Design and evaluation of load balancing algorithms in P2P streaming.

Wang, Yongzhi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p.68-72).Abstract also in Chinese.Abstract --- p.iAcknowledgement --- p.iiChapter 1 --- Introduction --- p.1Chapter 2 --- Abstract Model --- p.7Chapter 2.1 --- Request allocation problem --- p.7Chapter 2.2 --- Neighbor selection problem --- p.11Chapter 3 --- Simulation Model --- p.14Chapter 4 --- Load Balancing Algorithms --- p.18Chapter 4.1 --- Request allocation --- p.18Chapter 4.2 --- Neighbor selection algorithms --- p.24Chapter 4.2.1 --- What to measure? --- p.24Chapter 4.2.2 --- Timeout-based neighbor selection algorithms --- p.25Chapter 4.2.3 --- Periodic neighbor selection algorithms --- p.33Chapter 4.2.4 --- Comparison: Timeout-based versus Periodical neighbor selection algorithms --- p.39Chapter 4.3 --- Further experiments --- p.41Chapter 4.3.1 --- Request window size --- p.41Chapter 4.3.2 --- Impact of K --- p.42Chapter 4.3.3 --- Adaptive adjustment of the neighbor selection period --- p.43Chapter 4.3.4 --- Performance with adequate bandwidth --- p.45Chapter 5 --- Minimizing Server´ةs Load --- p.49Chapter 6 --- Background Study --- p.56Chapter 6.1 --- P2P content distribution system --- p.56Chapter 6.1.1 --- P2P File sharing system --- p.56Chapter 6.1.2 --- P2P streaming system --- p.59Chapter 6.1.3 --- P2P Video on Demand system --- p.61Chapter 6.2 --- Congestion control --- p.62Chapter 7 --- Conclusion --- p.67Bibliography --- p.6

A framework for the dynamic management of Peer-to-Peer overlays

Peer-to-Peer (P2P) applications have been associated with inefficient operation, interference with other network services and large operational costs for network providers. This thesis presents a framework which can help ISPs address these issues by means of intelligent management of peer behaviour. The proposed approach involves limited control of P2P overlays without interfering with the fundamental characteristics of peer autonomy and decentralised operation. At the core of the management framework lays the Active Virtual Peer (AVP). Essentially intelligent peers operated by the network providers, the AVPs interact with the overlay from within, minimising redundant or inefficient traffic, enhancing overlay stability and facilitating the efficient and balanced use of available peer and network resources. They offer an “insider‟s” view of the overlay and permit the management of P2P functions in a compatible and non-intrusive manner. AVPs can support multiple P2P protocols and coordinate to perform functions collectively. To account for the multi-faceted nature of P2P applications and allow the incorporation of modern techniques and protocols as they appear, the framework is based on a modular architecture. Core modules for overlay control and transit traffic minimisation are presented. Towards the latter, a number of suitable P2P content caching strategies are proposed. Using a purpose-built P2P network simulator and small-scale experiments, it is demonstrated that the introduction of AVPs inside the network can significantly reduce inter-AS traffic, minimise costly multi-hop flows, increase overlay stability and load-balancing and offer improved peer transfer performance

Flexible Application-Layer Multicast in Heterogeneous Networks

This work develops a set of peer-to-peer-based protocols and extensions in order to provide Internet-wide group communication. The focus is put to the question how different access technologies can be integrated in order to face the growing traffic load problem. Thereby, protocols are developed that allow autonomous adaptation to the current network situation on the one hand and the integration of WiFi domains where applicable on the other hand

