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5G is a key tool for the cloud-based wireless production of audio-visual contents. By providing higher throughput and lower latency than previous mobile network technologies, as well as flexible allocation of network resources, it enables that some content production pipelines, which are currently implemented using wired connectivity, are provided on top of a 5G network. This brings down production costs, reduces environmental impact and increases operational efficiency. In particular, live immersive production services, such as free-viewpoint video (FVV), can be provided in a much more efficient way. In this paper, we address the challenge of producing and streaming FVV services in real time through 5G networks. We have adapted a state-of-the-art FVV system to integrate it within a 5G architecture, using three key technological enablers: mmWave radio access network to support the uplink traffic requirements from FVV cameras, multi-access edge computing to run video processing algorithms with minimum latency, and end-to-end slicing to guarantee a sufficient quality of service (QoS) within the production pipeline. We have built a field trial over the production network of a telecommunication operator, including a mmWave pilot deployment, edge cloud processing, and remote content production, involving three different locations across Spain. We have measured the key performance indicators at the relevant parts of our trial deployment, showing that, with existing 5G technology, it is possible to achieve live FVV production, although with some limitations. We have also analyzed such limitations, obtaining some insights on how the next generation of 5G networks can overcome them to achieve higher quality of experience (QoE). [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technology provisions unprecedented opportunities to evolve the interconnection among human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated Mobile Edge Computing (MEC) with remote clouds is a promising platform to enable delay-sensitive service provisioning for IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices. Thus, computation-intensive and sensing data from IoT devices can be offloaded to the MEC network immediately for processing, and the service response latency can be significantly reduced. In this paper, we first formulate two novel optimization problems for delay-sensitive IoT applications, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction on the use of the services provided by the MEC, and show the NP-hardness of the defined problems. We then devise efficient approximation and online algorithms with provable performance guarantees for the problems in a special case where the bandwidth capacity constraint is negligible. We also develop efficient heuristic algorithms for the problems with the bandwidth capacity constraint. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising in reducing service delays and enhancing user satisfaction, and the proposed algorithms outperform their counterparts by at least 10.8 percent. [ABSTRACT FROM AUTHOR]

The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness. [ABSTRACT FROM AUTHOR]

With the vision of Internet of Things gaining popularity at a global level, efficient publish/subscribe middleware for communication within and across data centers is extremely desirable. In this respect, the very popular Software-Defined Networking, which enables publish/subscribe middleware to perform line-rate filtering of events directly on hardware, can prove to be very useful. While deploying content filters directly on switches of a software-defined network allows optimized paths, high throughput rates, and low end-to-end latency, it suffers from certain inherent limitations with respect to number of bits available on hardware switches to represent these filters. Such a limitation affects expressiveness of filters, resulting in unnecessary traffic in the network. In this paper, we explore various complementary techniques to represent content filters expressively while being limited by hardware. We implement and evaluate techniques that i) use workload, in terms of events and subscriptions, to represent content, and ii) efficiently select attributes to reduce redundancy in content. Our detailed performance evaluations show the potential of these techniques in reducing unnecessary traffic when subjected to different workloads. Furthermore, the techniques proposed in this paper require significant updates to the network, i.e., the data plane, which must be performed in a consistent manner to ensure desired system behavior. As a result, in this paper, we, also, design and evaluate a light-weight approach that ensures data plane consistency in the presence of dynamic network updates. [ABSTRACT FROM AUTHOR]

Cooperative vehicles are better able to detect the environment and self-localize than a single vehicle. Cooperative vehicles can quickly cover the entire environment by communicating and cooperating with each other and can also reduce localization and mapping error by merging the cooperative vehicle information from observation and navigation. In this paper, we propose a novel algorithm for an effective solution of navigation and mapping for cooperative vehicles in an unknown environment. We present an improved centralized and collaborative monocular simultaneous localization and mapping (CCM-SLAM) approach. The proposed algorithm can accurately compute the transformation matrix for cooperative vehicle maps and reduce the communication delay, data loss among vehicles and decrease the bandwidth demand. The quaternion and credibility similarity transformation (QC-Sim(3)) method we proposed is used to accurately merge the matched maps and accomplish loop closures. The sending messages at variable frequencies (SMVF) method we proposed and an improved detection and resending lost data (I-DRLD) method we proposed can improve the accuracy of pose estimation. SMVF solves the time-delay problem by sending messages to the vehicles at flexible frequencies while I-DRLD detects and resends the lost data. We also adopt Intra-frame Feature Compression (IFC) to decrease the bandwidth demand in the process of the transmitting data. The experiments demonstrate the superiority of our proposed algorithm compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Encryption alone is not enough to protect data privacy, because access pattern leaks some sensitive information. Oblivious RAM (ORAM), the solution to this problem, is still far from practical deployment for heavy storage and communication/ computation overhead. To reduce them, an insightful idea was proposed to utilize non-colluding clouds to shift client computation and client-cloud communication to the clouds. The proposed multi-cloud ORAM achieved $O$ O (1) client-cloud bandwidth cost and removed most of client computation. In this paper, we exploit “disconnected ORAM operation” and design “two-layer encryption” to further reduce these overheads. Experiments show that our proposed scheme, NewMCOS, significantly reduces evict cache size from GB/MB to KB level with about 2-3 times lower response time and 20 percent savings in bandwidth for clouds, compared to other schemes. Theoretically speaking, we reduce evict cache size from $O(\sqrt{N})$ O (N) to $O(ZK)$ O (Z K) , where $N$ N is the number of real data blocks, $K$ K is the number of clouds ($2

Network Function Virtualization (NFV) and Software-Defined Networks (SDN) enable Internet Service Providers (ISPs) to place Virtual Network Functions (VNFs) to achieve the performance and security benefit without incurring high Operating Expenses (OPEX) and Capital Expenses (CAPEX). In NFV environment, Service Function Chains (SFCs) always need to steer the traffic through a series of VNF instances in predefined orders. Moreover, the required number and placement of VNF instances should be optimized to adapt to dynamic network load. Therefore, it is considerable for ISPs to conduct an optimal SFC embedding strategy to improve the network performance and revenue. In the paper, we study the SFC Embedding Problem (SFC-EP) with dynamic VNF placement in geo-distributed cloud system. We formulate this problem as a Binary Integer Programming (BIP) model aiming to embed SFC requests with the minimum embedding cost. Furthermore, the novel SFC eMbedding APproach (SFC-MAP) and VNF Dynamic Release Algorithm (VNF-DRA) have been proposed to efficiently embed SFC requests and optimize the number of placed VNF instances. Performance evaluation results show that the proposed algorithms can provide higher performance in terms of SFC request acceptance rate, network throughput, and mean VNF utilization rate and efficiently reduce the total VNF running time compared with the algorithms in existing literatures. [ABSTRACT FROM AUTHOR]

