A Clustering and Scheduling Algorithm Based on Task Duplication

Abstract

This paper addresses an important and classic scheduling problem,the static scheduling of dependent tasks in homogeneous environment.It is NP hard even when the resources are unbounded,and finds many applications in the parallel and distributed computation area.Dependent tasks are usually denoted by Directed Acyclic Graph(DAG),and solving heuristics are commonly categorized to priority list based,cluster based and task duplication based schemes.Task-duplication-based(TDB)algorithms are of better performance than non-duplication ones.A new TDB clustering and scheduling algorithm,called the dynamic critical predecessor(DCP)algorithm,is proposed in this paper.DCP algorithm defines a new selective strategy for important ancestors to be duplicated.The primary aim is to get the shortest schedule length,and the next is to utilize as less resources as possible.Based on an improved definition of granularity,DCP algorithm achieves a better performance guarantee for arbitrary DAG than relative works reported in the literature.Experimental results on several benchmarks show that DCP algorithm is quite effective and it exceeds other classic TDB algorithms.Especially for the classic EZ benchmark,DCP algorithm gets an optimal solution with 8 makespan,which is better than the optimal result taken for before with 8.5 makespan.Complement graph of a DAG is defined,and a similar algorithm is developed to produce a 2-optimal schedule for tree graph if task duplication is not allowed for the tasks.