An empirical investigation of task scheduling and VM consolidation schemes in cloud environment

Computer Science Review, Год журнала: 2023, Номер 50, С. 100583 - 100583

Опубликована: Сен. 1, 2023

Язык: Английский

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GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing

IEEE Access, Год журнала: 2023, Номер 11, С. 128537 - 128548

Опубликована: Янв. 1, 2023

Resource sharing, selection, and aggregation are vital functions of grid computing. However, managing resources in a grid-based environment is stimulating task. It necessary to update the topographical dispersal possessed by various organisations with usage guidelines, financial frameworks, load, availability patterns. Users servers have different objectives, methods, needs. This article suggests cost-effective framework for resource management computing look at address these difficulties. The proposed has three main functions, which help construction, load balancing, allocation. A Genetic engineering approach been implemented establish relationship between pool jobs nodes that improve utilization. methodology also optimizes overall cost minimizing turnaround time. results research compared commonly used algorithms claiming 1.5 10% better results.

Язык: Английский

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GCRS – Green Computing Resource Scheduler: An Optimized Energy Efficient Cloud Data Centers to Scale Down Carbon Emission

Опубликована: Окт. 18, 2023

Cloud computing is a powerful technology that rapidly growing in popularity with energy consumption being major concern. data centers consume lot of energy, which can lead to high operating costs and carbon emissions. This significant bottleneck restricting the development cloud computing. If people continue rely on traditional centers, environmental impact will only get worse. Green solutions are needed address this challenge. designed utilize resources while minimizing efficiently. providers offer different levels service, defined service-level agreements (SLAs). SLAs typically include things like availability response time for requests, assigned clients. try best exploit their by allocating them users enhances utilization lowers idle time. be done using scheduling algorithms. In proposed work" or servers maintain clients read write. Servers classified as read-and-write separately based capacity, tasks accordingly. The model uses every server's full potential, resulting an environment consumes less energy. objective Computing Resource Scheduling (GCRS) reduce consumed emitted center through resources.

Язык: Английский

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An Improved Glowworm Swarm Optimization Based on Various Mutation Operators

IEEE Access, Год журнала: 2024, Номер 12, С. 106359 - 106384

Опубликована: Янв. 1, 2024

Glowworm Swarm Optimization (GSO) is a population-based optimization algorithm that successfully solves numerous problems. Nonetheless, the convergence speed required to reach optimal solutions can be made more efficient by skipping local optima. Also, considerable attention tuning parameter of crucial improve speed. In this study, three variants GSO are proposed using various mutation operators (Gaussian, Cauchy, and Lévy) its prevent it from getting stuck in optimum. The small random changes provided Gaussian help fine-tuning position Glowworms. Meanwhile, Cauchy offers large assist movement operator exploring wide area search space. Lévy characterized occasional jumps, which have potential explore problem space effectively. performance accuracy methods studied based on famous multimodal unimodal benchmark test functions, as well CEC2014 suite. Additionally, we experimented with set engineering effects settings improved discussed Response Surface Methodology (RSM). Results revealed suggested algorithms, offer better than basic other variants. comparison state-of-the-art GGSO obtained best results for 68.75%, 63.33% functions CEC2014, respectively. statistical tests show superiority over modified algorithms.

Язык: Английский

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A tri-chromosome-based evolutionary algorithm for energy-efficient workflow scheduling in clouds

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 91, С. 101751 - 101751

Опубликована: Окт. 21, 2024

Язык: Английский

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