Recently, various applications including data analytics and machine learning have been developed for geo-distributed cloud data centers. For those applications, the ways of mapping parallel processes to physical nodes (i.e., "process mapping") could significantly impact the performance of the applications because of non-uniform communication cost in geo-distributed environments. What's more, the different data privacy requirements in geo-distributed data centers pose additional constraints on process mapping solutions. While process mapping has been widely studied in grid/cluster environments, few of the existing studies have considered the problem in geo-distributed cloud environment, which is a challenging task due to the multi-level data privacy constraints, heterogeneous network performance and process failures. In this paper, we introduce the special privacy requirements in geo-distributed data centers and formulate the geo-distributed process mapping problem as an optimization problem with multiple constraints. We develop a new method to efficiently find good process mapping solutions to the problem. Experimental results on real clouds (including Amazon EC2 and Windows Azure) and simulations demonstrate that our proposed approach can achieve significant performance improvement compared to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

On multi-core processors, applications are run sharing the cache. This paper presents optimization theory to co-locate applications to minimize cache interference and maximize performance. The theory precisely specifies MRC-based composition, optimization, and correctness conditions. The paper also presents a new technique called footprint symbiosis to obtain the best shared cache performance under fair CPU allocation as well as a new sampling technique which reduces the cost of locality analysis. When sampling and optimization are combined, the paper shows that it takes less than 0.1 second analysis per program to obtain a co-run that is within 1.5 percent of the best possible performance. In an exhaustive evaluation with 12,870 tests, the best prior work improves co-run performance by 56 percent on average. The new optimization improves it by another 29 percent. Without single co-run test, footprint symbiosis is able to choose co-run choices that are just 8 percent slower than the best co-run solutions found with exhaustive testing. [ABSTRACT FROM PUBLISHER]

Application partitioning that splits the executions into local and remote parts, plays a critical role in high-performance mobile offloading systems. Optimal partitioning will allow mobile devices to obtain the highest benefit from Mobile Cloud Computing (MCC) or Mobile Edge Computing (MEC). Due to unstable resources in the wireless network (network disconnection, bandwidth fluctuation, network latency, etc.) and at the service nodes (different speeds of mobile devices and cloud/edge servers, memory, etc.), static partitioning solutions with fixed bandwidth and speed assumptions are unsuitable for offloading systems. In this paper, we study how to dynamically partition a given application effectively into local and remote parts while reducing the total cost to the degree possible. For general tasks (represented in arbitrary topological consumption graphs), we propose a Min-Cost Offloading Partitioning (MCOP) algorithm that aims at finding the optimal partitioning plan (i.e., to determine which portions of the application must run on the mobile device and which portions on cloud/edge servers) under different cost models and mobile environments. Simulation results show that the MCOP algorithm provides a stable method with low time complexity which significantly reduces execution time and energy consumption by optimally distributing tasks between mobile devices and servers, besides it adapts well to mobile environmental changes. [ABSTRACT FROM AUTHOR]

With the growing importance of in-memory data processing, cloud service providers have launched large memory virtual machine services to accommodate memory intensive workloads. Such large memory services using low volume scaled-up machines are far less cost-efficient than scaled-out services consisting of high volume commodity servers. By exploiting memory usage imbalance across cloud nodes, disaggregated memory can scale up the memory capacity for a virtual machine in a cost-effective way. Disaggregated memory allows available memory in remote nodes to be used for the virtual machine requiring more memory than its locally available memory. It supports high performance with the faster direct memory while satisfying the memory capacity demand with the slower remote memory. This paper proposes a new hypervisor-integrated disaggregated memory system for cloud computing. The hypervisor-integrated design has several new contributions in its disaggregated memory design and implementation. First, with the tight hypervisor integration, it investigates a new page management mechanism and policy tuned for disaggregated memory in virtualized systems. Second, it restructures the memory management procedures and relieves the scalability concern for supporting large virtual machines. Third, exploiting page access records available to the hypervisor, it supports application-aware elastic block sizes for fetching remote memory pages with different granularities. Depending on the degrees of spatial locality for different regions of memory in a virtual machine, the optimal block size for each memory region is dynamically selected. The experimental results with the implementation integrated to the KVM hypervisor, show that the disaggregated memory can provide on average 6 percent performance degradation compared to the ideal local-memory only machine, even though the direct memory capacity is only 50 percent of the total memory footprint. [ABSTRACT FROM AUTHOR]

In recent years, there have been a number of large-scale deployments of P2P real-time streaming platforms in cloud storage systems due to their fast and convenient storage services for platform designers. Note that providing real-time streaming services for a platform is costly because of heavy bandwidth consumption from the platform to end-users. To cope with the above issue, our idea is to exploit the upload bandwidth of volunteers, namely, edge or fog nodes, to form an edge-node-assisted real-time streaming P2P platform. Specifically, according to designed strategies, the cloud distributes chunk replicas to edge nodes that can respond to end-user requests, and the public cloud supplements end-user demands only when the edge nodes cannot accommodate them. One of the important issues while introducing edge nodes into the system is how to utilize their resources efficiently. However, mainly due to the heterogeneous upload capacity of edge nodes and uneven request rates for different streaming chunks, the replica utilization at the edge nodes is diverse for different replica distribution strategies. Likewise bandwidth consumption in the public cloud varies correspondingly. Thus, this paper describes a mathematical model of the proposed replica distribution and designs an optimal algorithm for distributing chunk replicas. The results validate the effectiveness of the proposal. [ABSTRACT FROM AUTHOR]

Fair bandwidth allocation in datacenter networks has received a substantial amount of research attention, as multiple tenants are hosted by virtual machines in a public cloud. In the context of private datacenters, link bandwidth is shared among applications running data parallel frameworks, such as MapReduce, instead. In this paper, we introduce the rigorous definition of performance-centric fairness, with the guiding principle that the performance that data parallel applications will enjoy should be proportional to their weights. We first investigate the problem of maximizing application performance while maintaining strict performance-centric fairness. We then present an inherent tradeoff between fairness and efficiency, which is interpreted from the perspectives of bandwidth utilization and social welfare, respectively. From the first perspective, we propose an algorithm to improve bandwidth utilization by introducing an extended version of fairness. From the second perspective, we formulate an optimization problem of bandwidth allocation that maximizes the social welfare across all the applications, allowing a tunable degree of relaxation on performance-centric fairness. A distributed algorithm is then presented to solve the problem, based on dual based decomposition. With extensive simulations, we demonstrate the effectiveness of our algorithms in improving efficiency and application performance (by up to $1.4X$ ), with flexible degree of relaxation on the performance-centric fairness. [ABSTRACT FROM AUTHOR]

