A multi-agent system approach to load-balancing and resource allocation for distributed computing

Conference item

In this research we use a decentralized computing approach to allocate and schedule tasks on a massively distributed grid. Using emergent properties of multi-agent systems, the algorithm dynamically creates and dissociates clusters to serve the changing resource demands of a global task queue. The algorithm is compared to a standard first-in first-out (FIFO) scheduling algorithm. Experiments done on a simulator show that the distributed resource allocation protocol (dRAP) algorithm outperforms the FIFO scheduling algorithm on time to empty queue, average waiting time, and CPU utilization. Such a decentralized computing approach holds promise for massively distributed processing scenarios like SETI@home and Google MapReduce.

Authors: Banerjee, S; Hecker, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Verlag
• Host title: First Complex Systems Digital Campus World E-Conference 2015
• Journal: Springer Proceedings in Complexity
• Pages: 41-54
• Series: Springer Proceedings in Complexity
• Publication date: 2016-12-26
• Acceptance date: 2017-01-11
• DOI: 10.1007/978-3-319-45901-1_4
• ISSN: 2213-8684
• ISBN: 9783319459011