Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496125
Title: Decentralised economic resource allocation for computational grids
Author: Davy, Simon Mark
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2008
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
Grid computing is the concept of harnessing the power of many computational resources in a transparent manner. It is currently an active research area, with significant challenges due to the scale and level of heterogeneity involved. One of the key challenges in implementing grid systems is resource allocation. Currently, centralised approaches are employed that have limited scalability and reliability, which is a key factor in achieving a usable grid system. The field of economics is the study of allocating scarce resources using economic mechanisms. Such systems can be highly scalable, robust and adaptive and as such are a potential solution to the grid allocation problem. There is also a natural fit of the economic allocation metaphor to grid systems, given the diversity of autonomy of grid resources. We propose that an economic system is a suitable mechanism for grid resource allocation. We propose a simple market mechanism to explore this idea. Our system is a fully decentralised economic allocation scheme, which aims to achieve a high degree of scalability and reliability, and easily allows resources to retain their autonomy. We implement a simulation of a grid system to analyse this system, and explore its performance and scalability, with a comparison to existing systems. We use a network to facilitate communication between participating agents, and we pay particular attention to the topology of the network between participating agents, examining the effects of different topologies on the performance of the system.
Supervisor: Djemame, K. ; Noble, J. A. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.496125  DOI: Not available
Share: