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Even though memory denial-of-service attacks can cause severe performance degradations on co-located virtual machines, a previous detection scheme against such attacks cannot accurately detect the attacks and also generates high detection delay and high performance overhead since it assumes that cache-related statistics of an application follow the same probability distribution at all times, which may not be true for all types of applications. In this paper, we present the experimental results showing the impacts of memory DoS attacks on different types of cloud-based applications. Based on these results, we propose two lightweight and responsive Statistical based Detection Schemes (SDS/B and SDS/P) that can detect such attacks accurately. SDS/B constructs a profile of normal range of cache-related statistics for all applications and use statistical methods to infer an attack when the real-time collected statistics exceed this normal range, while SDS/P exploits the increased periods of access patterns for periodic applications to infer an attack. Upon SDS, we further leverage deep neural network (DNN) techniques to design a DNN-based detection scheme that is general to various types of applications and more robust to adaptive attack scenarios. Our evaluation results show that SDS/B, SDS/P and DNN outperform the state-of-the-art detection scheme, e.g., with 65% higher specificity, 40% shorter detection delay, and 7% less performance overhead. We also discuss how to use SDS and DNN-based detection schemes under different situations. [ABSTRACT FROM AUTHOR]

To support higher demand for datacenter networks, networking researchers have proposed hybrid electrical/optical datacenter networks (Hybrid-DCN) that leverages optical circuit switching (OCS) along with traditional electrical packet switching (EPS). However, due to the high reconfiguration delay of OCS, OCS is used only for bulk data transfers between racks to amortize the reconfiguration delay. Existing job schedulers for data-parallel frameworks are not designed for Hybrid-DCN, since they neither place tasks to aggregate data traffic to take advantage of OCS, nor schedule tasks to minimize the Coflow completion time (CCT). In this paper, we describe the mismatch between existing job schedulers and the advanced Hybrid-DCN, introduce the requirements for the new scheduler, and present the implementation of Co-scheduler, a job scheduler for data-parallel frameworks that aims to improve job performance by placing the tasks of jobs to aggregate enough data traffic to better leverage OCS to minimize the CCT in Hybrid-DCN. Specifically, for every job, Co-scheduler computes guidelines on how many racks to place the job’s input data and the job’s tasks. The guidelines are dynamically generated based on the real-time job characteristics or predictable job characteristics from prior runs, with the aim of leveraging OCS whenever possible and efficient and minimizing CCT of jobs. Co-scheduler then schedules the tasks of jobs based on the guidelines. We evaluate the effectiveness of Co-scheduler using trace-driven simulation. The evaluation demonstrates that Co-scheduler can improve makespan, average job completion time, and average CCT of a workload by up to 56%, 61%, and 79%, respectively, compared to the state-of-the-art schedulers. [ABSTRACT FROM AUTHOR]

Data availability is one of the most important performance factors in cloud storage systems. To enhance data availability, replication is a common approach to handle the machine failures. However, previously proposed replication schemes cannot effectively handle both correlated and non-correlated machine failures, especially while increasing the data availability with limited resources. The schemes for correlated machine failures must create a constant number of replicas for each data object, which often neglects diverse data popularities and does not utilize the resource to maximize the expected data availability. Also, the previous schemes neglect the consistency maintenance cost and the storage cost caused by replication. It is critical for cloud providers to maximize data availability (hence minimize SLA violations) while minimizing costs caused by replication in order to maximize the revenue. In this paper, we build a nonlinear integer programming model to maximize data availability in both types of failures, and therefore minimize the cost caused by replication. Based on the model’s solution for the replication degree of each data object, we propose a low-cost multi-failure (correlated and non-correlated machine failures) resilient replication scheme (MRR). MRR can effectively handle both correlated and non-correlated machine failures, considers data popularities to enhance data availability, and also tries to minimize consistency maintenance and storage cost. Extensive numerical results from trace parameters and experiments from real-world Amazon S3 demonstrate that MRR achieves high data availability, low data loss probability and low consistency maintenance and storage costs when compared to previous replication schemes. [ABSTRACT FROM AUTHOR]