Network virtualization is regarded as the pillar of cloud computing, enabling the multi-tenancy concept where multiple Virtual Networks (VNs) can cohabit the same substrate network. With network virtualization, the problem of allocating resources to the various tenants, commonly known as the Virtual Network Embedding problem, emerges as a challenge. Its NP-Hard nature has drawn a lot of attention from the research community, many of which however overlooked the type of communication that a given VN may exhibit, assuming that they all exhibit a one-to-one (unicast) communication only. In this paper, we motivate the importance of characterizing the mode of communication in VN requests, and we focus our attention on the problem of embedding VNs with a one-to-many (multicast) communication mode. Throughout this paper, we highlight the unique properties of multicast VNs and its distinct Quality of Service (QoS) requirements, most notably the end-delay and delay-variation constraints for delay-sensitive multicast services. Further, we showcase the limitations of handling a multicast VN as unicast. To this extent, we formally define the VNE problem for Multicast VNs (MVNs) and prove its NP-Hard nature. We propose two novel approach to solve the Multicast VNE (MVNE) problem with end-delay and delay variation constraints: A 3-Step MVNE technique, and a Tabu-Search algorithm. We motivate the intuition behind our proposed embedding techniques, and provide a competitive analysis of our suggested approaches over multiple metrics and against other embedding heuristics. [ABSTRACT FROM PUBLISHER]

Lambda paradigm, also known as Function as a Service (FaaS), is a novel event-driven concept that allows companies to build scalable and reliable enterprise applications in an off-premise computing data-center as a serverless solution. In practice, however, an important goal for the service provider of a Lambda platform is to devise an efficient way to consolidate multiple Lambda functions in a single host. While the majority of existing resource management solutions use only operating-system level metrics (e.g., average utilization of computing and I/O resources) to allocate the available resources among the submitted workloads in a balanced way, a resource allocation schema that is oblivious to the issue of shared-resource contention can result in a significant performance variability and degradation within the entire platform. This paper proposes a predictive controller scheme that dynamically allocates resources in a Lambda platform. This scheme uses a prediction tool to estimate the future rate of every event stream and takes into account the quality of service enforcements requested by the owner of each Lambda function. This is formulated as an optimization problem where a set of cost functions are introduced (i) to reduce the total QoS violation incidents; (ii) to keep the CPU utilization level within an accepted range; and (iii) to avoid the fierce contention among collocated applications for obtaining shared resources. Performance evaluation is carried out by comparing the proposed solution with an enhanced interference-aware version of three well-known heuristics, namely spread, binpack (the two native clustering solutions employed by Docker Swarm) and best-effort resource allocation schema. Experimental results show that the proposed controller improves the overall performance (in terms of reducing the end-to-end response time) by 14.9 percent on average compared to the best result of the other heuristics. The proposed solution also increases the overall CPU utilization by 18 percent on average (for lightweight workloads), while achieves an average 87 percent (maximum 146 percent) improvement in preventing QoS violation incidents. [ABSTRACT FROM AUTHOR]

Network function virtualization (NFV) brings great conveniences and benefits for the enterprises to outsource their network functions to the cloud datacenter. In this paper, we address the virtual network function (VNF) placement problem in cloud datacenter considering users’ service function chain requests (SFCRs). To optimize the resource utilization, we take two less-considered factors into consideration, which are the time-varying workloads, and the basic resource consumptions (BRCs) when instantiating VNFs in physical machines (PMs). Then the VNF placement problem is formulated as an integer linear programming (ILP) model with the aim of minimizing the number of used PMs. Afterwards, a Two-StAge heurisTic solution (T-SAT) is designed to solve the ILP. T-SAT consists of a correlation-based greedy algorithm for SFCR mapping (first stage) and a further adjustment algorithm for virtual network function requests (VNFRs) in each SFCR (second stage). Finally, we evaluate T-SAT with the artificial data we compose with Gaussian function and trace data derived from Google's datacenters. The simulation results demonstrate that the number of used PMs derived by T-SAT is near to the optimal results and much smaller than the benchmarks. Besides, it improves the network resource utilization significantly. [ABSTRACT FROM AUTHOR]

Cooperation between the fog and the cloud in mobile cloud computing environments could offer improved offloading services to smart mobile user equipment (UE) with computation intensive tasks. In this paper, we tackle the computation offloading problem in a mixed fog/cloud system by jointly optimizing the offloading decisions and the allocation of computation resource, transmit power, and radio bandwidth while guaranteeing user fairness and maximum tolerable delay. This optimization problem is formulated to minimize the maximal weighted cost of delay and energy consumption (EC) among all UEs, which is a mixed-integer non-linear programming problem. Due to the NP-hardness of the problem, we propose a low-complexity suboptimal algorithm to solve it, where the offloading decisions are obtained via semidefinite relaxation and randomization, and the resource allocation is obtained using fractional programming theory and Lagrangian dual decomposition. Simulation results are presented to verify the convergence performance of our proposed algorithms and their achieved fairness among UEs, and the performance gains in terms of delay, EC, and the number of beneficial UEs over existing algorithms. [ABSTRACT FROM AUTHOR]

With the growing popularity of cloud storage, more and more diverse applications with diverse service level agreements (SLAs) are being accommodated into it. The quality of service (QoS) support for applications in a shared cloud storage becomes important. However, performance isolation, diverse performance requirements, especially harsh latency guarantees and high system utilization, are all challenging and desirable for QoS design. In this paper, we propose a service curve-based QoS algorithm to support latency guarantee applications, IOPS guarantee applications and best-effort applications at the same storage system, which not only provides a QoS guarantee for applications, but also pursues better system utilization. Three priority queues are exploited and different service curves are applied for different types of applications. I/O requests from different applications are scheduled and dispatched among the three queues according to their service curves and I/O urgency status, so that QoS requirements of all applications can be guaranteed on the shared storage system. Our experimental results show that our algorithm not only simultaneously guarantees the QoS targets of latency and throughput (IOPS), but also improves the utilization of storage resources. [ABSTRACT FROM PUBLISHER]

