CBWO:一种新颖的云计算多目标负载平衡技术

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Vahideh Hayyolalam, Öznur Özkasap
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引用次数: 0

摘要

在云计算系统中,多样化应用的需求不断增长,导致资源分配和工作负载分配面临挑战,从而增加了能源消耗和计算成本。为了应对这些挑战,我们提出了一种新颖的负载平衡方法,即 CBWO,它将混沌理论与黑寡妇优化算法相结合。我们的方法旨在通过提高能源效率和资源利用率来优化云计算环境。我们采用 CloudSim 进行仿真,评估能源消耗、资源利用率、时间跨度、任务完成时间和不平衡程度等关键性能指标。实验结果证明了我们的方法的优越性,与现有的解决方案相比,我们的方法平均提高了 67.28% 的时间跨度和 29.03% 的能耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CBWO: A Novel Multi-objective Load Balancing Technique for Cloud Computing
In cloud computing systems, the growing demand for diverse applications has led to challenges in resource allocation and workload distribution, resulting in increased energy consumption and computational costs. To address these challenges, we propose a novel load-balancing method, namely CBWO, that integrates Chaos theory with the Black Widow Optimization algorithm. Our approach is designed to optimize cloud computing environments by improving energy efficiency and resource utilization. We employ CloudSim for simulations, evaluating key performance metrics such as energy consumption, resource utilization, makespan, task completion time, and imbalance degree. The experimental results demonstrate the superiority of our method, achieving average improvements of 67.28% in makespan and 29.03% in energy consumption compared to existing solutions.
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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