlanetLab real-world testbed demonstrate the efficiency and effectiveness of GeWave in comparison with other representative consistency maintenance schemes. It dramatically reduces the overhead and yields significant improvements on effectiveness, scalability, and churn resilience of previous file consistency maintenance methods. [ABSTRACT FROM AUTHOR]

Internet-based video streaming applications are becoming more and more popular, attracting millions of online viewers every day. The incredible growth of viewers, dynamics of participants, and high video quality of service (QoS) requirement pose scalability, availability, and low-latency challenges to peer-to-peer (P2P) live video streaming systems. Tree-based systems have low-delay but are vulnerable to churn, while mesh-based systems are churn-resilient but suffer from high delay and overhead. Also, both structures cannot make full utilization of the bandwidth in the system. To tackle the challenges, we propose a DHT-aided chunk-driven overlay (DCO). It introduces a scalable DHT ring structure into a mesh-based overlay to efficiently manage video stream sharing. DCO includes a two-layer hierarchical DHT-based infrastructure, a chunk sharing algorithm, and a video provider selection algorithm. It selects stable nodes to form a scalable DHT-based infrastructure. The nodes in the DHT serve as distributed matchmakers between video providers and requesters. To motivate stable nodes to serve as the DHT nodes, we introduce an incentive mechanism based on the game theory. Aided by DHT, DCO guarantees stream chunk availability and assigns to a chunk requester a provider among all available providers in the system so that stream chunks are transmitted along a dynamic tree with top-down decreasing node bandwidth. In this way, DCO takes full advantage of available bandwidth in the system and, at the same time, provides high scalability and low latency. Experimental results show the superior performance of DCO compared with mesh-based and tree-based systems, and the effectiveness of the incentive mechanism and provider selection algorithm. [ABSTRACT FROM AUTHOR]

In peer-to-peer networks (P2Ps), many autonomous peers without preexisting trust relationships share resources with each other. Due to their open environment, the P2Ps usually employ reputation systems to provide guidance in selecting trustworthy resource providers for high reliability and security; however, node collusion impairs the effectiveness of reputation systems in trustworthy node selection. Although some reputation systems have certain mechanisms to counter collusion, the effectiveness of the mechanisms is not sufficiently high. In this paper, we leverage social networks to enhance the capability of reputation systems in combating collusion. We first analyzed real trace of the reputation system in the Overstock online auction platform, which incorporates a social network. The analysis reveals the impact of the social network on user purchasing and reputation rating patterns. We, thus, identified suspicious collusion behavior patterns and propose a social network-based mechanism, SocialTrust, to counter collusion. SocialTrust adaptively adjusts the weight of ratings based on the social distance and interest relationship between peers. Experiment results show that SocialTrust can significantly strengthen the capability of current reputation systems in combating collusion. [ABSTRACT FROM AUTHOR]

This work focuses on Delay Tolerant Networks (DTNs) in a social network environment. DTNs do not have a complete path from a source to a destination most of the time. Previous data routing approaches in DTNs are primarily based on either flooding or single-copy routing. However, these methods incur either high overhead due to excessive transmissions or long delays due to suboptimal choices for relay nodes. Probabilistic forwarding that forwards a message to a node with a higher delivery utility enhances single-copy routing. However, current probabilistic forwarding methods only consider node contact frequency in calculating the utility while neglecting the influence of contact duration on the throughput, though both contact frequency and contact duration reflect the node movement pattern in a social network. In this paper, we theoretically prove that considering both factors leads to higher throughput than considering only contact frequency. To fully exploit a social network for high throughput and low routing delay, we propose a Social network oriented and duration utility-based distributed multicopy routing protocol (SEDUM) for DTNs. SEDUM is distinguished by three features. First, it considers both contact frequency and duration in node movement patterns of social networks. Second, it uses multicopy routing and can discover the minimum number of copies of a message to achieve a desired routing delay. Third, it has an effective buffer management mechanism to increase throughput and decrease routing delay. Theoretical analysis and simulation results show that SEDUM provides high throughput and low routing delay compared to existing routing approaches. The results conform to our expectation that considering both contact frequency and duration for delivery utility in routing can achieve higher throughput than considering only contact frequency, especially in a highly dynamic environment with large routing messages. [ABSTRACT FROM AUTHOR]

In peer-to-peer file sharing systems, file replication and consistency maintenance are widely used techniques for high system performance. Despite significant iriterdependencies between them, these two issues are typically addressed separately. Most file replication methods rigidly specify replica nodes, leading to low replica utilization, unnecessary replicas and hence extra consistency maintenance overhead. Most consistency maintenance methods propagate update messages based on message spreading or a structure without considering file replication dynamism, leading to inefficient file update and hence high possibility of outdated file response. This paper presents an Integrated file Replication and consistency Maintenance mechanism (IRM) that integrates the two techniques in a systematic and harmonized manner. It achieves high efficiency in file replication and consistency maintenance at a significantly low cost. Instead of passively accepting replicas and updates, each node determines file replication and update polling by dynamically adapting to time-varying file query and update rates, which avoids unnecessary file replications and updates. Simulation results demonstrate the effectiveness of IRM in comparison with other approaches. It dramatically reduces overhead and yields significant improvements on the efficiency of both file replication and consistency maintenance approaches. [ABSTRACT FROM AUTHOR]

Abstract: File replication is a widely used technique for high performance in peer-to-peer content delivery networks. A file replication technique should be efficient and at the same time facilitates efficient file consistency maintenance. However, most traditional methods do not consider nodes’ available capacity and physical location in file replication, leading to high overhead for both file replication and consistency maintenance. This paper presents a proactive low-overhead file replication scheme, namely Plover. By making file replicas among physically close nodes based on nodes’ available capacities, Plover not only achieves high efficiency in file replication but also supports low-cost and timely consistency maintenance. It also includes an efficient file query redirection algorithm for load balancing between replica nodes. Theoretical analysis and simulation results demonstrate the effectiveness of Plover in comparison with other file replication schemes. It dramatically reduces the overhead of both file replication and consistency maintenance compared to other schemes. In addition, it yields significant improvements in reduction of overloaded nodes. [Copyright &y& Elsevier]

Abstract: Internet-based distributed systems enable globally-scattered resources to be collectively pooled and used in a cooperative manner to achieve unprecedented petascale supercomputing capabilities. Numerous resource discovery approaches have been proposed to help achieve this goal. To report or discover a multi-attribute resource, most approaches use multiple messages, with one message for each attribute, leading to high overhead of memory consumption, node communication, and subsequent merging operation. Another approach can report and discover a multi-attribute resource using one query by reducing multi-attribute to a single index, but it is not practically effective in an environment with a large number of different resource attributes. Furthermore, few approaches are able to locate resources geographically close to the requesters, which is critical to system performance. This paper presents a P2P-based intelligent resource discovery (PIRD) mechanism that weaves all attributes into a set of indices using locality sensitive hashing, and then maps the indices to a structured P2P overlay. PIRD can discover resources geographically close to requesters by relying on a hierarchical P2P structure. It significantly reduces overhead and improves search efficiency and effectiveness in resource discovery. It further incorporates the Lempel–Ziv–Welch algorithm to compress attribute information for higher efficiency. Theoretical analysis and simulation results demonstrate the efficiency of PIRD in comparison with other approaches. It dramatically reduces overhead and yields significant improvements on the efficiency of resource discovery. [Copyright &y& Elsevier]