Dynamic task scheduling in computing cluster environments

TL;DR

This paper introduces a dynamic, load-balancing task scheduling algorithm for cluster computing environments that models the system as hyper-grids to optimize performance and resource utilization.

Contribution

It proposes a novel hyper-grid based dynamic scheduling algorithm combining centralized and decentralized policies for improved load balancing.

Findings

Algorithm effectively balances load among cluster nodes.

Simulation confirms improved system efficiency.

Identifies critical points for algorithm activation.

Abstract

In this study, a cluster-computing environment is employed as a computational platform. In order to increase the efficiency of the system, a dynamic task scheduling algorithm is proposed, which balances the load among the nodes of the cluster. The technique is dynamic, nonpreemptive, adaptive, and it uses a mixed centralised and decentralised policies. Based on the divide and conquer principle, the algorithm models the cluster as hyper-grids and then balances the load among them. Recursively, the hyper-grids of dimension k are divided into grids of dimensions k - 1, until the dimension is 1. Then, all the nodes of the cluster are almost equally loaded. The optimum dimension of the hyper-grid is chosen in order to achieve the best performance. The simulation results show the effective use of the algorithm. In addition, we determined the critical points (lower bounds) in which the algorithm can to be triggered.