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

Distributed Information Systems and Data Mining in Self-Organizing Networks

The diffusion of sensors and devices to generate and collect data is capillary. The infrastructure that envelops the smart city has to react to the contingent situations and to changes in the operating environment. At the same time, the complexity of a distributed system, consisting of huge amounts of components fixed and mobile, can generate unsustainable costs and latencies to ensure robustness, scalability, and reliability, with type architectures middleware. The distributed system must be able to self-organize and self-restore adapting its operating strategies to optimize the use of resources and overall efficiency. Peer-to-peer systems (P2P) can offer solutions to face the requirements of managing, indexing, searching and analyzing data in scalable and self-organizing fashions, such as in cloud services and big data applications, just to mention two of the most strategic technologies for the next years. In this thesis we present G-Grid, a multi-dimensional distributed data indexing able to efficiently execute arbitrary multi-attribute exact and range queries in decentralized P2P environments. G-Grid is a foundational structure and can be effectively used in a wide range of application environments, including grid computing, cloud and big data domains. Nevertheless we proposed some improvements on the basic structure introducing a bit of randomness by using Small World networks, whereas are structures derived from social networks and show an almost uniform traffic distribution. This produced huge advantages in efficiency, cutting maintenance costs, without losing efficacy. Experiments show how this new hybrid structure obtains the best performance in traffic distribution and it a good settlement for the overall performance on the requirements desired in the modern data systems

Economic regulation for multi tenant infrastructures

Large scale computing infrastructures need scalable and effi cient resource allocation mechanisms to ful l the requirements of its participants and applications while the whole system is regulated to work e ciently. Computational markets provide e fficient allocation mechanisms that aggregate information from multiple sources in large, dynamic and complex systems where there is not a single source with complete information. They have been proven to be successful in matching resource demand and resource supply in the presence of sel sh multi-objective and utility-optimizing users and sel sh pro t-optimizing providers. However, global infrastructure metrics which may not directly affect participants of the computational market still need to be addressed -a.k.a. economic externalities like load balancing or energy-efficiency. In this thesis, we point out the need to address these economic externalities, and we design and evaluate appropriate regulation mechanisms from di erent perspectives on top of existing economic models, to incorporate a wider range of objective metrics not considered otherwise. Our main contributions in this thesis are threefold; fi rst, we propose a taxation mechanism that addresses the resource congestion problem e ffectively improving the balance of load among resources when correlated economic preferences are present; second, we propose a game theoretic model with complete information to derive an algorithm to aid resource providers to scale up and down resource supply so energy-related costs can be reduced; and third, we relax our previous assumptions about complete information on the resource provider side and design an incentive-compatible mechanism to encourage users to truthfully report their resource requirements effectively assisting providers to make energy-eff cient allocations while providing a dynamic allocation mechanism to users.Les infraestructures computacionals de gran escala necessiten mecanismes d’assignació de recursos escalables i eficients per complir amb els requisits computacionals de tots els seus participants, assegurant-se de que el sistema és regulat apropiadament per a que funcioni de manera efectiva. Els mercats computacionals són mecanismes d’assignació de recursos eficients que incorporen informació de diferents fonts considerant sistemes de gran escala, complexos i dinàmics on no existeix una única font que proveeixi informació completa de l'estat del sistema. Aquests mercats computacionals han demostrat ser exitosos per acomodar la demanda de recursos computacionals amb la seva oferta quan els seus participants son considerats estratègics des del punt de vist de teoria de jocs. Tot i això existeixen mètriques a nivell global sobre la infraestructura que no tenen per que influenciar els usuaris a priori de manera directa. Així doncs, aquestes externalitats econòmiques com poden ser el balanceig de càrrega o la eficiència energètica, conformen una línia d’investigació que cal explorar. En aquesta tesi, presentem i descrivim la problemàtica derivada d'aquestes externalitats econòmiques. Un cop establert el marc d’actuació, dissenyem i avaluem mecanismes de regulació apropiats basats en models econòmics existents per resoldre aquesta problemàtica des de diferents punts de vista per incorporar un ventall més ampli de mètriques objectiu que no havien estat considerades fins al moment. Les nostres contribucions principals tenen tres vessants: en primer lloc, proposem un mecanisme de regulació de tipus impositiu que tracta de mitigar l’aparició de recursos sobre-explotats que, efectivament, millora el balanceig de la càrrega de treball entre els recursos disponibles; en segon lloc, proposem un model teòric basat en teoria de jocs amb informació o completa que permet derivar un algorisme que facilita la tasca dels proveïdors de recursos per modi car a l'alça o a la baixa l'oferta de recursos per tal de reduir els costos relacionats amb el consum energètic; i en tercer lloc, relaxem la nostra assumpció prèvia sobre l’existència d’informació complerta per part del proveïdor de recursos i dissenyem un mecanisme basat en incentius per fomentar que els usuaris facin pública de manera verídica i explícita els seus requeriments computacionals, ajudant d'aquesta manera als proveïdors de recursos a fer assignacions eficients des del punt de vista energètic a la vegada que oferim un mecanisme l’assignació de recursos dinàmica als usuari

