A Bio-inspired Energy Efficient Dynamic Task Scheduling (BEDTS) scheme and classification for virtualization CDC

IF 0.9 4区 工程技术 Q3 ENGINEERING, MULTIDISCIPLINARY Journal of Engineering Research Pub Date : 2024-09-01 DOI:10.1016/j.jer.2023.08.026
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Abstract

Cloud Data Centers (CDCs) have evolved into an essential computing infrastructure for businesses. These applications generate a variety of data traffic with varying needs that must be examined and processed on CDCs. Every CDC is made up of multiple servers, virtual infrastructures, and physical connections that manage the internet's informative traffic. CDCs make use of a number of critical technologies, such as virtualization and Service Level Agreements (SLA). Virtualization makes it easier to share cloud computing resources (for example, by separating a powerful Physical Machine (PM) into a series of Virtual Machines (VM)) whose power is comparatively less. Even though virtualization increases the use of PMs by establishing a set of VMs for offering customised services to satisfy the needs of end-users, it also introduces another difficulty to cloud computing: map the VMs to the appropriate PMs. This is referred to as the VM deployment problem, and it is a Nondeterministic Polynomial time problem (NP-problem). CDC task scheduling is appropriate to augment the energy efficiency and resource usage in cloud computing. In the case of real-time tasks in virtualized CDC, the Bio-Inspired Energy Efficient Dynamic Task Scheduling (BEDTS) method is proposed. The Adaptive Elephant Herding Optimization (AEHO) technique is introduced in the BEDTS algorithm for optimal selection of VMs with resource restrictions and jobs. Initially, heterogeneous jobs and VMs are identified using a previous scheduling record. The Bayes classifier and Historical Scheduling Record (HSR), which allow for the identification of both the task type and VM type, serve as the foundation for task categorization. Then, related tasks are combined and then scheduled to make the best use of the host's operational status. When compared to previous approaches, experimental results reveal that BEDTS considerably enhances the scheduling performance on the whole, attains improved CDC resource utilisation, boosts task guarantee ratio, lowers average response time, and decreases the energy usage.
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用于虚拟化 CDC 的生物启发节能动态任务调度(BEDTS)方案和分类
云数据中心(CDC)已发展成为企业必不可少的计算基础设施。这些应用产生的各种数据流量需求各不相同,必须在 CDC 上进行检查和处理。每个云计算中心都由多台服务器、虚拟基础设施和物理连接组成,用于管理互联网的信息流量。数据中心采用了许多关键技术,如虚拟化和服务水平协议(SLA)。虚拟化使云计算资源共享变得更加容易(例如,通过将功能强大的物理机(PM)分离成一系列功能相对较弱的虚拟机(VM))。尽管虚拟化通过建立一组虚拟机来提供定制服务以满足最终用户的需求,从而提高了 PM 的使用率,但它也给云计算带来了另一个难题:将虚拟机映射到相应的 PM 上。这被称为虚拟机部署问题,是一个非确定性多项式时间问题(NP 问题)。CDC 任务调度适用于提高云计算的能效和资源利用率。针对虚拟 CDC 中的实时任务,提出了生物启发高能效动态任务调度(BEDTS)方法。BEDTS 算法中引入了自适应大象群优化(AEHO)技术,用于优化选择有资源限制的虚拟机和作业。首先,利用以前的调度记录识别异构作业和虚拟机。贝叶斯分类器和历史调度记录(HSR)可识别任务类型和虚拟机类型,是任务分类的基础。然后,对相关任务进行组合和调度,以充分利用主机的运行状态。与之前的方法相比,实验结果表明,BEDTS 从整体上大大提高了调度性能,改善了 CDC 资源利用率,提高了任务保证率,降低了平均响应时间,并减少了能源消耗。
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来源期刊
Journal of Engineering Research
Journal of Engineering Research ENGINEERING, MULTIDISCIPLINARY-
CiteScore
1.60
自引率
10.00%
发文量
181
审稿时长
20 weeks
期刊介绍: Journal of Engineering Research (JER) is a international, peer reviewed journal which publishes full length original research papers, reviews, case studies related to all areas of Engineering such as: Civil, Mechanical, Industrial, Electrical, Computer, Chemical, Petroleum, Aerospace, Architectural, Biomedical, Coastal, Environmental, Marine & Ocean, Metallurgical & Materials, software, Surveying, Systems and Manufacturing Engineering. In particular, JER focuses on innovative approaches and methods that contribute to solving the environmental and manufacturing problems, which exist primarily in the Arabian Gulf region and the Middle East countries. Kuwait University used to publish the Journal "Kuwait Journal of Science and Engineering" (ISSN: 1024-8684), which included Science and Engineering articles since 1974. In 2011 the decision was taken to split KJSE into two independent Journals - "Journal of Engineering Research "(JER) and "Kuwait Journal of Science" (KJS).
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