Scalable download protocols

Scalable on-demand content delivery systems, designed to effectively handle increasing request rates, typically use service aggregation or content replication techniques. Service aggregation relies on one-to-many communication techniques, such as multicast, to efficiently deliver content from a single sender to multiple receivers. With replication, multiple geographically distributed replicas of the service or content share the load of processing client requests and enable delivery from a nearby server.Previous scalable protocols for downloading large, popular files from a single server include batching and cyclic multicast. Analytic lower bounds developed in this thesis show that neither of these protocols consistently yields performance close to optimal. New hybrid protocols are proposed that achieve within 20% of the optimal delay in homogeneous systems, as well as within 25% of the optimal maximum client delay in all heterogeneous scenarios considered.In systems utilizing both service aggregation and replication, well-designed policies determining which replica serves each request must balance the objectives of achieving high locality of service, and high efficiency of service aggregation. By comparing classes of policies, using both analysis and simulations, this thesis shows that there are significant performance advantages in using current system state information (rather than only proximities and average loads) and in deferring selection decisions when possible. Most of these performance gains can be achieved using only “local” (rather than global) request information.Finally, this thesis proposes adaptations of already proposed peer-assisted download techniques to support a streaming (rather than download) service, enabling playback to begin well before the entire media file is received. These protocols split each file into pieces, which can be downloaded from multiple sources, including other clients downloading the same file. Using simulations, a candidate protocol is presented and evaluated. The protocol includes both a piece selection technique that effectively mediates the conflict between achieving high piece diversity and the in-order requirements of media file playback, as well as a simple on-line rule for deciding when playback can safely commence

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model

Distributed k-ary System: Algorithms for Distributed Hash Tables

This dissertation presents algorithms for data structures called distributed hash tables (DHT) or structured overlay networks, which are used to build scalable self-managing distributed systems. The provided algorithms guarantee lookup consistency in the presence of dynamism: they guarantee consistent lookup results in the presence of nodes joining and leaving. Similarly, the algorithms guarantee that routing never fails while nodes join and leave. Previous algorithms for lookup consistency either suffer from starvation, do not work in the presence of failures, or lack proof of correctness. Several group communication algorithms for structured overlay networks are presented. We provide an overlay broadcast algorithm, which unlike previous algorithms avoids redundant messages, reaching all nodes in O(log n) time, while using O(n) messages, where n is the number of nodes in the system. The broadcast algorithm is used to build overlay multicast. We introduce bulk operation, which enables a node to efficiently make multiple lookups or send a message to all nodes in a specified set of identifiers. The algorithm ensures that all specified nodes are reached in O(log n) time, sending maximum O(log n) messages per node, regardless of the input size of the bulk operation. Moreover, the algorithm avoids sending redundant messages. Previous approaches required multiple lookups, which consume more messages and can render the initiator a bottleneck. Our algorithms are used in DHT-based storage systems, where nodes can do thousands of lookups to fetch large files. We use the bulk operation algorithm to construct a pseudo-reliable broadcast algorithm. Bulk operations can also be used to implement efficient range queries. Finally, we describe a novel way to place replicas in a DHT, called symmetric replication, that enables parallel recursive lookups. Parallel lookups are known to reduce latencies. However, costly iterative lookups have previously been used to do parallel lookups. Moreover, joins or leaves only require exchanging O(1) messages, while other schemes require at least log(f) messages for a replication degree of f. The algorithms have been implemented in a middleware called the Distributed k-ary System (DKS), which is briefly described

Scalable on-demand streaming of stored complex multimedia

Previous research has developed a number of efficient protocols for streaming popular multimedia files on-demand to potentially large numbers of concurrent clients. These protocols can achieve server bandwidth usage that grows much slower than linearly with the file request rate, and with the inverse of client start-up delay. This hesis makes the following three main contributions to the design and performance evaluation of such protocols. The first contribution is an investigation of the network bandwidth requirements for scalable on-demand streaming. The results suggest that the minimum required network bandwidth for scalable on-demand streaming typically scales as K/ln(K) as the number of client sites K increases for fixed request rate per client site, and as ln(N/(ND+1)) as the total file request rate N increases or client start-up delay D decreases, for a fixed number of sites. Multicast delivery trees configured to minimize network bandwidth usage rather than latency are found to only modestly reduce the minimum required network bandwidth. Furthermore, it is possible to achieve close to the minimum possible network and server bandwidth usage simultaneously with practical scalable delivery protocols. Second, the thesis addresses the problem of scalable on-demand streaming of a more complex type of media than is typically considered, namely variable bit rate (VBR) media. A lower bound on the minimum required server bandwidth for scalable on-demand streaming of VBR media is derived. The lower bound analysis motivates the design of a new immediate service protocol termed VBR bandwidth skimming (VBRBS) that uses constant bit rate streaming, when sufficient client storage space is available, yet fruitfully exploits the knowledge of a VBR profile. Finally, the thesis proposes non-linear media containing parallel sequences of data frames, among which clients can dynamically select at designated branch points, and investigates the design and performance issues in scalable on-demand streaming of such media. Lower bounds on the minimum required server bandwidth for various non-linear media scalable on-demand streaming approaches are derived, practical non-linear media scalable delivery protocols are developed, and, as a proof-of-concept, a simple scalable delivery protocol is implemented in a non-linear media streaming prototype system

DIVE on the internet

This dissertation reports research and development of a platform for Collaborative Virtual Environments (CVEs). It has particularly focused on two major challenges: supporting the rapid development of scalable applications and easing their deployment on the Internet. This work employs a research method based on prototyping and refinement and promotes the use of this method for application development. A number of the solutions herein are in line with other CVE systems. One of the strengths of this work consists in a global approach to the issues raised by CVEs and the recognition that such complex problems are best tackled using a multi-disciplinary approach that understands both user and system requirements. CVE application deployment is aided by an overlay network that is able to complement any IP multicast infrastructure in place. Apart from complementing a weakly deployed worldwide multicast, this infrastructure provides for a certain degree of introspection, remote controlling and visualisation. As such, it forms an important aid in assessing the scalability of running applications. This scalability is further facilitated by specialised object distribution algorithms and an open framework for the implementation of novel partitioning techniques. CVE application development is eased by a scripting language, which enables rapid development and favours experimentation. This scripting language interfaces many aspects of the system and enables the prototyping of distribution-related components as well as user interfaces. It is the key construct of a distributed environment to which components, written in different languages, connect and onto which they operate in a network abstracted manner. The solutions proposed are exemplified and strengthened by three collaborative applications. The Dive room system is a virtual environment modelled after the room metaphor and supporting asynchronous and synchronous cooperative work. WebPath is a companion application to a Web browser that seeks to make the current history of page visits more visible and usable. Finally, the London travel demonstrator supports travellers by providing an environment where they can explore the city, utilise group collaboration facilities, rehearse particular journeys and access tourist information data

Slurpie: A Cooperative Bulk Data Transfer Protocol

We present Slurpie: a peer-to-peer protocol for bulk data transfer. Slurpie is specifically designed to reduce client download times for large, popular files, and to reduce load on servers that serve these files. Slurpie employs a novel adaptive downloading strategy to increase client performance, and employs a randomized backoff strategy to precisely control load on the server. We describe a full implementation of the Slurpie protocol, and present results from both controlled localarea and wide-area testbeds. Our results show that Slurpie clients improve performance as the size of the network increases, and the server is completely insulated from large flash crowds entering the Slurpie network