A Multi-Agent System Approach to Load-Balancing and Resource Allocation for Distributed Computing
Abstract
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- September 2015
- DOI:
- 10.48550/arXiv.1509.06420
- arXiv:
- arXiv:1509.06420
- Bibcode:
- 2015arXiv150906420B
- Keywords:
-
- Computer Science - Neural and Evolutionary Computing;
- Computer Science - Distributed;
- Parallel;
- and Cluster Computing
- E-Print:
- Complex Systems Digital Campus 2015 World eConference Conference on Complex Systems