Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments

P. Thambidura,E. Ilavarasan

doi:10.3844/jcssp.2007.94.103

Abstract

A heterogeneous computing environment is a suite of heterogeneous processors interconnected by high-speed networks, thereby promising high speed processing of computationally intensive applications with diverse computing needs. Scheduling of an application modeled by Directed Acyclic Graph (DAG) is a key issue when aiming at high performance in this kind of environment. The problem is generally addressed in terms of task scheduling, where tasks are the schedulable units of a program. The task scheduling problems have been shown to be NP-complete in general as well as several restricted cases. In this study we present a simple scheduling algorithm based on list scheduling, namely, low complexity Performance Effective Task Scheduling (PETS) algorithm for heterogeneous computing systems with complexity O (e) (p+ log v), which provides effective results for applications represented by DAGs. The analysis and experiments based on both randomly generated graphs and graphs of some real applications show that the PETS algorithm substantially outperforms the existing scheduling algorithms such as Heterogeneous Earliest Finish Time (HEFT), Critical-Path-On a Processor (CPOP) and Levelized Min Time (LMT), in terms of schedule length ratio, speedup, efficiency, running time and frequency of best results.

Highlights

A growing emphasis on concurrent processing of jobs has lead to an increased acceptance of heterogeneous computing environments and the availability of a network of processors makes a costeffective utilization of underlying parallelism for applications like weather modeling, image processing, real-time and distributed database systems
We proposed a new algorithm called Performance Effective Task Scheduling (PETS) algorithm, which gives the best performance in terms of performance and cost metrics for Directed Acyclic Graph (DAG) structured applications compared to the exiting scheduling algorithms such as Levelized Min Time (LMT) and Heterogeneous Earliest Finish Time (HEFT) and Critical-Path-On a Processor (CPOP) reported in this study
Related works: we present the related task scheduling algorithms for heterogeneous computing environment that we used for comparison with our algorithm, which are Levelized Min Time algorithm[5], Heterogeneous Earliest Finish Time algorithm[6] and Critical Path On a processor algorithm[6]

Summary

Introduction

A growing emphasis on concurrent processing of jobs has lead to an increased acceptance of heterogeneous computing environments and the availability of a network of processors makes a costeffective utilization of underlying parallelism for applications like weather modeling, image processing, real-time and distributed database systems. The time complexity of CPOP algorithm is equal to O (v2 x p) where v is the number of tasks in a dense graph and p is the number of processors.

Results

Conclusion