A HOLISTIC REDUNDANCY- AND INCENTIVE-BASED FRAMEWORK TO IMPROVE CONTENT AVAILABILITY IN PEER-TO-PEER NETWORKS

Peer-to-Peer (P2P) technology has emerged as an important alternative to the traditional client-server communication paradigm to build large-scale distributed systems. P2P enables the creation, dissemination and access to information at low cost and without the need of dedicated coordinating entities. However, existing P2P systems fail to provide high-levels of content availability, which limit their applicability and adoption. This dissertation takes a holistic approach to device mechanisms to improve content availability in large-scale P2P systems. Content availability in P2P can be impacted by hardware failures and churn. Hardware failures, in the form of disk or node failures, render information inaccessible. Churn, an inherent property of P2P, is the collective effect of the users’ uncoordinated behavior, which occurs when a large percentage of nodes join and leave frequently. Such a behavior reduces content availability significantly. Mitigating the combined effect of hardware failures and churn on content availability in P2P requires new and innovative solutions that go beyond those applied in existing distributed systems. To addresses this challenge, the thesis proposes two complementary, low cost mechanisms, whereby nodes self-organize to overcome failures and improve content availability. The first mechanism is a low complexity and highly flexible hybrid redundancy scheme, referred to as Proactive Repair (PR). The second mechanism is an incentive-based scheme that promotes cooperation and enforces fair exchange of resources among peers. These mechanisms provide the basis for the development of distributed self-organizing algorithms to automate PR and, through incentives, maximize their effectiveness in realistic P2P environments. Our proposed solution is evaluated using a combination of analytical and experimental methods. The analytical models are developed to determine the availability and repair cost properties of PR. The results indicate that PR’s repair cost outperforms other redundancy schemes. The experimental analysis was carried out using simulation and the development of a testbed. The simulation results confirm that PR improves content availability in P2P. The proposed mechanisms are implemented and tested using a DHT-based P2P application environment. The experimental results indicate that the incentive-based mechanism can promote fair exchange of resources and limits the impact of uncooperative behaviors such as “free-riding”

A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques

Hoverlay : a peer-to-peer system for on demand sharing of capacity across network applications

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Hoverlay : a peer-to-peer system for on demand sharing of capacity across network applications

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Accurate Player Modeling and Cheat-Proof Gameplay in Peer-to-Peer Based Multiplayer Online Games

We present the first detailed measurement study and models of the virtual populations in popular Massively Multiplayer Online Role-Playing Games (MMORPGs). Our results show that, amongst several MMORPGs with very different play styles, the patterns of behaviors are consistent and can be described using a common set of models. In addition, we break down actions common to Trading Card Games (TCGs) and explain how they can be executed between players without the need for a third party referee. In each action, the player is either prevented from cheating, or if they do cheat, the opponent will be able to prove they have done so. We show these methods are secure and may be used in many various styles of TCGs. We measure moves in a real TCG to compare to our implementation of Match+Guardian (M+G), our secure Peer-to-Peer (P2P) protocol for implementing online TCGs. Our results, based on an evaluation of M+G\u27s performance on the Android (TM) platform, show that M+G can be used in a P2P fashion on mobile devices. Finally, we introduce and outline a HYbrid P2P ARchitecture for Trading Card Games, HYPAR-TCG. The system utilizes Distributed Hash Tables (DHTs) and other P2P overlays to store cached game data and to perform game matchmaking. This helps reduce the network and computational load to the central servers. We describe how a centralized server authority can work in concert with a P2P gameplay protocol, while still allowing for reputation and authoritative account management

Efficient Content Distribution With Managed Swarms

Content distribution has become increasingly important as people have become more reliant on Internet services to provide large multimedia content. Efficiently distributing content is a complex and difficult problem: large content libraries are often distributed across many physical hosts, and each host has its own bandwidth and storage constraints. Peer-to-peer and peer-assisted download systems further complicate content distribution. By contributing their own bandwidth, end users can improve overall performance and reduce load on servers, but end users have their own motivations and incentives that are not necessarily aligned with those of content distributors. Consequently, existing content distributors either opt to serve content exclusively from hosts under their direct control, and thus neglect the large pool of resources that end users can offer, or they allow end users to contribute bandwidth at the expense of sacrificing complete control over available resources. This thesis introduces a new approach to content distribution that achieves high performance for distributing bulk content, based on managed swarms. Managed swarms efficiently allocate bandwidth from origin servers, in-network caches, and end users to achieve system-wide performance objectives. Managed swarming systems are characterized by the presence of a logically centralized coordinator that maintains a global view of the system and directs hosts toward an efficient use of bandwidth. The coordinator allocates bandwidth from each host based on empirical measurements of swarm behavior combined with a new model of swarm dynamics. The new model enables the coordinator to predict how swarms will respond to changes in bandwidth based on past measurements of their performance. In this thesis, we focus on the global objective of maximizing download bandwidth across end users in the system. To that end, we introduce two algorithms that the coordinator can use to compute efficient allocations of bandwidth for each host that result in high download speeds for clients. We have implemented a scalable coordinator that uses these algorithms to maximize system-wide aggregate bandwidth. The coordinator actively measures swarm dynamics and uses the data to calculate, for each host, a bandwidth allocation among the swarms competing for the host's bandwidth. Extensive simulations and a live deployment show that managed swarms significantly outperform centralized distribution services as well as completely decentralized peer-to-peer systems

