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Hybrid peer-to-peer assisted cloud-based video-on-demand (VoD) systems augment cloud-based VoD systems with P2P networks to improve scalability and save bandwidth costs in the cloud. In these systems, the VoD service provider (e.g., NetFlix) relies on the cloud to deliver videos to users and pays for the cloud bandwidth consumption. The users can download videos from both the cloud and peers in the P2P network. It is important for the VoD service provider to i) minimize the cloud bandwidth consumption, and ii) guarantee users' satisfaction (i.e., quality-of-experience). Though previous adaptive bitrate streaming (ABR) methods improve video playback smoothness, they cannot achieve these two goals simultaneously. To tackle this challenge, we propose AutoTune, a game-based adaptive bitrate streaming method. In AutoTune, we formulate the bitrate adaptation problem in ABR as a noncooperative Stackelberg game, where VoD service provider and the users are players. The VoD service provider acts as a leader and it decides the VoD service price for users with the objective of minimizing cloud bandwidth consumption while ensuring users' participation. In response to the VoD service price, the users select video bitrates that lead to maximum utility (defined as a function of its satisfaction minus associated VoD service fee). Finally, the Stackelberg equilibrium is reached in which the cloud bandwidth consumption is minimized while users are satisfied with selected video bitrates. To enhance the performance of AutoTune, we further propose the reputation-based incentive scheme and the popularity-based cache management scheme. Experimental results from the PeerSim simulator and the PlanetLab real-world testbed show that compared to existing methods, AutoTune can provide high user satisfaction and save cloud bandwidth consumption. Also, the proposed enhancement schemes are effective in improving the performance of AutoTune. [ABSTRACT FROM AUTHOR]

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

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

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

In current peer-to-peer (P2P) live streaming systems, nodes in a channel form a P2P overlay for video sharing. To watch a new channel, a node depends on the centralized server to join in the overlay of the channel. In today's live streaming applications, the increase in the number of channels triggers users' desire of watching multiple channels successively or simultaneously. However, the support of such watching modes in current applications is no better than joining in different channel overlays successively or simultaneously through the centralized server, which if widely used, poses a heavy burden on the server. In order to achieve higher efficiency and scalability, we propose a Social-network-Aided efficient liVe strEaming system (SAVE). SAVE regards users' channel switching or multichannel watching as interactions between channels. By collecting the information of channel interactions, nodes' interests, and watching times, SAVE forms nodes in multiple channels with frequent interactions into an overlay, constructs bridges between overlays of channels with less frequent interactions, and enables nodes to identify friends sharing similar interests and watching times. Thus, a node can connect to a new channel while staying in its current overlay, using bridges or relying on its friends, reducing the need to contact the centralized server. We further propose the channel-closeness-based chunk-pushing strategy and capacity-based chunk provider selection strategy to enhance the system performance. Extensive experimental results from the PeerSim simulator and PlanetLab verify that SAVE outperforms other systems in system efficiency and server load reduction, as well as the effectiveness of the two proposed strategies. [ABSTRACT FROM PUBLISHER]

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

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

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