Multi‐Objective Workflow Scheduling in Cloud Using Archimedes Optimization Algorithm

ABSTRACT Cloud computing has changed the technology landscape for over a decade and led to an astounding growth in the number of applications it may be used for. Consequently, there has been a significant spike in the demand for improved algorithms to schedule workflows efficiently. These were mostl...

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Vydané v:Concurrency and computation Ročník 37; číslo 4-5
Hlavní autori: Kushwaha, Shweta, Shankar Singh, Ravi, Prajapati, Kanika
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken Wiley Subscription Services, Inc 28.02.2025
Predmet:
Algorithms
Archimedes optimization algorithm
Cloud computing
cost‐efficiency
Effectiveness
Energy consumption
Entrapment
Heuristic methods
local escaping operation
metaheuristics
Optimization
Optimization algorithms
optimization techniques
pareto‐optimality
Performance measurement
Resource utilization
Scheduling
Workflow
workflow scheduling
ISSN:1532-0626, 1532-0634
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Shrnutí:ABSTRACT Cloud computing has changed the technology landscape for over a decade and led to an astounding growth in the number of applications it may be used for. Consequently, there has been a significant spike in the demand for improved algorithms to schedule workflows efficiently. These were mostly concerned with heuristic, metaheuristic, and hybrid approaches to workflow scheduling that mostly suffer from the problem of local optima entrapment. Due to such heavy traffic on the cloud resources, there is still a need for less computationally complex approaches. In light of this, this article proposes a novel approach: a multi‐objective Modified Local Escaping Archimedes Optimization (MLEAO) algorithm for workflow scheduling. This strategy involves initialization of the population of Archimedes Optimization algorithm through the HEFT algorithm to provide an inclination towards the solutions with improved makespan while achieving a cost‐efficient workflow scheduling decision and avoiding the problem of local optima entrapment using a local escaping operation. To validate the efficacy of our approach, we conducted extensive experiments using scientific workflows as benchmarks. Through our investigations, we significantly improved makespan, cost, resource utilization, and energy consumption. Moreover, the effectiveness of our proposed approach is also verified by performance metrics such as hypervolume, s‐metric, and dominance relationships between the proposed and state‐of‐the‐art approaches.
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content type line 14
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.8393