A fault tolerant, peer-to-peer based scheduler for home grids

This thesis presents a fault-tolerant, Peer-to-Peer (P2P) based grid scheduling system for highly dynamic and highly heterogeneous environments, such as home networks, where we can find a variety of devices (laptops, PCs, game consoles, etc.) and networks. The number of devices found in a house that are capable of processing data has been increasing in the last few years. However, being able to process data does not mean that these devices are powerful, and, in a home environment, there will be a demand for some applications that need significant computing resources, beyond the capabilities of a single domestic device, such as a set top box (examples of such applications are TV recommender systems, image processing and photo indexing systems). A computational grid is a possible solution for this problem, but the constrained environment in the home makes it difficult to use conventional grid scheduling technologies, which demand a powerful infrastructure. Our solution is based on the distribution of the matchmaking task among providers, leaving the final allocation decision to a central scheduler that can be running on a limited device without a big loss in performance. We evaluate our solution by simulating different scenarios and configurations against the Opportunistic Load Balance (OLB) scheduling heuristic, which we found to be the best option for home grids from the existing solutions that we analysed. The results have shown that our solution performs similar or better to OLB. Furthermore, our solution also provides fault tolerance, which is not achieved with OLB, and we have formally verified the behaviour our solution against two cases of network partition failure

An agent-based visualisation system.

This thesis explores the concepts of visual supercomputing, where complex distributed systems are used toward interactive visualisation of large datasets. Such complex systems inherently trigger management and optimisation problems; in recent years the concepts of autonomic computing have arisen to address those issues. Distributed visualisation systems are a very challenging area to apply autonomic computing ideas as such systems are both latency and compute sensitive, while most autonomic computing implementations usually concentrate on one or the other but not both concurrently. A major contribution of this thesis is to provide a case study demonstrating the application of autonomic computing concepts to a computation intensive, real-time distributed visualisation system. The first part of the thesis proposes the realisation of a layered multi-agent system to enable autonomic visualisation. The implementation of a generic multi-agent system providing reflective features is described. This architecture is then used to create a flexible distributed graphic pipeline, oriented toward real-time visualisation of volume datasets. Performance evaluation of the pipeline is presented. The second part of the thesis explores the reflective nature of the system and presents high level architectures based on software agents, or visualisation strategies, that take advantage of the flexibility of the system to provide generic features. Autonomic capabilities are presented, with fault recovery and automatic resource configuration. Performance evaluation, simulation and prediction of the system are presented, exploring different use cases and optimisation scenarios. A performance exploration tool, Delphe, is described, which uses real-time data of the system to let users explore its performance