A Multi-Agent System Approach to Load-Balancing and Resource Allocation for Distributed Computing

TL;DR

This paper presents a decentralized multi-agent system algorithm for load balancing and resource allocation in distributed computing, demonstrating improved performance over FIFO scheduling in simulations for large-scale processing tasks.

Contribution

It introduces the dRAP algorithm that dynamically manages clusters in a multi-agent system to optimize resource use and task scheduling in distributed environments.

Findings

dRAP outperforms FIFO in queue time and CPU utilization

Decentralized approach adapts to changing resource demands

Effective for large-scale distributed processing scenarios

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.