Typically called big data processing, analyzing large volumes of data from geographically distributed regions with machine learning algorithms has emerged as an important analytical tool for governments and multinational corporations. The traditional wisdom calls for the collection of all the data across the world to a central data center location, to be processed using data-parallel applications. This is neither efficient nor practical as the volume of data grows exponentially. Rather than transferring data, we believe that computation tasks should be scheduled near the data, while data should be processed with a minimum amount of transfers across data centers. In this paper, we design and implement Flutter, a new task scheduling algorithm that reduces both the completion times and the network costs of big data processing jobs across geographically distributed data centers. To cater to the specific characteristics of data-parallel applications, in the case of optimizing the job completion times only, we first formulate our problem as a lexicographical min-max integer linear programming (ILP) problem, and then transform the ILP problem into a nonlinear program problem with a separable convex objective function and a totally unimodular constraint matrix, which can be further solved using a standard linear programming solver efficiently in an online fashion. In the case of improving both time- and cost- efficiency, we formulate the general problem as an ILP problem and we find out that solving an LP problem can achieve the same goal in the real practice. Our implementation of Flutter is based on Apache Spark, a modern framework popular for big data processing. Our experimental results have shown convincing evidence that Flutter can shorten both job completion times and network costs by a substantial margin. [ABSTRACT FROM AUTHOR]

Recent research trends exhibit a growing imbalance between the demands of tenants’ software applications and the provisioning of hardware resources. Misalignment of demand and supply gradually hinders workloads from being efficiently mapped to fixed-sized server nodes in traditional data centers. The incurred resource holes not only lower infrastructure utilization but also cripple the capability of a data center for hosting large-sized workloads. This deficiency motivates the development of a new rack-wide architecture referred to as the composable system . The composable system transforms traditional server racks of static capacity into a dynamic compute platform. Specifically, this novel architecture aims to link up all compute components that are traditionally distributed on traditional server boards, such as central processing unit (CPU), random access memory (RAM), storage devices, and other application-specific processors. By doing so, a logically giant compute platform is created and this platform is more resistant against the variety of workload demands by breaking the resource boundaries among traditional server boards. In this paper, we introduce the concepts of this reconfigurable architecture and design a framework of the composable system for cloud data centers. We then develop mathematical models to describe the resource usage patterns on this platform and enumerate some types of workloads that commonly appear in data centers. From the simulations, we show that the composable system sustains nearly up to 1.6 times stronger workload intensity than that of traditional systems and it is insensitive to the distribution of workload demands. This demonstrates that this composable system is indeed an effective solution to support cloud data center services. [ABSTRACT FROM AUTHOR]

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]

Ethernet is the main choice of connectivity in data centers operating in clouds. The Ethernet standards body (IEEE 802.3) has generated Ethernet specifications with increasing transmission rates since its inception. As a result, it is observed that data centers with groups of physical hosts having heterogeneous bandwidths. In this paper, we present an energy-aware virtual machine’s scheduling method, taking into account this heterogeneity while aiming at energy efficiency. This method consists of two algorithms: one to determine to which physical hosts virtual machines should be allocated, and the other for the provision of bandwidth on physical hosts to virtual machines. Our study shows that, regarding energy savings for the studied scenarios, the presented method is comparable to other energy-aware methods in data centers with groups of machines with homogeneous settings, surpassing them in groups of machines with heterogeneous configurations, bringing improvements especially to data centers with heterogeneous bandwidths’ networks or to where restrictive SLAs are used for the provision of bandwidth to virtual machines. [ABSTRACT FROM PUBLISHER]

Centralized cloud infrastructures have become the popular platforms for data-intensive computing today. However, they suffer from inefficient data mobility due to the centralization of cloud resources, and hence, are highly unsuited for geo-distributed data-intensive applications where the data may be spread at multiple geographical locations. In this paper, we present Nebula: a dispersed edge cloud infrastructure that explores the use of voluntary resources for both computation and data storage. We describe the lightweight Nebula architecture that enables distributed data-intensive computing through a number of optimization techniques including location-aware data and computation placement, replication, and recovery. We evaluate Nebula performance on an emulated volunteer platform that spans over 50 PlanetLab nodes distributed across Europe, and show how a common data-intensive computing framework, MapReduce, can be easily deployed and run on Nebula. We show Nebula MapReduce is robust to a wide array of failures and substantially outperforms other wide-area versions based on emulated existing systems. [ABSTRACT FROM PUBLISHER]

With the globalization of service, organizations continuously produce large volumes of data that need to be analysed over geo-dispersed locations. Traditionally central approach that moving all data to a single cluster is inefficient or infeasible due to the limitations such as the scarcity of wide-area bandwidth and the low latency requirement of data processing. Processing big data across geo-distributed datacenters continues to gain popularity in recent years. However, managing distributed MapReduce computations across geo-distributed datacenters poses a number of technical challenges: how to allocate data among a selection of geo-distributed datacenters to reduce the communication cost, how to determine the Virtual Machine (VM) provisioning strategy that offers high performance and low cost, and what criteria should be used to select a datacenter as the final reducer for big data analytics jobs. In this paper, these challenges is addressed by balancing bandwidth cost, storage cost, computing cost, migration cost, and latency cost, between the two MapReduce phases across datacenters. We formulate this complex cost optimization problem for data movement, resource provisioning and reducer selection into a joint stochastic integer nonlinear optimization problem by minimizing the five cost factors simultaneously. The Lyapunov framework is integrated into our study and an efficient online algorithm that is able to minimize the long-term time-averaged operation cost is further designed. Theoretical analysis shows that our online algorithm can provide a near optimum solution with a provable gap and can guarantee that the data processing can be completed within pre-defined bounded delays. Experiments on WorldCup98 web site trace validate the theoretical analysis results and demonstrate that our approach is close to the offline-optimum performance and superior to some representative approaches. [ABSTRACT FROM PUBLISHER]

Regenerating codes for distributed storage have attracted much research interest in the past decade. Such codes trade the bandwidth needed to repair a failed node with the overall amount of data stored in the network. Minimum storage regenerating (MSR) codes are an important class of optimal regenerating codes that minimize (first) the amount of data stored per node and (then) the repair bandwidth. Specifically, an [n,k,d] - (\alpha ) MSR code \mathbb C over \smash \mathbb F\!q stores a file \mathcal F consisting of \alpha k symbols over \smash {\mathbb {F}_{\!q}} among n$ nodes, each storing \alpha $ symbols, in such a way that: 1) the file k$ of the n$ nodes and 2) the content of any failed node can be reconstructed by accessing any d$ of the remaining symbols from each of these nodes. In practice, the file \mathcal F is typically available in uncoded form on some $k$ of the $n$ nodes, known as systematic nodes, and the defining node-repair condition above can be relaxed to requiring the optimal repair bandwidth for systematic nodes only. Such codes are called systematic–repair MSR codes. Unfortunately, finite– $\alpha $ constructions of $[n,k,d]$ MSR codes are known only for certain special cases: either low rate, namely $k/n \leqslant 0.5$ , or high repair connectivity, namely $d = n-1$ . Our main result in this paper is a finite– $\alpha $ construction of systematic-repair $[n,k,d]$ MSR codes for all possible values of parameters $n,k,d$ . We also introduce a generalized construction for $[n,k]$ MSR codes to achieve the optimal repair bandwidth for all values of $d$ simultaneously. [ABSTRACT FROM PUBLISHER]

