GSAGA: A hybrid algorithm for task scheduling in cloud infrastructure

Cloud computing is becoming a very popular form of distributed computing, in which digital resources are shared via the Internet. The user is provided with an overview of many available resources. Cloud providers want to get the most out of their resources, and users are inclined to pay less for bet...

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Veröffentlicht in:The Journal of supercomputing Jg. 78; H. 15; S. 17423 - 17449
Hauptverfasser: Pirozmand, Poria, Javadpour, Amir, Nazarian, Hamideh, Pinto, Pedro, Mirkamali, Seyedsaeid, Ja’fari, Forough
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.10.2022
Springer Nature B.V
Schlagworte:
Algorithms
Cloud computing
Compilers
Computer networks
Computer Science
Distributed processing
Genetic algorithms
Internet resources
Interpreters
Processor Architectures
Programming Languages
Random search method
Search algorithms
Task scheduling
Traveling salesman problem
Cloud computing
Gravitational search
Epigenomics
NP-hard problem
GSA
Genetic algorithm
SLA
Scheduling
Resources
ISSN:0920-8542, 1573-0484
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Zusammenfassung:Cloud computing is becoming a very popular form of distributed computing, in which digital resources are shared via the Internet. The user is provided with an overview of many available resources. Cloud providers want to get the most out of their resources, and users are inclined to pay less for better performance. Task scheduling is one of the most important aspects of cloud computing. In order to achieve high performance from cloud computing systems, tasks need to be scheduled for processing by appropriate computing resources. The large search space of this issue makes it an NP-hard problem, and more random search methods are required to solve this problem. Multiple solutions have been proposed with several algorithms to solve this problem until now. This paper presents a hybrid algorithm called GSAGA to solve the Task Scheduling Problem (TSP) in cloud computing. Although it has a high ability to search the problem space, the Genetic Algorithm (GA) performs poorly in terms of stability and local search. It is therefore possible to create a stable algorithm by combining the general search capacities of the GA with the Gravitational Search Algorithm (GSA). Our experimental results indicate that the proposed algorithm can solve the problem with higher efficiency compared with the state-of-the-art.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-022-04539-8