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

Scalable reliable on-demand media streaming protocols

This thesis considers the problem of delivering streaming media, on-demand, to potentially large numbers of concurrent clients. The problem has motivated the development in prior work of scalable protocols based on multicast or broadcast. However, previous protocols do not allow clients to efficiently: 1) recover from packet loss; 2) share bandwidth fairly with competing flows; or 3) maximize the playback quality at the client for any given client reception rate characteristics. In this work, new protocols, namely Reliable Periodic Broadcast (RPB) and Reliable Bandwidth Skimming (RBS), are developed that efficiently recover from packet loss and achieve close to the best possible server bandwidth scalability for a given set of client characteristics. To share bandwidth fairly with competing traffic such as TCP, these protocols can employ the Vegas Multicast Rate Control (VMRC) protocol proposed in this work. The VMRC protocol exhibits TCP Vegas-like behavior. In comparison to prior rate control protocols, VMRC provides less oscillatory reception rates to clients, and operates without inducing packet loss when the bottleneck link is lightly loaded. The VMRC protocol incorporates a new technique for dynamically adjusting the TCP Vegas threshold parameters based on measured characteristics of the network. This technique implements fair sharing of network resources with other types of competing flows, including widely deployed versions of TCP such as TCP Reno. This fair sharing is not possible with the previously defined static Vegas threshold parameters. The RPB protocol is extended to efficiently support quality adaptation. The Optimized Heterogeneous Periodic Broadcast (HPB) is designed to support a range of client reception rates and efficiently support static quality adaptation by allowing clients to work-ahead before beginning playback to receive a media file of the desired quality. A dynamic quality adaptation technique is developed and evaluated which allows clients to achieve more uniform playback quality given time-varying client reception rates

Abstract Group-Guaranteed Channel Capacity in Multimedia Storage Servers

One of the open questions in the design of multimedia storage servers is in what order to serve incoming requests. Given the capability provided by the disk layout and scheduling algorithms to serve multiple streams simultaneously, improved request scheduling algorithms can reduce customer waiting times. This results in better service and/or lower customer loss. In this paper we define a new class of request scheduling algorithms, called Group-Guaranteed Server Capacity (GGSC), that preassign server channel capacity to groups of objects. We also define a particular formal method for computing the assigned capacities to achieve a given performance objective. We observe that the FCFS policy can provide the precise time of service to incoming customer requests. Under this assumption, we compare the performance of one of the new GGSC algorithms, GGSCW-FCFS, against FCFS and against two other recently proposed scheduling algorithms: Maximum Factored Queue length (MFQ), and the FCFS-n algorithm that preassigns capacity only to each of the n most popular objects. The algorithms are compared for both competitive market and captured audience environments. Key findings of the algorithm comparisons are that: (1) FCFS-n has no advantage over FCFS if FCFS gives time of service guarantees to arriving customers, (2) FCFS and GGSCW-FCFS are superior to MFQ for both competitive and captive audience environments, (3) for competitive servers that are configured for customer loss less than 10%, FCFS is superior to all other algorithms examined in this paper, and (4) for captive audience environments that have objects with variable playback length, GGSCW-FCFS is the most promising of the policies considered in this paper. The conclusions for FCFS-n and MFQ differ from previous work because we focus on competitive environments with customer loss under 10%, we assume FCFS can provide time of service guarantees to all arriving customers, and we consider the distribution of customer waiting time as well as the average waiting time.