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

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.