Cloud Resource Scheduling Using Multi-Strategy Fused Honey Badger Algorithm.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2025-02-01 DOI:10.1089/big.2023.0146
Haitao Xie, Chengkai Li, Zhiwei Ye, Tao Zhao, Hui Xu, Jiangyi Du, Wanfang Bai
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引用次数: 0

Abstract

Cloud resource scheduling is one of the most significant tasks in the field of big data, which is a combinatorial optimization problem in essence. Scheduling strategies based on meta-heuristic algorithms (MAs) are often chosen to deal with this topic. However, MAs are prone to falling into local optima leading to decreasing quality of the allocation scheme. Algorithms with good global search ability are needed to map available cloud resources to the requirements of the task. Honey Badger Algorithm (HBA) is a newly proposed algorithm with strong search ability. In order to further improve scheduling performance, an Improved Honey Badger Algorithm (IHBA), which combines two local search strategies and a new fitness function, is proposed in this article. IHBA is compared with 6 MAs in four scale load tasks. The comparative simulation results obtained reveal that the proposed algorithm performs better than other algorithms involved in the article. IHBA enhances the diversity of algorithm populations, expands the individual's random search range, and prevents the algorithm from falling into local optima while effectively achieving resource load balancing.

基于多策略融合蜜獾算法的云资源调度。
云资源调度是大数据领域最重要的任务之一,本质上是一个组合优化问题。通常选择基于元启发式算法(MAs)的调度策略来处理该主题。然而,MAs容易陷入局部最优,导致分配方案的质量下降。需要具有良好全局搜索能力的算法将可用的云资源映射到任务的需求上。蜂蜜獾算法(Honey Badger Algorithm, HBA)是一种新提出的具有较强搜索能力的算法。为了进一步提高调度性能,本文提出了一种结合两种局部搜索策略和新的适应度函数的改进蜜獾算法(IHBA)。IHBA在4个规模负载任务中与6ma进行比较。对比仿真结果表明,该算法的性能优于本文所涉及的其他算法。IHBA增强了算法种群的多样性,扩大了个体的随机搜索范围,在有效实现资源负载均衡的同时防止算法陷入局部最优。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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