It is common for cloud users to require clusters of inter-connected virtual machines (VMs) in a geo-distributed IaaS cloud, to run their services. Compared to isolated VMs, key challenges on dynamic virtual cluster (VC) provisioning (computation + communication resources) lie in two folds: (1) optimal placement of VCs and inter-VM traffic routing involve NP-hard problems, which are non-trivial to solve offline, not to mention if an online efficient algorithm is sought; (2) an efficient pricing mechanism is missing, which charges a market-driven price for each VC as a whole upon request, while maximizing system efficiency or provider revenue over the entire span. This paper proposes efficient online auction mechanisms to address the above challenges. We first design SWMOA, a novel online algorithm for dynamic VC provisioning and pricing, achieving truthfulness, individual rationality, computation efficiency, and $(1+2\log \mu)$ -competitiveness in social welfare, where $\mu$ is related to the problem size. Next, applying a randomized reduction technique, we convert the social welfare maximizing auction into a revenue maximizing online auction, PRMOA, achieving $O(\log \mu)$ -competitiveness in provider revenue, as well as truthfulness, individual rationality and computation efficiency. We investigate auction design in different cases of resource cost functions in the system. We validate the efficacy of the mechanisms through solid theoretical analysis and trace-driven simulations. [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]

Modern day data centers coordinate hundreds of thousands of heterogeneous tasks and aim at delivering highly reliable cloud computing services. Although offering equal reliability to all users benefits everyone at the same time, users may find such an approach either inadequate or too expensive to fit their individual requirements, which may vary dramatically. In this paper, we propose a novel method for providing elastic reliability optimization in cloud computing. Our scheme makes use of peer-to-peer checkpointing and allows user reliability levels to be jointly optimized based on an assessment of their individual requirements and total available resources in the data center. We show that the joint optimization can be efficiently solved by a distributed algorithm using dual decomposition. The solution improves resource utilization and presents an additional source of revenue to data center operators. Our validation results suggest a significant improvement of reliability over existing schemes. [ABSTRACT FROM PUBLISHER]

With the rapid development of cloud technology, many services have been transferred from local computers to the cloud-based platform, which decreases the amount of computation done on the former. The local computer could thus be developed in the direction of portability and power saving. Graphics processing, apart from providing user interfaces featuring diversified special effects, is also significant in terms of application programs and play interactions. It is exactly on the basis of the concept of graphics processing that cloud-support rendering is developed, which is aimed to improve the graphics efficiency in mobile devices, via the graphics processing units in the cloud-based platform. The cloud-based platform and the mobile devices are usually connected by the Internet; however, as remote rendering might call for greater network bandwidth, its efficiency will be compromised if the network bandwidth is not stable. Given this limitation, this paper sets out to propose a quality-of-service-aware resource allocation strategy for mobile 3D graphics rendering, which is a hybrid rendering technology combining the client-side graphics processing capabilities with the graphics processing units in the cloud-based platform. When network bandwidth is not stable, the technology is able to assess the current network bandwidth, and dynamically configure the rendered frames on the client side and cloud-based platforms. Even when the client side could not access the network, it would still be possible to carry out the drawing through the graphics processing units on the local computer. Three applications are tested in this research: the technology can increase the frame rate by an average of 44.99% when the bandwidth is 10% greater than the minimum limit, by an average of 44.57% when the bandwidth is less than the minimum limit, by an average of 30.86% when the bandwidth is 10% less than the minimum limit, and by an average of 33.74% when the bandwidth is not stable. [ABSTRACT FROM PUBLISHER]

Cloud computing opens a new door for designing the next-generation video distribution platform. As video services move to the cloud, some related data-intensive applications, such as recommender system, automatic scoring mechanism, and prediction algorithm, should also be cloud-based. Since traditional cloud file systems like MapReduce/Hadoop exhibit cost disadvantages in data accessing, a Cache A Replica On Modification (CAROM) cloud file system is designed to achieve high data availability and low storage cost, which provides resiliency in cloud file systems with high efficiency. In this paper, we focus on moving data-intensive applications to the CAROM cloud file system, for optimizing access latencies while maintaining the benefit of low storage cost. To achieve this, we propose a novel scheduling mechanism as a lubricant between CAROM and data-intensive applications. Our scheme consists of three parts. First, tripartite graph is employed to describe the relationships among tasks, computation nodes, and data nodes. Second, we give a 1:1:1 framework based on the situation that the data of task have been stored in the cache, and introduce two variant frameworks 1:1:M and 1:N:M with the consideration of limitations of cache size and performance of task. Finally, a k -list algorithm is proposed as an approximation algorithm, and its mathematical definitions and proofs are given in detail. We conduct simulations to evaluate our scheme and the results prove that the performance of our algorithm is significantly better than that of the general two-layer algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, we present Prius, a hybrid edge cloud and client adaptation framework for HTTP adaptive streaming (HAS) by taking advantage of the new capabilities empowered by recent advances in edge cloud computing. In particular, emerging edge clouds are capable of accessing an application layer and radio access networks (RANs) information in real time. Coupled with powerful computation support, an edge cloud-assisted strategy is expected to significantly enrich mobile services. Meanwhile, although HAS has established itself as the dominant technology for video streaming, one key challenge for adapting HAS to mobile cellular networks is in overcoming the inaccurate bandwidth estimation and unfair bitrate adaptation under the highly dynamic cellular links. Edge cloud-assisted HAS presents a new opportunity to resolve these issues and achieve systematic enhancement of quality of experience (QoE) and QoE fairness in cellular networks. To explore this new opportunity, Prius overlays a layer of adaptation intelligence at the edge cloud to finalize the adaptation decisions while considering the initial bandwidth-irrelevant bitrate selection at the clients. Prius is able to exploit RAN channel status, client device characteristics, and application-layer information in order to jointly adapt the bitrate of multiple clients. Prius also adopts a QoE continuum model to track the cumulative viewing experience and an exponential smoothing estimation to accurately estimate a future channel under different moving patterns. Extensive trace-driven simulation results show that Prius with hybrid edge cloud and client adaptation is promising under both slow and fast-moving environments. Furthermore, the Prius adaptation algorithm achieves a near-optimal performance that outperforms the exiting strategies. [ABSTRACT FROM PUBLISHER]