So far, there is no file replication method that tries to reduce data loss in correlated node failures, which however are common in Disruption Tolerant Networks (DTNs). In this paper, we propose a distributed file replication method (called MobileCopy) in DTNs, which aims to achieve low probability of totally losing a file at the expense of having a high number of impacted files in an individual large-scale correlated node failure. MobileCopy is designed for community-based file sharing systems. It has two main components: i) data loss resistant and popularity aware file replication, and ii) distributed hash table (DHT)-based file replica indexing. MobileCopy considers file popularity to determine the number of replicas of a file in each community. Through limiting the possible combination of candidate replica holders, MobileCopy greatly reduces the probability of node failures that will lead to data loss, i.e., losing all replicas of a file. Moreover, MobileCopy enables nodes to efficiently store and fetch the placement information of file replicas through competition based file replication and considering node mobility throughput among communities. Extensive trace-driven experiments demonstrate the effectiveness of MobileCopy against correlated node failures compared with previous methods. [ABSTRACT FROM AUTHOR]

Always keeping a certain distance between vehicles in a platoon system is important for collision avoidance. Centralized platoon systems let the leader vehicle determine and notify the velocities of all the vehicles in the platoon. Unfortunately, such a centralized method generates high packet drop rate and communication delay due to the leader vehicle’s limited communication capability. Therefore, we propose a decentralized platoon network, in which each vehicle determines its velocity by only communicating with the vehicles in a short range. However, the multiple simultaneous transmissions between different pairs of vehicles may interfere with each other. By leveraging a typical feature of a platoon, we devise a channel allocation algorithm, called the Fast and Lightweight Autonomous channel selection algorithm (FLA), in which each vehicle determines its channel simply based on its distance to the leader vehicle. We also devise a strategy, in which a succeeding vehicle uses its stored common velocity profile when it is disconnected from its preceding vehicle and then adjusts its velocity once the connection is built. We conduct experiments on NS-3 and Matlab to evaluate the performance of our proposed methods and implement a real-world prototype by equipping vehicles with Android mobile devices. The experimental results demonstrate the superior performance of our decentralized platoon network over the previous centralized platoon networks. [ABSTRACT FROM AUTHOR]

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]

As the typical delay tolerant networks, the satellite networks possess the intermittent connections, the large-scale time delays and the time-varying topologies. Obviously, such features seriously affect the delivery of mission data with certain requirements on the traffic or latency. To decrease the delivery time, existing relay-based contact graph routing (CGR) method selects the earliest reachable path to forward mission data. However, this method cannot guarantee the transmission of a large amount data and wastes the rare contact opportunities. Therefore, in this paper, we focus on the transmission QoS problem of multiple missions over the satellite networks, and design a QoS support routing strategy to achieve multiple flow-maximizing paths with acceptable delivery delays. Especially, with the storage time aggregated graph (STAG), we construct an on-demand mission model to depict both the network dynamic characteristics and the different mission requirements, and then reduce the QoS support problem as a graph-based maximum flow problem. To solve this problem, one STAG-based multiple flow-maximizing routing scheme is proposed to ensure the mission QoS and match the rare network resources, which has low computation complexity. Finally, compared with CGR, simulation results demonstrate the proposed scheme can achieve a higher mission completion rate and resource utilization rate. [ABSTRACT FROM AUTHOR]

Packet routing is important for vehicular delay tolerant networks (VDTNs). Opportunistic routing algorithms based on historical records are insufficiently accurate in forwarder selection due to movement randomness of vehicles. Trajectory-based routing algorithms tackle vehicle movement randomness but cannot be directly used in VDTNs due to the dependence on APs. In this paper, we develop a distributed trajectory-based routing algorithm (called MobiT) for VDTNs. This non-trivial task faces three challenges. First, vehicle trajectories must be sufficiently collected. Second, the trajectories cannot be updated frequently due to limited resources of the repository nodes. Third, achieving high routing performance even with partially collected trajectories. Our real trace study lays the foundation of the design of MobiT. Taking advantage of different roles of vehicles, MobiT uses service vehicles that move in wide areas to collect vehicle trajectories, and rely on the service vehicles and roadside units (called schedulers) for routing scheduling. By using regular temporal congestion state of road segments, MobiT schedules the packet to arrive at a roadside unit prior to the destination vehicle to improve routing performance. Furthermore, MobiT leverages vehicles’ long-term mobility patterns to assist routing. Our trace-driven simulation and real experiments show the effectiveness and efficiency of MobiT. [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]

