A new reliability‐based task scheduling algorithm in cloud computing

Summary In the last decade, the scale of heterogeneous computing (HC) systems such as heterogeneous cloud computing environments was growing like never before. So network failures are unavoidable in such systems, which affect system reliability. Since the task scheduling algorithm in HC is challengi...

Full description

Saved in:
Bibliographic Details
Published in:International journal of communication systems Vol. 35; no. 3
Main Authors: Amini Motlagh, Aida, Movaghar, Ali, Rahmani, Amir Masoud
Format: Journal Article
Language:English
Published: Chichester Wiley Subscription Services, Inc 01.02.2022
Subjects:
Cloud computing
directed acyclic graph (DAG)
evolutionary algorithm
Evolutionary algorithms
Failure rates
Genetic algorithms
Graph theory
heterogeneous systems
Network reliability
reliability
Scheduling
System reliability
Task scheduling
Virtual environments
virtual machine
ISSN:1074-5351, 1099-1131
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Summary In the last decade, the scale of heterogeneous computing (HC) systems such as heterogeneous cloud computing environments was growing like never before. So network failures are unavoidable in such systems, which affect system reliability. Since the task scheduling algorithm in HC is challenging, we investigate a new reliability‐aware task scheduling algorithm (RATSA) in this paper. RATSA is designed to schedule tasks on directed acyclic graphs (DAGs) by using the shuffled frog‐leaping algorithm (SFLA) and genetic algorithm (GA) as evolutionary algorithms. The population‐based SFLA‐GA is applied to optimize makespan in the RATSA as an NP‐complete problem. Moreover, the proposed algorithm exploits a new heuristic‐based earliest finish time technique for task mapping to virtual machines (VMs) section to decrease the failure rate. Experimental results on random DAGs indicate that RATSA improves some current algorithms in terms of reliability and has acceptable performance in makespan. The results reveal that the RATSA decreases the overall failure rate by 43% compared to some current task scheduling algorithms. We investigate a new reliability‐aware task scheduling algorithm (RATSA) in this paper. RATSA is designed to schedule tasks on DAGs by using shuffled frog‐leaping algorithm (SFLA) and genetic algorithm (GA) as evolutionary algorithms. The population‐based SFLA‐GA is applied to optimize makespan in the RATSA as a NP‐complete problem.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1074-5351
1099-1131
DOI:10.1002/dac.5022