Increasingly, many bandwidth-intensive applications have been ported to the cloud platform. In practice, however, some disadvantages including equipment failures, bandwidth overload and long-distance transmission often damage the QoS about data availability, bandwidth provision and access locality respectively. While some recent solutions have been proposed to cope with one or two of disadvantages, but not all. Moreover, as the number of data objects scales, most of the current offline algorithms solving a constraint optimization problem suffer from low computational efficiency. To overcome these problems, in this paper we propose an approach that aims to make fully efficient use of the cloud resources to enable bandwidth-intensive applications to achieve the desirable level of SLA-specified QoS mentioned above cost-effectively and timely. First we devise a constraint-based model that describes the relationship among data object placement, user cells bandwidth allocation, operating costs and QoS constraints. Second, we use the distributed heuristic algorithm, called DREAM-L, that solves the model and produces a budget solution to meet SLA-specified QoS. Third, we propose an object-grouping technique that is integrated into DREAM-L , called DREAM-LG, to significantly improve the computational efficiency of our algorithm. The results of hundreds of thousands of simulation-based experiments demonstrate that DREAM-LG provides much better data availability, bandwidth provision and access locality than the state-of-the-art solutions at modest cloud operating costs and within a small and acceptable range of time. [ABSTRACT FROM PUBLISHER]

The upsurge of cloud video recording (CVR) has gained increasing attention from the general public and entrepreneurs. With live video records archived in the cloud, the CVR paradigm enables various smart services by keeping track of activities in the monitored region from anywhere at any time. However, the limited upstream bandwidth affects the quality of surveillance when multiple distributed cameras share the same upstream link. To solve the problem, this paper proposes an efficient upstream bandwidth multiplexing algorithm to intelligently allocate upstream bandwidth for each live video stream while maximizing the overall utility from the perspective of a CVR user. Specifically, we formulate the upstream bandwidth multiplexing problem as a constrained stochastic optimization problem, and apply the technique of hierarchical approximation to solve it efficiently. Our algorithm can be extended to take the priority of video streams into account and allocate more upstream bandwidth to video streams with higher priorities. We explicitly prove the approximation ratio of the proposed algorithm. In addition, we also conduct extensive trace-driven simulations to verify the effectiveness of our algorithm. The simulation results show that our algorithm improves the overall CVR user utility by over 20% compared with other alternatives, and the average utility per bandwidth unit is guaranteed to be stable even when the number of video streams increases. [ABSTRACT FROM AUTHOR]

Virtualization of servers and networks is a key technique to resolve the conflict between the increasing demands on computing power and the high cost of hardware in data centers. In order to map virtual networks to physical infrastructure efficiently, designers have to make careful decisions on the allocation of limited resources, which makes placement of virtual networks in data centers a critical issue. In this paper, we study the placement of virtual networks in fat-tree data center networks. In order to meet the requirements of instant parallel data transfer between multiple computing units, we propose a model of multicast-capable virtual networks (MVNs). We then design four virtual machine (VM) placement schemes to embed MVNs into fat-tree data center networks, named Most-Vacant-Fit (MVF), Most-Compact-First (MCF), Mixed-Bidirectional-Fill (MBF), and Malleable-Shallow-Fill (MSF). All these VM placement schemes guarantee the nonblocking multicast capability of each MVN while simultaneously achieving significant saving in the cost of network hardware. In addition, each VM placement scheme has its unique features. The MVF scheme has zero interference to existing computing tasks in data centers; the MCF scheme leads to the greatest cost saving; the MBF scheme simultaneously possesses the merits of MVF and MCF, and it provides an adjustable parameter allowing cloud providers to achieve preferred balance between the cost and the overhead; the MSF scheme performs at least as well as MBF, and possesses some additional predictable features. Finally, we compare the performance and overhead of these VM placement schemes, and present simulation results to validate the theoretical results. [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]

Infrastructure-as-a-service (IaaS) cloud providers offer tenants elastic computing resources in the form of virtual machine (VM) instances to run their jobs. Recently, providing predictable performance (i.e., performance guarantee) for tenant applications is becoming increasingly compelling in IaaS clouds. However, the hardware heterogeneity and performance interference across the same type of cloud VM instances can bring substantial performance variation to tenant applications, which inevitably stops the tenants from moving their performance-sensitive applications to the IaaS cloud. To tackle this issue, this paper proposes Heifer, a Heterogeneity and interference-aware VM provisioning framework for tenant applications, by focusing on MapReduce as a representative cloud application. It predicts the performance of MapReduce applications by designing a lightweight performance model using the online-measured resource utilization and capturing VM interference. Based on such a performance model, Heifer provisions the VM instances of the good-performing hardware type (i.e., the hardware that achieves the best application performance) to achieve predictable performance for tenant applications, by explicitly exploring the hardware heterogeneity and capturing VM interference. With extensive prototype experiments in our local private cloud and a real-world public cloud (i.e., Microsoft Azure) as well as complementary large-scale simulations, we demonstrate that Heifer can guarantee the job performance while saving the job budget for tenants. Moreover, our evaluation results show that Heifer can improve the job throughput of cloud datacenters, such that the revenue of cloud providers can be increased, thereby achieving a win-win situation between providers and tenants. [ABSTRACT FROM PUBLISHER]

Service level agreements (SLAs) for storage performance in virtualized systems are difficult to guarantee, because different consolidated virtual machines have their own performance requirements. Moreover, hard disk drives (HDDs) in virtualized systems are being replaced by solid-state drives (SSDs). SSDs have higher throughput and lower latency than HDDs; however, they pose new challenges in terms of SLAs. In this paper, we determine that existing I/O schedulers working with SSDs fail to guarantee SLAs among virtualmachines, and do not effectively utilize the high performance of SSDs. To address this issue, we propose the opportunistic I/O scheduler (OIOS), a novel I/O scheduler for SSDs. OIOS guarantees SLAs and fully utilizes the high performance of SSDs. To support realistic SLAs, OIOS provides diverse SLA support functions, including reservations, limitations, and proportional sharing. In addition, OIOS accepts SLAs that are specified in four measurement types: bandwidth, I/Os per second (IOPS), latency, and utilization. Experimental results show that OIOS increases the aggregated bandwidth of VMs by 80 percent compared to mClock, while achieving a similar level of fairness. In addition, we evaluate the proposed scheduler with realistic benchmarks, such as Filebench and the Yahoo CloudServing Benchmark. OIOS successfully guarantees the requirements of diverse SLAs with different metrics. [ABSTRACT FROM AUTHOR]

With the tremendous growth of cloud computing, it is increasingly critical to provide quantifiable performance to tenants and to improve resource utilization for the cloud provider. Though many recent proposals focus on guaranteeing job performance (with a particular note on network bandwidth) in the cloud, they usually lack efficient utilization  of cloud resource, or vice versa. In this paper we present DCloud, which leverages the (soft) deadlines of cloud computing jobs to enable flexible and efficient resource utilization in data centers. With the deadline requirement of a job guaranteed, DCloud employs both time sliding (postponing the launching time of a job) and bandwidth scaling (adjusting the bandwidth associated with VMs) in resource allocation, so as to better match the resource allocated to the job with the cloud's residual resource. Extensive simulations and testbed experiments show that DCloud can accept much more jobs than existing solutions, and significantly increase the cloud provider's revenue with less cost for individual tenants. [ABSTRACT FROM AUTHOR]

