DE-RALBA: dynamic enhanced resource aware load balancing algorithm for cloud computing.

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
PeerJ Computer Science Pub Date : 2025-03-18 eCollection Date: 2025-01-01 DOI:10.7717/peerj-cs.2739
Altaf Hussain, Muhammad Aleem, Atiq Ur Rehman, Umer Arshad
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引用次数: 0

Abstract

Cloud computing provides an opportunity to gain access to the large-scale and high-speed resources without establishing your own computing infrastructure for executing the high-performance computing (HPC) applications. Cloud has the computing resources (i.e., computation power, storage, operating system, network, and database etc.) as a public utility and provides services to the end users on a pay-as-you-go model. From past several years, the efficient utilization of resources on a compute cloud has become a prime interest for the scientific community. One of the key reasons behind inefficient resource utilization is the imbalance distribution of workload while executing the HPC applications in a heterogenous computing environment. The static scheduling technique usually produces lower resource utilization and higher makespan, while the dynamic scheduling achieves better resource utilization and load-balancing by incorporating a dynamic resource pool. The dynamic techniques lead to increased overhead by requiring a continuous system monitoring, job requirement assessments and real-time allocation decisions. This additional load has the potential to impact the performance and responsiveness on computing system. In this article, a dynamic enhanced resource-aware load balancing algorithm (DE-RALBA) is proposed to mitigate the load-imbalance in job scheduling by considering the computing capabilities of all VMs in cloud computing. The empirical assessments are performed on CloudSim simulator using instances of two scientific benchmark datasets (i.e., heterogeneous computing scheduling problems (HCSP) instances and Google Cloud Jobs (GoCJ) dataset). The obtained results revealed that the DE-RALBA mitigates the load imbalance and provides a significant improvement in terms of makespan and resource utilization against existing algorithms, namely PSSLB, PSSELB, Dynamic MaxMin, and DRALBA. Using HCSP instances, the DE-RALBA algorithm achieves up to 52.35% improved resources utilization as compared to existing technique, while more superior resource utilization is achieved using the GoCJ dataset.

DE-RALBA:用于云计算的动态增强的资源感知负载均衡算法。
云计算提供了访问大规模和高速资源的机会,而无需为执行高性能计算(HPC)应用程序建立自己的计算基础设施。云将计算资源(即计算能力、存储、操作系统、网络和数据库等)作为公用事业,并以现收现付模式向最终用户提供服务。在过去的几年里,高效利用计算云上的资源已经成为科学界的主要兴趣。资源利用效率低下的一个关键原因是在异构计算环境中执行HPC应用程序时工作负载分布不平衡。静态调度技术通常产生较低的资源利用率和较高的makespan,而动态调度技术通过合并动态资源池实现更好的资源利用率和负载平衡。动态技术需要持续的系统监控、工作需求评估和实时分配决策,从而导致开销增加。这种额外的负载有可能影响计算系统的性能和响应性。本文通过考虑云计算中所有虚拟机的计算能力,提出了一种动态增强的资源感知负载平衡算法DE-RALBA,以缓解作业调度中的负载不平衡问题。在CloudSim模拟器上使用两个科学基准数据集(即异构计算调度问题(HCSP)实例和谷歌云作业(GoCJ)数据集)的实例进行实证评估。得到的结果表明,DE-RALBA减轻了负载不平衡,并在makespan和资源利用率方面提供了显着改善,相对于现有的算法,即PSSLB, PSSELB, Dynamic MaxMin和DRALBA。使用HCSP实例,DE-RALBA算法的资源利用率比现有技术提高了52.35%,而使用GoCJ数据集实现了更高的资源利用率。
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
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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