An Improved Grey Wolf Optimization Algorithm
Based Task Scheduling in Cloud
Computing Environment
Gobalakrishnan Natesan1 and Arun
Chokkalingam2
1Department
of Information Technology, Sathyabama University, India
2Department
of Electronics and Communication Engineering, R.M.K College of Engineering and
Technology, India
Abstract: The demand for massive computing
power and storage space has been escalating in various fields and in order to
satisfy this need a new technology known as cloud computing is introduced. The
capability of providing these services effectively and economically has made
cloud computing technology more popular. With the advent of virtualization, IT
services being offered have started to shift to cloud computing. Virtualization
had paved way for resource availability in an inexhaustible manner. As Cloud
Computing is still at its unrefined form and to derive its full potential more
analysis is needed. The way in which resources and tasks get allocated in cloud
environment requires more analysis. This in turn accounts for the Quality of
Services (QoS) of the services offered by cloud service providers. This paper
proposes to simulate the Performance-Cost Grey Wolf Optimization (PCGWO)
algorithm based to achieve optimization in the process of allocation of
resources and tasks in cloud computing domain using CloudSim toolkit. The main
purpose is to lower both the processing time and cost in accordance to
objective function. The superiority of proposed technique is evident from the
simulation results that show a comprehensive reduction in task completion time
and cost. Also using this technique more no. of tasks can be efficiently
completed within the deadline. Thus the results indicate that in accordance to
performance the PCGWO method fares better than existing algorithms.
Keywords: Virtualization,
cloud computing, GWO, task scheduling, optimization, resource, CloudSim and QoS.
Received July 8, 2017;
accepted September 13, 2017
https://doi.org/10.34028/iajit/17/1/9