Network coding is an important cloud storage technique, which can recover data with small repair bandwidth and high reliability compared to the existing erasure coding and replication methods. However, regardless of which data recovery technique is used, the repaired data in a geographically distributed cloud storage system are easy to be eavesdropped at the transmission link between the local datacenter and its remote backup site. This kind of network security issue is called link eavesdropping in this paper. For a network coded cloud storage system, we propose a systematic design methodology to determine the important data recovery system parameters for any specified security level. Through analysis, we present the performance curves to relate the remote repair bandwidth and the number of coded data fragments. Consequently, all the important system parameters of a network coded data recovery system, including the number of storage nodes and the link capacity between the datacenter and the backup site, can be precisely designed for satisfying different security level requirements. [ABSTRACT FROM AUTHOR]

With the increasing utilization and popularity of the cloud infrastructure, more and more data are moved to the cloud storage systems. This makes the availability of cloud storage services critically important, particularly given the fact that outages of cloud storage services have indeed happened from time to time. Thus, solely depending on a single cloud storage provider for storage services can risk violating the service-level agreement (SLA) due to the weakening of service availability. This has led to the notion of Cloud-of-Clouds, where data redundancy is introduced to distribute data among multiple independent cloud storage providers, to address the problem. The key in the effectiveness of the Cloud-of-Clouds approaches lies in how the data redundancy is incorporated and distributed among the clouds. However, the existing Cloud-of-Clouds approaches utilize either replication or erasure codes to redundantly distribute data across multiple clouds, thus incurring either high space or high performance overheads. In this paper, we propose a hybrid redundant data distribution approach, called HyRD, to improve the cloud storage availability in Cloud-of-Clouds by exploiting the workload characteristics and the diversity of cloud providers. In HyRD, large files are distributed in multiple cost-efficient cloud storage providers with erasure-coded data redundancy while small files and file system metadata are replicated on multiple high-performance cloud storage providers. The experiments conducted on our lightweight prototype implementation of HyRD show that HyRD improves the cost efficiency by 33.4 and 20.4 percent, and reduces the access latency by 58.7 and 34.8 percent than the DuraCloud and RACS schemes, respectively. [ABSTRACT FROM AUTHOR]

Content delivery networks (CDNs) have been widely implemented to provide scalable cloud services. Such networks support resource pooling by allowing virtual machines or physical servers to be dynamically activated and deactivated according to current user demand. This paper examines online video replication and placement problems in CDNs. An effective video provisioning scheme must simultaneously (i) utilize system resources to reduce total energy consumption and (ii) limit replication overhead. We propose a scheme called adaptive data placement (ADP) that can dynamically place and reorganize video replicas among cache servers on subscribers’ arrival and departure. Both the analyses and simulation results show that ADP can reduce the number of activated cache servers with limited replication overhead. In addition, ADP's performance is approximate to the optimal solution. [ABSTRACT FROM PUBLISHER]

Big-data computing is a new critical challenge for the ICT industry. Engineers and researchers are dealing with data sets of petabyte scale in the cloud computing paradigm. Thus, the demand for building a service stack to distribute, manage, and process massive data sets has risen drastically. In this paper, we investigate the Big Data Broadcasting problem for a single source node to broadcast a big chunk of data to a set of nodes with the objective of minimizing the maximum completion time. These nodes may locate in the same datacenter or across geo-distributed datacenters. This problem is one of the fundamental problems in distributed computing and is known to be NP-hard in heterogeneous environments. We model the Big-data broadcasting problem into a LockStep Broadcast Tree (LSBT) problem. The main idea of the LSBT model is to define a basic unit of upload bandwidth, r, such that a node with capacity c broadcasts data to a set of [c/r] children at the rater. Note that r is a parameter to be optimized as part of the LSBT problem. We further divide the broadcast data into m chunks. These data chunks can then be broadcast down the LSBT in a pipeline manner. In a homogeneous network environment in which each node has the same upload capacity c, we show that the optimal uplink rate r* of LSBT is either c/2 or c/3, whichever gives the smaller maximum completion time. For heterogeneous environments, we present an O(nlog2n) algorithm to select an optimal uplink rater* and to construct an optimal LSBT. Numerical results show that our approach performs well with less maximum completion time and lower computational complexity than other efficient solutions in literature. [ABSTRACT FROM PUBLISHER]

Cloud-enabled vehicular networks are a new paradigm to improve the quality of vehicular services, which have drawn considerable attention in industry and academia. In this paper, we consider the resource management and sharing problem for bandwidth and computing resources to support mobile applications in cloud-enabled vehicular networks. In such an environment, cloud service providers (SPs) can cooperate to form coalitions to share their idle resources with each other. We propose a coalition game model based on two-sided matching theory for cooperation among cloud SPs to share their idle resources. As a result, the resources can be better utilized, and the QoS for users can be improved. Numerical results indicate that our scheme can improve resource utilization and increase by 75% the QoS of the applications compared with that without cooperation. Moreover, the higher service cost of cooperation brings negative effect on coalition formation. The higher cooperation willingness of cloud SPs and the lower service cost support more service applications. [ABSTRACT FROM PUBLISHER]

Nowadays, large-scale video distribution feeds a significant fraction of the global Internet traffic. However, existing content delivery networks may not be cost efficient enough to distribute adaptive video streaming, mainly due to the lack of orchestration on storage, computing, and bandwidth resources. In this paper, we leverage Media Cloud to deliver on-demand adaptive video streaming services, where those resources can be dynamically scheduled in an on-demand fashion. Our objective is to minimize the total operational cost by optimally orchestrating multiple resources. Specifically, we formulate an optimization problem, by examining a three-way tradeoff between the caching, transcoding, and bandwidth costs, at each edge server. Then, we adopt a two-step approach to analytically derive the closed-form solution of the optimal transcoding configuration and caching space allocation, respectively, for every edge server. Finally, we verify our solution throughout extensive simulations. The results indicate that our approach achieves significant cost savings compared with the existing methods used in content delivery networks. In addition, we also find the optimal strategy and its benefits can be affected by a list of system parameters, including the unit cost of different resources, the hop distance to the origin server, the Zipf parameter of users’ request patterns, and the settings of different bitrate versions for one segment. [ABSTRACT FROM AUTHOR]

