Open this publication in new window or tab >>2006 (English)In: Proceedings of the IEEE International Symposium on High Performance Distributed Computing 2006, 2006, Vol. 15th IEEE International Symposium, p. 132-143Conference paper, Published paper (Refereed)
Abstract [en]
We present the implementation and analysis of a market-based resource allocation system for computational Grids. Although Grids provide a way to share resources and take advantage of statistical multiplexing, a variety of challenges remain. One is the economically efficient allocation of resources to users from disparate organizations who have their own and sometimes conflicting requirements for both the quantity and quality of services. Another is secure and scalable authorization despite rapidly changing allocations.
Our solution to both of these challenges is to use a market-based resource allocation system. This system allows users to express diverse quantity- and quality-of-service requirements, yet prevents them from denying service to other users. It does this by providing tools to the user to predict and tradeoff risk and expected return in the computational market. In addition, the system enables secure and scalable authorization by using signed money-transfer tokens instead of identity-based authorization. This removes the overhead of maintaining and updating access control lists, while restricting usage based on the amount of money transferred We examine the performance of the system by running a bioinformatics application on a fully operational implementation of an integrated Grid market.
Keywords
Computational methods, Marketing, Multiplexing, Quality of service, Resource allocation, Statistical methods, Computational market, Computing grid, Market based resource allocation system, Quantity of service
National Category
Industrial Biotechnology Information Systems
Identifiers
urn:nbn:se:kth:diva-7799 (URN)10.1109/HPDC.2006.1652144 (DOI)000239086500011 ()2-s2.0-33845897137 (Scopus ID)
Conference
The IEEE International Symposium on High Performance Distributed Computing 2006
Note
QC 201006222007-12-102007-12-102022-06-26Bibliographically approved