Metaheuristic task scheduling algorithms for cloud computing environments

Summary Cloud computing has the advantage of providing flexibility, high‐performance, pay‐as‐you‐use, and on‐demand service. One of the important research issues in cloud computing is task scheduling. The purpose of scheduling is to assign tasks to available resources while providing optimization on...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Concurrency and computation Ročník 34; číslo 9
Hlavní autoři: Aktan, Merve Nur, Bulut, Hasan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc 25.04.2022
Témata:
Algorithms
Cloud computing
Completion time
differential evolution algorithm
Evolutionary algorithms
Evolutionary computation
genetic algorithm
Genetic algorithms
Heuristic methods
metaheuristic algorithms
Optimization
Scheduling
Simulated annealing
Task scheduling
Virtual environments
ISSN:1532-0626, 1532-0634
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Summary Cloud computing has the advantage of providing flexibility, high‐performance, pay‐as‐you‐use, and on‐demand service. One of the important research issues in cloud computing is task scheduling. The purpose of scheduling is to assign tasks to available resources while providing optimization on some objectives. Tasks have diversified characteristics, and resources are heterogeneous. These properties make task scheduling an NP‐complete problem. In this study, metaheuristic and hybrid metaheuristic algorithms are developed for task scheduling problems in cloud computing environments. We have developed genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA) based metaheuristic algorithms, which are also combined with a greedy approach (GR). In addition to this, we have developed hybrid metaheuristics algorithms, called DE‐SA and GA‐SA, which are also combined with a greedy approach. The proposed approaches are evaluated in terms of completion time and load balancing of virtual machines. In terms of average completion time, as the number of tasks increases, it has been observed that the DESA algorithm outperforms the solely used DE and SA algorithms. In addition, experiments show that hybrid algorithms improve both the average completion time and the average standard deviation of virtual machine loads for some task groups.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.6513