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QoS-aware resource management in cloud computing based on fuzzy meta-heuristic method.

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Název: QoS-aware resource management in cloud computing based on fuzzy meta-heuristic method.
Autoři: Long, Guiling, Wang, Shaorong, Lv, Cong
Zdroj: Cluster Computing; Aug2025, Vol. 28 Issue 4, p1-35, 35p
Témata: MATHEMATICS software, COMPUTER software, FUZZY algorithms, EVOLUTIONARY algorithms, CLOUD computing, PARTICLE swarm optimization
Abstrakt: With great agility, availability, scalability, and resilience, cloud computing has recently become one of the most popular platforms for offering compute, storage, and analytics services to businesses and end users on a pay-per-use basis. This eliminates the need to set up a high-performance computing platform by giving people and organizations access to a vast pool of high processing resources. Task scheduling in cloud computing has become a highly valued resource for researchers in recent years. Nonetheless, task scheduling is generally considered an NP-hard problem. In this paper a hybrid algorithm IVPTS designed for dependable cloud computing task scheduling and VM placement. We address the optimization of task execution time and resource balance concurrently by integrating an improved particle swarm optimization algorithm along with fuzzy framework. We then conduct a new algorithm for VM placement problem, with using this strategy along with PSO-Fuzzy scheduling we can improve the cloud computing infrastructure. The results demonstrate that compared to the ELBA and ERA algorithm, IVPTS reduces makespan by 11% and enhances energy consumption by 15%. Additionally, compared to GWO and PSO algorithms, IVPTS achieves 13%, 5% reduction in makespan, and improves energy consumption by 12%, 5%. Moreover, the simulations indicate that the proposed method can improve the cloud environment's reliability. Furthermore, compared to other methods, IVPTS exhibits a better degree of imbalance and makespan results. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:With great agility, availability, scalability, and resilience, cloud computing has recently become one of the most popular platforms for offering compute, storage, and analytics services to businesses and end users on a pay-per-use basis. This eliminates the need to set up a high-performance computing platform by giving people and organizations access to a vast pool of high processing resources. Task scheduling in cloud computing has become a highly valued resource for researchers in recent years. Nonetheless, task scheduling is generally considered an NP-hard problem. In this paper a hybrid algorithm IVPTS designed for dependable cloud computing task scheduling and VM placement. We address the optimization of task execution time and resource balance concurrently by integrating an improved particle swarm optimization algorithm along with fuzzy framework. We then conduct a new algorithm for VM placement problem, with using this strategy along with PSO-Fuzzy scheduling we can improve the cloud computing infrastructure. The results demonstrate that compared to the ELBA and ERA algorithm, IVPTS reduces makespan by 11% and enhances energy consumption by 15%. Additionally, compared to GWO and PSO algorithms, IVPTS achieves 13%, 5% reduction in makespan, and improves energy consumption by 12%, 5%. Moreover, the simulations indicate that the proposed method can improve the cloud environment's reliability. Furthermore, compared to other methods, IVPTS exhibits a better degree of imbalance and makespan results. [ABSTRACT FROM AUTHOR]
ISSN:13867857
DOI:10.1007/s10586-024-05021-1