OSSA Scheduler: Opposition-Based Learning Salp Swarm Algorithm for Task Scheduling in Cloud Computing

In cloud computing, task scheduling has a direct influence on service quality. Task scheduling means allocating tasks to available resources based on user specifications. This NP-hard problem seeks to develop an optimal scheduler for resource allocation to complete tasks in the shortest amount of ti...

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Главные авторы:	Lapina, M. A., Лапина, М. А.
Формат:	Статья
Язык:	English
Опубликовано:	Springer Science and Business Media Deutschland GmbH 2024
Темы:	Cloud computing Task scheduling Metaheuristics Opposition-Based Learning Salp swarm algorithm
Online-ссылка:	https://dspace.ncfu.ru/handle/123456789/29248
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id	ir-123456789-29248
record_format	dspace
spelling	ir-123456789-292482024-11-27T11:39:39Z OSSA Scheduler: Opposition-Based Learning Salp Swarm Algorithm for Task Scheduling in Cloud Computing Lapina, M. A. Лапина, М. А. Cloud computing Task scheduling Metaheuristics Opposition-Based Learning Salp swarm algorithm In cloud computing, task scheduling has a direct influence on service quality. Task scheduling means allocating tasks to available resources based on user specifications. This NP-hard problem seeks to develop an optimal scheduler for resource allocation to complete tasks in the shortest amount of time achievable. Several methods have been presented to tackle the task scheduling issue. In this study, an Opposition-based Learning Salp Swarm Algorithm (OSSA) to address task scheduling issues. The initial population phase of the proposed OSSA scheduler for task scheduling in a cloud computing environment uses Opposition-Based Learning (OBL) to minimize execution time. OBL generates a diversified and high-quality initial population, improving the optimization process's overall performance. The paper compares the proposed OSSA algorithm to various metaheuristic algorithms, like the standard Salp Swarm Algorithm (SSA), Differential Evolution (DE) and Sine Cosine Algorithm (SCA). The results shows that the OSSA algorithm can solve the task scheduling problem more efficiently and achieve superior solutions for minimizing the makespan. 2024-11-27T11:39:00Z 2024-11-27T11:39:00Z 2024 Статья Qasim M., Sajid M., Lapina M. OSSA Scheduler: Opposition-Based Learning Salp Swarm Algorithm for Task Scheduling in Cloud Computing // Lecture Notes in Networks and Systems. - 2024. - 863 LNNS. - pp. 237 - 248. - DOI: 10.1007/978-3-031-72171-7_24 https://dspace.ncfu.ru/handle/123456789/29248 en Lecture Notes in Networks and Systems application/pdf Springer Science and Business Media Deutschland GmbH
institution	СКФУ
collection	Репозиторий
language	English
topic	Cloud computing Task scheduling Metaheuristics Opposition-Based Learning Salp swarm algorithm
spellingShingle	Cloud computing Task scheduling Metaheuristics Opposition-Based Learning Salp swarm algorithm Lapina, M. A. Лапина, М. А. OSSA Scheduler: Opposition-Based Learning Salp Swarm Algorithm for Task Scheduling in Cloud Computing
description	In cloud computing, task scheduling has a direct influence on service quality. Task scheduling means allocating tasks to available resources based on user specifications. This NP-hard problem seeks to develop an optimal scheduler for resource allocation to complete tasks in the shortest amount of time achievable. Several methods have been presented to tackle the task scheduling issue. In this study, an Opposition-based Learning Salp Swarm Algorithm (OSSA) to address task scheduling issues. The initial population phase of the proposed OSSA scheduler for task scheduling in a cloud computing environment uses Opposition-Based Learning (OBL) to minimize execution time. OBL generates a diversified and high-quality initial population, improving the optimization process's overall performance. The paper compares the proposed OSSA algorithm to various metaheuristic algorithms, like the standard Salp Swarm Algorithm (SSA), Differential Evolution (DE) and Sine Cosine Algorithm (SCA). The results shows that the OSSA algorithm can solve the task scheduling problem more efficiently and achieve superior solutions for minimizing the makespan.
format	Статья
author	Lapina, M. A. Лапина, М. А.
author_facet	Lapina, M. A. Лапина, М. А.
author_sort	Lapina, M. A.
title	OSSA Scheduler: Opposition-Based Learning Salp Swarm Algorithm for Task Scheduling in Cloud Computing
title_short	OSSA Scheduler: Opposition-Based Learning Salp Swarm Algorithm for Task Scheduling in Cloud Computing
title_full	OSSA Scheduler: Opposition-Based Learning Salp Swarm Algorithm for Task Scheduling in Cloud Computing
title_fullStr	OSSA Scheduler: Opposition-Based Learning Salp Swarm Algorithm for Task Scheduling in Cloud Computing
title_full_unstemmed	OSSA Scheduler: Opposition-Based Learning Salp Swarm Algorithm for Task Scheduling in Cloud Computing
title_sort	ossa scheduler: opposition-based learning salp swarm algorithm for task scheduling in cloud computing
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2024
url	https://dspace.ncfu.ru/handle/123456789/29248
work_keys_str_mv	AT lapinama ossascheduleroppositionbasedlearningsalpswarmalgorithmfortaskschedulingincloudcomputing AT lapinama ossascheduleroppositionbasedlearningsalpswarmalgorithmfortaskschedulingincloudcomputing
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OSSA Scheduler: Opposition-Based Learning Salp Swarm Algorithm for Task Scheduling in Cloud Computing

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