Grid Self-Load-Balancing: the Agent Process Paradigm

Abstract

Load balancing aims to exploit networked resources equitably in such a way that no resources are overloaded while others are under-loaded or idle. Many approaches have been proposed and implemented, but as new infrastructures emerge like grids and Global Computing (GC), new challenges are raised with regard to network latency. The location policy, as one of the main fundamentals of load balancing solutions, aims to locate overloaded and under-loaded nodes in a network. To do so, multiple communication messages are sent across the network. This technique wastes network resources and causes remarkable network delays in environments like GC, which makes it impractical. In this paper, we propose a new paradigm for adaptive distributed load balancing inspired by swarm intelligence and multi-agent systems. In such a paradigm, no load balancing service would be required. In fact, work tasks are self-aware and capable of self-load-balancing over an unknown-load network. By its nature and based on stigmergy mechanisms, communication frequency of the proposed paradigm is significantly reduced compared to existing solutions. The present work explains the fundamentals of this paradigm, coined the Agent Process Paradigm (APP), as well as its underlying algorithms. Results of performance evaluation are presented and discussed at the end of this paper.