A credit-based approach to scalable video transmission over a peer-to-peer social network

PhDThe objective of the research work presented in this thesis is to study scalable video transmission over peer-to-peer networks. In particular, we analyse how a credit-based approach and exploitation of social networking features can play a significant role in the design of such systems. Peer-to-peer systems are nowadays a valid alternative to the traditional client-server architecture for the distribution of multimedia content, as they transfer the workload from the service provider to the final user, with a subsequent reduction of management costs for the former. On the other hand, scalable video coding helps in dealing with network heterogeneity, since the content can be tailored to the characteristics or resources of the peers. First of all, we present a study that evaluates subjective video quality perceived by the final user under different transmission scenarios. We also propose a video chunk selection algorithm that maximises received video quality under different network conditions. Furthermore, challenges in building reliable peer-to-peer systems for multimedia streaming include optimisation of resource allocation and design mechanisms based on rewards and punishments that provide incentives for users to share their own resources. Our solution relies on a credit-based architecture, where peers do not interact with users that have proven to be malicious in the past. Finally, if peers are allowed to build a social network of trusted users, they can share the local information they have about the network and have a more complete understanding of the type of users they are interacting with. Therefore, in addition to a local credit, a social credit or social reputation is introduced. This thesis concludes with an overview of future developments of this research work

Partitioning and Offloading for IoT and Video Streaming Applications that Utilize Computing Resources at the Network Edge

The Internet of Things (IoT) is a concept in which physical objects embedded with sensors, actuators, and network connectivity can communicate and react to their surroundings. IoT applications connect physical objects for the purpose of decision making by sensing and analysing generated data from the embedded sensors in physical objects. IoT applications are growing rapidly as sensors become less expensive. Sensors generate large amounts of data that may meaningless unless the data is used to derive knowledge with in a certain period of time. Stream processing paradigm is used by IoT to provide requirements of IoT applications. In a stream processing paradigm, unlike traditional data bases, data is not stored but rather processed as it is generated. To transfer generated data from distributed data sources to a processing center such as cloud may not allow for real-time processing due to the network delay. Another high-demand application is live streaming of video. The performance of live video stream systems is inferior when there is a sudden large demand in the number of users. This thesis addresses some of the limitations of current architectures for video streaming systems and IoT applications based on the use of nearby computing resources (e.g., cloudlet, fog). First, we addressed the degrading performance in video stream systems when a flash crowd occurs. The performance of video streaming systems is affected by flash crowd and degrade the quality of service for subscribers to the content delivery system. A flash crowd happens when there is a sudden large increase in the number of users. Therefore, flash crowds increase network traffic for any particular server. The main challenge is to make sure that the video streaming system has sufficient capacity to handle the occurrence of flash crowds. Second, we address the limitation of current architectures for running mobile applications by introducing a dynamic partitioning and offloading of a mobile application. Mobile devices have limited resources including short battery life, storage capacity and processor performance. This limits the applications that can run on it. Mobile applications can be partitioned so that some of the application runs on a cloud. This works well for applications with relatively little data to be transferred and that do not have a high level of interaction with the user. Challenges with applications that have large amounts of data to be transferred and have a high level interactiveness is the high latency incurred by the network and packet loss of the wireless network. A mobile application can be partitioned so that part of it runs on a nearby computing resource e.g., fog node or cloudlet. This thesis presents a framework that introduces fine-grained offloading approach and support for runtime and dynamic partitioning of an application. Third, we present a solution for placement of stream operators over distributed fog nodes for live processing of data streams from geographically distributed data sources. This placement of stream operators takes place in such a way that it supports applications with a high volume of data that require real-time (or near real-time) analysis To this end, this thesis proposed a set of algorithms for placement of stream operators among fog nodes