Effective and Economical Content Delivery and Storage Strategies for Cloud Systems

Cloud computing has proved to be an effective infrastructure to host various applications and provide reliable and stable services. Content delivery and storage are two main services provided by the cloud. A high-performance cloud can reduce the cost of both cloud providers and customers, while providing high application performance to cloud clients. Thus, the performance of such cloud-based services is closely related to three issues. First, when delivering contents from the cloud to users or transferring contents between cloud datacenters, it is important to reduce the payment costs and transmission time. Second, when transferring contents between cloud datacenters, it is important to reduce the payment costs to the internet service providers (ISPs). Third, when storing contents in the datacenters, it is crucial to reduce the file read latency and power consumption of the datacenters. In this dissertation, we study how to effectively deliver and store contents on the cloud, with a focus on cloud gaming and video streaming services. In particular, we aim to address three problems. i) Cost-efficient cloud computing system to support thin-client Massively Multiplayer Online Game (MMOG): how to achieve high Quality of Service (QoS) in cloud gaming and reduce the cloud bandwidth consumption; ii) Cost-efficient inter-datacenter video scheduling: how to reduce the bandwidth payment cost by fully utilizing link bandwidth when cloud providers transfer videos between datacenters; iii) Energy-efficient adaptive file replication: how to adapt to time-varying file popularities to achieve a good tradeoff between data availability and efficiency, as well as reduce the power consumption of the datacenters. In this dissertation, we propose methods to solve each of aforementioned challenges on the cloud. As a result, we build a cloud system that has a cost-efficient system to support cloud clients, an inter-datacenter video scheduling algorithm for video transmission on the cloud and an adaptive file replication algorithm for cloud storage system. As a result, the cloud system not only benefits the cloud providers in reducing the cloud cost, but also benefits the cloud customers in reducing their payment cost and improving high cloud application performance (i.e., user experience). Finally, we conducted extensive experiments on many testbeds, including PeerSim, PlanetLab, EC2 and a real-world cluster, which demonstrate the efficiency and effectiveness of our proposed methods. In our future work, we will further study how to further improve user experience in receiving contents and reduce the cost due to content transfer

System support for keyword-based search in structured Peer-to-Peer systems

In this dissertation, we present protocols for building a distributed search infrastructure over structured Peer-to-Peer systems. Unlike existing search engines which consist of large server farms managed by a centralized authority, our approach makes use of a distributed set of end-hosts built out of commodity hardware. These end-hosts cooperatively construct and maintain the search infrastructure. The main challenges with distributing such a system include node failures, churn, and data migration. Localities inherent in query patterns also cause load imbalances and hot spots that severely impair performance. Users of search systems want their results returned quickly, and in ranked order. Our main contribution is to show that a scalable, robust, and distributed search infrastructure can be built over existing Peer-to-Peer systems through the use of techniques that address these problems. We present a decentralized scheme for ranking search results without prohibitive network or storage overhead. We show that caching allows for efficient query evaluation and present a distributed data structure, called the View Tree, that enables efficient storage, and retrieval of cached results. We also present a lightweight adaptive replication protocol, called LAR that can adapt to different kinds of query streams and is extremely effective at eliminating hotspots. Finally, we present techniques for storing indexes reliably. Our approach is to use an adaptive partitioning protocol to store large indexes and employ efficient redundancy techniques to handle failures. Through detailed analysis and experiments we show that our techniques are efficient and scalable, and that they make distributed search feasible