Cloud gaming has become a new trend for gamers to access high-end video games. By rendering games in the remote cloud and streaming video scenes to the users, games can be played anywhere, anytime, on any device (e.g., smartphones, tablets, or personal computers). In this paper, we address the problem of achieving cost-effective adaptive cloud gaming in geo-distributed data centers from the perspective of cloud gaming service providers (CGSPs). Unlike previous work, we consider a cloud gaming system supported with the adaptive streaming technology. Our purpose is to minimize the overall service cost for CGSPs, by adaptively adjusting the selection of data centers, virtual machine allocation and video bitrate configuration for each user. Meanwhile, we also need to ensure good-enough quality of experience (QoE) for gamers. To this objective, we formulate the problem into a constrained stochastic optimization problem, and apply the Lyapunov optimization theory to drive the corresponding online strategy with provable upper bounds. Due to the diverse QoE requirements of video games, we also take the difference among game genres into account during the algorithm design. Finally, we conduct extensive trace-driven simulations to evaluate the effectiveness of our algorithm and our results show that our proposed algorithm can achieve significant gain over other alternative approaches. [ABSTRACT FROM AUTHOR]

Bandwidth reservation has been recognized as a value-added service to the cloud provider in recent years. We consider an open market of cloud bandwidth reservation, in which cloud providers offer bandwidth reservation services to cloud tenants, especially online streaming service providers, who have strict requirements on the amount of bandwidth to guarantee their quality of services. In this paper, we model the open market as a double-sided auction, and propose the first family of STrategy-proof double Auctions for multi-cloud, multi-tenant bandwidth Reservation (STAR). STAR contains two auction mechanisms. The first one, STAR-Grouping, divides the tenants into groups by a bid-independent way, and carefully matches the cloud providers with the tenant groups to form good trades. The second one, STAR-Padding, greedily matches the cloud providers with the tenants, and fills the partially reserved cloud provider(s) with a novel virtual padding tenant who can be a component of the auctioneer. Our analysis shows that both of the two auction mechanisms achieve strategy-proofness and ex-post budget balance. Our evaluation results show that they achieve good performance in terms of social welfare, cloud bandwidth utilization, and tenant satisfaction ratio. [ABSTRACT FROM AUTHOR]

With the increasing availability of graph data and widely adopted cloud computing paradigm, graph partitioning has become an efficient pre-processing technique to balance the computing workload and cope with the large scale of input data. Since the cost of partitioning the entire graph is strictly prohibitive, there are some recent tentative works towards streaming graph partitioning which run faster, are easily parallelized, and can be incrementally updated. Most of the existing works on streaming partitioning assume that worker nodes within a cluster are homogeneous in nature. Unfortunately, this assumption does not always hold. Experiments show that these homogeneous algorithms suffer a significant performance degradation when running at heterogeneous environment. In this paper, we propose a novel adaptive streaming graph partitioning approach to cope with heterogeneous environment. We first formally model the heterogeneous computing environment with the consideration of the unbalance of computing ability (e.g., the CPU frequency) and communication ability (e.g., the network bandwidth) for each node. Based on this model, we propose a new graph partitioning objective function that aims to minimize the total execution time of the graph-processing job. We then explore some simple yet effective streaming algorithms for this objective function that can achieve balanced and efficient partitioning result. Extensive experiments are conducted on a moderate sized computing cluster with real-world web and social network graphs. The results demonstrate that the proposed approach achieves significant improvement compared with the state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

Enabled by virtualization technologies, various multi-tier applications (such as web applications) are hosted by virtual machines (VMs) in cloud data centers. Live migration of multi-tier applications across geographically distributed data centers is important for load management, power saving, routine server maintenance and quality-of-service. Different from a single-VM migration, VMs in a multi-tier application are closely correlated, which results in a correlated VM migrations problem. Current live migration algorithms for single-VM cause significant application performance degradation because intermediate data exchange between different VMs suffers relatively low bandwidth and high latency across distributed data centers. In this paper, we design and implement a coordination system called VMbuddies for correlated VM migrations in the cloud. Particularly, we propose an adaptive network bandwidth allocation algorithm to minimize the migration cost in terms of migration completion time, network traffic and migration downtime. Experiments using a public benchmark show that VMbuddies significantly reduces the performance degradation and migration cost of multi-tier applications. [ABSTRACT FROM AUTHOR]

Large-scale datacenters have been widely used to host cloud services, which are typically allocated to different virtual machines (VMs) through resource multiplexing across shared physical servers. Although recent studies have primarily focused on harnessing live migration of VMs to achieve load balancing and power saving among different servers, there has been little attention on the incurred performance interference and cost on both source and destination servers during and after such VM migration. To avoid potential violations of service-level-agreement (SLA) demanded by cloud applications, this paper proposes iAware, a lightweight interference-aware VM live migration strategy. It empirically captures the essential relationships between VM performance interference and key factors that are practically accessible through realistic experiments of benchmark workloads on a Xen virtualized cluster platform. iAware jointly estimates and minimizes both migration and co-location interference among VMs, by designing a simple multi-resource demand-supply model. Extensive experiments and complementary large-scale simulations are conducted to validate the performance gain and runtime overhead of iAware in terms of I/O and network throughput, CPU consumption, and scalability, compared to the traditional interference-unaware VM migration approaches. Moreover, we demonstrate that iAware is flexible enough to cooperate with existing VM scheduling or consolidation policies in a complementary manner, such that the load balancing or power saving can still be achieved without sacrificing performance. [ABSTRACT FROM AUTHOR]

Content distribution networks (CDNs) built on clouds have recently started to emerge. Compared to conventional CDNs, cloud-based CDNs have the benefit of cost efficient hosting services without owning infrastructure. However, resource provisioning and replica placement in cloud CDNs involve a number of challenging issues, mainly due to the dynamic nature of demand patterns. To deal with this dynamic nature, this paper proposes a set of novel algorithms to solve the joint problem of resource provisioning and caching (i.e., replica placement) for cloud-based CDNs with an emphasis on handling the dynamic demand patterns. Firstly, we propose a provisioning and caching algorithm framework called Differential Provisioning and Caching (DPC) algorithm, which aims to rent cloud resources to build CDNs and whereby to cache contents so that the total rental cost can be minimized while all demands are served. DPC consists of 2 steps. Step 1 first maximizes total demands supported by unexpired resources. Then, step 2 minimizes the total rental cost for new resources to serve all remaining demands. For each step we design both greedy and iterative heuristics, each with different advantages over the existing approaches. Moreover, to dynamically adjusts the placement of contents and route maps, we further propose the Caching and Request Balancing (CRB) algorithm, which is light-weight and thus can be frequently executed as a companion of DPC to maximize the total demands. Performance evaluation results are presented to demonstrate the effectiveness and competitiveness of our approaches when compared to existing algorithms. [ABSTRACT FROM PUBLISHER]