Paper Info
Title
Crow-Penguin Optimizer For Multiobjective Task Scheduling Strategy In Cloud Computing
Abstract
Task scheduling in the cloud is the multiobjective optimization problem, and most of the task scheduling problems fail to offer an effective trade-off between the load, resource utilization, makespan, and Quality of Service (QoS). To bring a balance in the trade-off, this paper proposes a method, termed as crow-penguin optimizer for multiobjective task scheduling strategy in cloud computing (CPO-MTS). The proposed algorithm decides the optimal execution of the available tasks in the available cloud resources in minimal time. The proposed algorithm is the fusion of the Crow Search optimization Algorithm (CSA) and the Penguin Search Optimization Algorithm (PeSOA), and the optimal allocation of the tasks depends on the newly designed optimization algorithm. The proposed algorithm exhibits a better convergence rate and converges to the global optimal solution rather than the local optima. The formulation of the multiobjectives aims at a maximum value through attaining the maximum QoS and resource utilization and minimum load and makespan, respectively. The experimentation is performed using three setups, and the analysis proves that the method attained a better QoS, makespan, Resource Utilization Cost (RUC), and load at a rate of 0.4729, 0.0432, 0.0394, and 0.0298, respectively.
Year
DOI
Venue
2020
10.1002/dac.4467
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
Keywords
DocType
Volume
cloud computing, crow search optimization algorithm, multiobjective functions, penguin search optimization algorithm, task scheduling
Journal
33
Issue
ISSN
Citations 
14
1074-5351
1
PageRank 
References 
Authors
0.35
0
3
Name
Order
Citations
PageRank
Harvinder Singh148245.45
Sanjay Tyagi210.35
Pardeep Kumar324324.38