Paper Info
Title
E-HEFT: Enhancement Heterogeneous Earliest Finish Time algorithm for Task Scheduling based on Load Balancing in Cloud Computing
Abstract
Cloud computing is one of the most widely spreaded platforms for executing tasks through virtual machines as processing elements. However, there are various issues that need to be addressed in order to be efficiently utilized for workflow applications. One of the fundamental issues in cloud computing is related to task scheduling. Optimal scheduling of tasks in cloud computing is an NP-complete optimization problem, and many algorithms have been proposed to solve it. Furthermore, the existing algorithms fail to either meet the user's Quality of Service (QoS) requirements such as minimizing the makespan and satisfying budget constraints, or to incorporate some basic principles of cloud computing such as elasticity and heterogeneity of computing resources. Among these algorithms, the Heterogeneous Earliest Finish Time (HEFT) heuristic is known to give good results in short time for tasks scheduling in heterogeneous systems. Generally, the HEFT algorithm yields good tasks execution time, but its drawback is that there is no load balancing. In this paper, an enhancement of Heterogeneous Earliest Finish Time (E-HEFT) algorithm under a user-specified financial constraint is proposed to achieve a well balanced load across the virtual machines while trying to minimize the makespan of a given workflow application. To evaluate the performance of the enhancement algorithm, we compare our algorithm with some existing scheduling algorithms. Experimental results show that our algorithm outperforms other algorithms by reducing the makespan and improving load balance among virtual machines.
Year
DOI
Venue
2018
10.1109/HPCS.2018.00100
2018 International Conference on High Performance Computing & Simulation (HPCS)
Keywords
Field
DocType
HEFT algorithm,heterogeneous earliest finish time heuristic,enhancement heterogeneous earliest finish time algorithm,processing elements,workflow applications,quality of service requirements,budget constraints,elasticity,heterogeneity,user-specified financial constraint,makespan,computing resources,NP-complete optimization problem,optimal scheduling,virtual machines,cloud computing,load balancing,task scheduling,E-HEFT
Load management,Heuristic,Job shop scheduling,Virtual machine,Load balancing (computing),Scheduling (computing),Computer science,Algorithm,Workflow application,Cloud computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-7880-0
3
0.35
References 
Authors
11
3
Name
Order
Citations
PageRank
Yassir Samadi171.43
Mostapha Zbakh284.83
Claude Tadonki372.78