To provide robust infrastructure as a service, clouds currently perform load balancing by migrating virtual machines (VMs) from heavily loaded physical machines (PMs) to lightly loaded PMs. Previous reactive load balancing algorithms migrate VMs upon the occurrence of load imbalance, while previous proactive load balancing algorithms predict PM overload to conduct VM migration. However, both methods cannot maintain long-term load balance and produce high overhead and delay due to migration VM selection and destination PM selection. To overcome these problems, in this paper, we propose a proactive Markov Decision Process (MDP)-based load balancing algorithm. We handle the challenges of allying MDP in virtual resource management in cloud datacenters, which allows a PM to proactively find an optimal action to transit to a lightly loaded state that will maintain for a longer period of time. We also apply the MDP to determine destination PMs to achieve long-term PM load balance state. Our algorithm reduces the numbers of service level agreement (SLA) violations by long-term load balance maintenance, and also reduces the load balancing overhead (e.g., CPU time and energy) and delay by quickly identifying VMs and destination PMs to migrate. We further propose enhancement methods for higher performance. First, we propose a cloud profit oriented reward system in the MDP model so that when the MDP tries to maximize the rewards for load balance, it concurrently improves the actual profit of the datacenter. Second, we propose a new MDP model, which considers the actions for both migrating a VM out of a PM and migrating a VM into a PM, in order to reduce the overhead and improve the effectiveness of load balancing. Our trace-driven experiments show that our algorithm outperforms both previous reactive and proactive load balancing algorithms in terms of SLA violation, load balancing efficiency, and long-term load balance maintenance. Our experimental results also show the effectiveness of our proposed enhancement methods. [ABSTRACT FROM PUBLISHER]

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]

Node searching in delay tolerant networks is of great importance for different applications, in which a locator node finds a target node in person. In the previous distributed node searching method, a locator traces the target along its movement path from its most frequently visited location. For this purpose, nodes leave traces during their movements and also store their long-term movement patterns in their frequently visited locations (i.e., preferred locations). However, such tracing leads to a long delay and high overhead on the locator by long-distance moving. Our trace data study confirms these problems and provides the foundation of our design of a new node searching method, called target-oriented method (TSearch). By leveraging social network properties, TSearch aims to enable a locator to directly move toward the target. Nodes create encounter records (ERs) indicating the locations and times of their encounters and make the ERs easily accessible by locators through message exchanges or a hierarchical structure. In node searching, a locator follows the target’s latest ER, the latest ERs of its friends (i.e., frequently meeting nodes), its preferred locations, and the target’s possible locations deduced from additional information for node searching. Extensive trace-driven and real-world experiments show that TSearch achieves significantly higher success rate and lower delay in node searching compared with previous methods. [ABSTRACT FROM PUBLISHER]

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]

Cooperative adaptive cruise control (CACC) systems have the potential to increase traffic throughput by allowing smaller headway between vehicles and moving vehicles safely in a platoon at a harmonized speed. CACC systems have been attracting significant attention from both academia and industry since connectivity between vehicles will become mandatory for new vehicles in the USA in the near future. In this paper, we review three basic and important aspects of CACC systems: communications, driver characteristics, and controls to identify the most challenging issues for their real-world deployment. Different routing protocols that support the data communication requirements between vehicles in the CACC platoon are reviewed. Promising and suitable protocols are identified. Driver characteristics related issues, such as how to keep drivers engaged in driving tasks during CACC operations, are discussed. To achieve mass acceptance, the control design needs to depict real-world traffic variability such as communication effects, driver behavior, and traffic composition. Thus, this paper also discusses the issues that existing CACC control modules face when considering close to ideal driving conditions. [ABSTRACT FROM PUBLISHER]

Underlying link-layer protocols of well-established wireless networks that use the conventional “store-and-forward” design paradigm cannot provide highly sustainable reliability and stability in wireless communication, which introduce significant barriers and setbacks in scalability and deployments of wireless networks. In this paper, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency and balancing en/decoding load among nodes. CEDAR is the first comprehensive theoretical framework for analyzing and designing distributed and adaptive error recovery for wireless networks. It employs a theoretically sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in a packet’s route. To identify the intermediate nodes for the decoding, we mathematically calculate the average packet delay and formalize the problem as a nonlinear integer programming problem. By minimizing the delays, we derive three propositions that: 1) can identify the intermediate nodes that minimize the propagation and transmission delay of a packet; and 2) and 3) can identify the intermediate nodes that simultaneously minimize the queuing delay and maximize the fairness of en/decoding load of all the nodes. Guided by the propositions, we then propose a scalable and distributed scheme in CEDAR to choose the intermediate en/decoding nodes in a route to achieve its objective. The results from real-world testbed “NESTbed” and simulation with MATLAB prove that CEDAR is superior to schemes using hop-by-hop decoding and destination decoding not only in packet delay and throughput but also in energy-consumption and load distribution balance. [ABSTRACT FROM AUTHOR]

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]