Yuhua Lin

SocialTube: P2P-assisted Video Sharing in Online Social Networks

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.

CloudFog: Leveraging Fog to Extend Cloud Gaming for Thin-Client MMOG with High Quality of Service

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.

VShare: A Wireless Social Network Aided Vehicle Sharing System Using Hierarchical Cloud Architecture

Carpool commuting enables multiple individual travelers with similar schedules and itineraries to share a common vehicle during a trip, and travelers can split travel costs in gas, parking and tolls with each other. It emerges as an effective way to solve traffic congestion, parking space tension and air pollution resulting from vehicle emissions. One of the challenges that restrict widespread adoption of carpool commuting lies in matching carpoolers. Existing carpooler matching methods include building carpool lanes in main airports and bus stops, using centralized servers to identify carpoolers based on historical travel data or real time travel requests. However, these methods cannot be applied to large scale adoption or incur long matching latency. To overcome drawbacks of existing methods, we propose VShare, a dynamic carpool system that leverages the wireless social network characteristic and hierarchical cloud server architecture. VShare incorporates two design components: matching through the wireless social network and a hierarchical cloud server structure. Upon receiving a user travel request, VShare first identifies possible carpoolers from neighbors in nearby locations, which reduces latency of sending travel requests to remote servers. If no carpool is found within nearby locations, a hierarchical cloud server architecture is used to match the travel requests. We have implemented the design of VShare and conducted trace-driven experiments. Experimental results show the effectiveness of VShare in substantially reducing matching latency while providing high success rate in matching carpollers.

An Interest-Based Per-Community P2P Hierarchical Structure for Short Video Sharing in the YouTube Social Network

The past few years have seen an explosion in the popularity of online short-video sharing in You Tube. As the number of users continued to grow, the bandwidth required to maintain acceptable quality of service (QoS) has greatly increased. Peer-to-peer (P2P) architectures have shown promise in reducing the bandwidth costs, however, the previous works build one P2P overlay for each video, which provides limited availability of video providers and produces high overlay maintenance overhead. To handle these problems, in this work, we novelly leverage the existing social network in You Tube, where a user subscribes to another users channel to track all his uploaded videos. The subscribers of a channel tend to watch the channels videos and common-interest nodes tend to watch the same videos. Also, the popularity of videos in one channel varies greatly. We study real trace data to confirm these properties. Based on these properties, we propose Social Tube that builds the subscribers of one channel into a P2P overlay and also clusters common-interest nodes in a higher level. It also incorporates a prefetching algorithm that prefetches higher-popularity videos. Extensive trace-driven simulation results and Planet Lab real world experimental results verify the effectiveness of Social Tube at reducing server load and overlay maintenance overhead and at improving QoS for users.

Combining Efficiency, Fidelity, and Flexibility in Resource Information Services

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.

Enhancing Collusion Resilience in Reputation Systems

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 collusions 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.

EcoFlow: An Economical and Deadline-Driven Inter-Datacenter Video Flow Scheduling System

As video streaming applications are deployed on the cloud, cloud providers are charged by ISPs for inter-data enter transfers under the dominant percentile-based charging models. In order to minimize the payment costs, existing works aim to keep the traffic on each link under the charging volume (i.e., 95th percentile traffic volume from the beginning of a charging period up to current time). However, these methods cannot fully utilize each links available bandwidth capacity, and may increase the charging volumes. To further reduce the bandwidth payment cost by fully utilizing link bandwidth, we propose an economical and deadline-driven video flow scheduling system, called EcoFlow. Considering different video flows have different transmission deadlines, EcoFlow transmits videos in the order of their deadline tightness and postpones the deliveries of later-deadline videos to later time slots so that the charging volume at current time interval will not increase. The flows that are expected to miss their deadlines are divided into sub flows to be rerouted to other underutilized links in order to meet their deadlines without increasing charging volumes. Experimental results on Planet Lab and EC2 show that compared to existing methods, EcoFlow achieves the least bandwidth costs for cloud providers.

A locality-aware similar information searching scheme

In a database, a similar information search means finding data records which contain the majority of search keywords. Due to the rapid accumulation of information nowadays, the size of databases has increased dramatically. An efficient information searching scheme can speed up information searching and retrieve all relevant records. This paper proposes a Hilbert curve-based similarity searching scheme (HCS). HCS considers a database to be a multidimensional space and each data record to be a point in the multidimensional space. By using a Hilbert space filling curve, each point is projected from a high-dimensional space to a low-dimensional space, so that the points close to each other in the high-dimensional space are gathered together in the low-dimensional space. Because the database is divided into many clusters of close points, a query is mapped to a certain cluster instead of searching the entire database. Experimental results prove that HCS dramatically reduces the search time latency and exhibits high effectiveness in retrieving similar information.

EAFR: An Energy-Efficient Adaptive File Replication System in Data-Intensive Clusters

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.

SmartQ: A Question and Answer System for Supplying High-Quality and Trustworthy Answers

Question and Answer (Q&A) systems aggregate the collected intelligence of all users to provide satisfying answers for questions. A well-developed Q&A system should incorporate features such as high question response rate, high answer quality, a spam-free environment for users and bridging disjoint social clusters. Previous works use reputation systems to achieve the goals. However, these reputation systems evaluate a user with an overall rating for all questions the user has answered regardless of the question categories, thus the reputation score does not accurately reflect the users ability to answer a question in a specific category. We propose SmartQ: a reputation based Q&A System. SmartQ employs a category and theme based reputation management system to evaluate users’ willingness and capability to answer various kinds of questions. The reputation system facilitates the forwarding of a question to favorable experts, which improves the question response rate and answer quality. SmartQ bridges disjoint social clusters by calculating reputation scores for each cluster on each question theme; SmartQ incorporates a lightweight spammer detection method to identify potential spammers. In order to reduce the loads of experts, we propose a strategy to recommend suggested answers from similar questions to each new question. Our trace-driven simulation on PeerSim demonstrates the effectiveness of SmartQ in providing good user experience. We then develop a real application of SmartQ and deploy it for use in a student group in Clemson University. The user feedback shows that SmartQ can provide high-quality answers for users in a community.