{"title":"A Bio-inspired Energy Efficient Dynamic Task Scheduling (BEDTS) scheme and classification for virtualization CDC","authors":"","doi":"10.1016/j.jer.2023.08.026","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48803,"journal":{"name":"Journal of Engineering Research","volume":"12 3","pages":"Pages 387-396"},"PeriodicalIF":0.9000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering Research","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2307187723002031","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
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.
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).