A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

Jafar Ababneh,Mauro Gaggero

doi:10.1155/2021/3517145

Abstract

In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance.

Highlights

Among the most well-known uses of metaheuristics for optimization purposes is the scheduling of tasks allocated on sequential or parallel processes
Drawing on the hunting behavior displayed by wild grey wolves (Canis lupus), Mirjalili and colleagues developed the grey wolf optimization (GWO) algorithm in 2014 as a method of effectively solving complicated problems based on population agents where living in packs and hierarchical organization comprising several ranks (e.g., α, β, δ, and ω) are some of the characteristics of grey wolves [11]; the hierarchical organization of grey wolves consists of four tiers, with the first tier representing the alpha, decisionmaker, and leader, the second tier representing the beta, which supports decision-making and is subordinate to alpha, the third tier representing the delta, which is subordinate, and the fourth tier representing the omega, which is subordinate
Such algorithms include particle swarm optimization (PSO) [36], simulated annealing (SA) [37], genetic algorithms (GA) [38], ant colony optimization (AC) [39], grey wolf optimization (GWO) algorithm [11], whale optimization algorithm (WOA) [12] and hybrid methods; here are some of the recent studies arranged chronically: In [40], Singh and Bansal proposed a new grey wolf optimizer compound with crossover and oppositionbased learning named GWO-XOBL to overcome inadequate variety of wolves prone which led to local optima, and at the end, GWO performance will decrease, the new algorithm evaluated using 13 well-known standard benchmark problems to give expressively performance enhancement compared to GWO and other algorithms

Summary

Introduction

Among the most well-known uses of metaheuristics for optimization purposes is the scheduling of tasks allocated on sequential or parallel processes. From the latest metaheuristic algorithms for addressing optimization problems by identifying global optimal solutions are the grey wolf optimization (GWO) algorithm and the whale optimization algorithm (WOA), which have been respectively introduced in 2014 [11] and 2016 [12] and are inspired by the behavior of grey wolves and whales. E problem of cloud task scheduling can be addressed by integrating GWO and WOA in a hybrid approach entitled hybrid grey wolf and whale optimization (HGWWO) algorithm. In [16], the latest approaches for integrating metaheuristics and precise algorithms were explored, while in [17], a novel hybrid approach was proposed, drawing on krill herd and cuckoo search tactics for global optimization tasks. It is proposed that the problem of task scheduling can be solved by implementing a hybrid approach (HGWWO) that integrates GWO and WOA.

Background

Literature Review

Mathematical Model

Results and Discussion

Conclusion