Evaluation Performance of Task Scheduling Algorithms in Heterogeneous Environments

Abstract

A heterogeneous computing environment is a large-scale distributed data processing environment, it is depends to some extent parameters on the application and that classified in three main categories such as the hardware, the communication layer, and the software. A computer system is consists of hardware and software from two or more different manufacturers. Scheduling is one of the important factors in the heterogeneous environment and the aim of task scheduling in the processing environment is to move computation towards data. In order to achieve improve performance, increase the throughput and minimizing the makespan; scheduler must avoid unnecessary data transmission. Hence, different scheduling algorithms for heterogeneous computing environment are necessary to provide good performance. How to speedup scheduling the service resources to achieve the lowest cost becomes more and more important. This paper tries to illustrate and analyze the overview of eighteen different scheduling algorithms for heterogeneous computing environment and their scheduling issues and problems.

A Unified Mechanism for Cloud Scheduling of Scientific Workflows

“…Scheduling algorithms could use heuristic and metaheuristic approaches and, therefore, two well-known categories of the heuristic and meta-heuristic algorithms do exist in the literature. The traditional and well known heuristic algorithms include First Come First Serve (FCFS) [29], Round Robin (RR) [29,30], Minimum Completion Time (MCT) [31][32][33], Min-Min [34], Max-Min [35], Heterogeneous Earliest Finish Time (HEFT) [34,36], Dynamic HEFT (DHEFT) [37] and Data Aware [36]. The literature exists recently proposed extended versions of these algorithms such as those presented in [38] and [39].…”

“…If a resource is assigned to a job, which is processing an already assigned job, then, the new job has to wait until a resource finishes that job. DHEFT was developed to solve these issues by equipping it with an efficient and dynamic scheduler for more effective assignment of tasks to the compute nodes [37].…”