Task Scheduling in Cloud Computing Using Spotted Hyena Optimizer

Task Scheduling in Cloud Computing Using Spotted Hyena Optimizer

Amandeep Kaur, Gaurav Dhiman, Meenakshi Garg
Copyright: © 2021 |Pages: 14
DOI: 10.4018/978-1-7998-5040-3.ch009
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Abstract

Cloud computing provides internet users with quick and efficient tools to access and share the data. One of the most important research problems that need to be addressed is the effective performance of cloud-based task scheduling. Different cloud-based task scheduling algorithms based on metaheuristic optimization techniques like genetic algorithm (GA) and particle swarm optimization (PSO) scheduling algorithms are demonstrated and analyzed. In this chapter, cloud computing based on the spotted hyena optimizer (SHO) is proposed with a novel task scheduling technique. SHO algorithm is population-based and inspired by nature's spotted hyenas to achieve global optimization over a given search space. The findings show that the suggested solution performs better than other competitor algorithms.
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Spotted Hyena Optimizer (Sho) Based Task Scheduling

In SHO algorithm, there are four important steps, that are stimulated by natural behaviours of the spotted hyena. The behaviours like hunting prey, searching prey, encircling prey, and attacking prey are as following. i

Encircling Prey

Encircling the prey is also called a target prey. In this the search agents will change their locations or positions according to the optimal solution. It is represented as follows:

978-1-7998-5040-3.ch009.m01
(1)
978-1-7998-5040-3.ch009.m02
(2) where Dm is the distance vector in between the prey and the spotted hyena, y is the present iteration, Pp signifies the prey position vector, whereas P signifies the spotted hyena position vector, and B and E are the coefficient vector.

978-1-7998-5040-3.ch009.m03
(3)
978-1-7998-5040-3.ch009.m04
(4)
978-1-7998-5040-3.ch009.m05
(5)

Hunting

SHO's next move is hunting the prey strategy. For this, it make a cluster of optimal solutions in contrast to the finest search agent and updates other search agents i' positions. In this process the following equations will be described:

978-1-7998-5040-3.ch009.m06
(6)
978-1-7998-5040-3.ch009.m07
(7)
978-1-7998-5040-3.ch009.m08
(8) where Pm determines the first best spotted hyena location, Pk is the position of other spotted Ihyenas and N is the number of spotted hyenas are as following:
978-1-7998-5040-3.ch009.m09
(9) where M is a random variable in the range i[ i0.5, i1], ins is the number of solutions, and cm is the number of optimal solutions in category N.

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