A Framework for Downloading Wide-Area Files

The challenge of efficiently retrieving files that are broken into segments and replicated across the widearea is of prime importance to wide-area, peer-to-peer, and Grid file systems. Two different algorithms addressing this challenge have been proposed and evaluated. While both have been successful in different performance scenarios, there has been no unifying work that can view both algorithms under a single framework. In this thesis, we define such a framework, where download algorithms are defined in terms of the four dimensions that the client always controls: the number of simultaneous downloads, the degree of work replication, the failover strategy, and the server selection algorithm. We then explore the impact of varying parameters along each of these dimensions, testing the framework over several types of file distributions. In addition, the additional dependencies and trends that arise when files are augmented with erasure codes rather than replication are examined

Adaptive Quality of Service Engine with Dynamic Queue Control

While the current routing and congestion control algorithms in use today are often sufficient for networks with relatively static topology, these algorithms may not be sufficient for military networks where a certain level of quality of service (QoS) needs to be achieved to complete a mission. Current networking technology limits a network\u27s ability to adapt to changes and interactions in the network, often resulting in sub-optimal performance. This research investigates the use of queue size predictions to create a network controller to optimize computer networks. These queue size predictions are made possible through the use of Kalman filters to detect network congestion. The premise is that intelligent agents can use such predictions to form context-aware, cognitive processes for managing communication in mobile networks. The network controller designed and implement in this thesis will take in the current and predicted network conditions and make intelligent choices to optimize the network

Adaptive Timeout Discovery using the Network Weather Service

this paper, we present a novel methodology for improving the performance and dependability of application-level messaging in Grid systems. Based on the Network Weather Service, our system uses non-parametric statistical forecasts of requestresponse times to automatically determine message timeouts. By choosing a timeout based on predicted network performance, the methodology improves application and Grid service performance as extraneous and overly-long timeouts are avoided. We describe the technique, the additional execution and programming overhead it introduces, and demonstrate the effectiveness using a wide-area test applicatio