An empirical investigation of task scheduling and VM consolidation schemes in cloud environment

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Sweta Singh , Rakesh Kumar , Dayashankar Singh
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引用次数: 0

Abstract

Cloud computing has evolved as a new paradigm in Internet computing, offering services to the end-users and large-organizations, on-demand and pay-per-the-usage basis with high availability, elasticity, scalability and resiliency. In order to improve the performance of the Cloud system, handling multiple heterogeneous tasks concurrently, an appropriate task scheduler is required. To meet the user’s requirements in terms of Quality of Service (QoS) parameters, the task scheduling algorithm should identify the order in which tasks should be executed. Energy efficiency is the significant challenge in today’s task scheduling to meet the prerequisite for green computing. By increasing resource utilization at the data centers, virtual machine (VM) Consolidation is also recognized as the most widely used and promising approach in terms of energy consumption and system performance. However, excessive VM Consolidation could constitute a violation of the Service Level Agreement (SLA). The paper makes a contribution by outlining the numerous approaches that researchers have used thus far to achieve the goals of scheduling and VM Consolidation, assuring energy efficiency, and maintaining system performance. This would give readers a better understanding of the problems and the potential for improvement while assisting them in selecting the ideal scheduling algorithm with Consolidation technique. Additionally, the techniques are divided into three categories: those that primarily focus on task scheduling; those that target Consolidation; and complete computation, integrating task scheduling with VM Consolidation. Further classification for the scheduling algorithms include heuristic, meta-heuristic, greedy, and hybrid task scheduling algorithms. In addition to a summary of the benefits and drawbacks of the suggested algorithms, prospective research directions and recent developments in this area is also covered in this paper.

云环境下任务调度和虚拟机整合方案的实证研究
云计算已经发展成为互联网计算中的一种新范式,为最终用户和大型组织提供服务,按需和按使用付费,具有高可用性、弹性、可伸缩性和弹性。为了提高云系统的性能,并发处理多个异构任务,需要一个合适的任务调度器。为了满足用户对QoS (Quality of Service)参数的要求,任务调度算法需要确定任务执行的顺序。能源效率是当前任务调度面临的重大挑战,是实现绿色计算的前提。通过提高数据中心的资源利用率,虚拟机(VM)整合也被认为是在能耗和系统性能方面使用最广泛和最有前途的方法。但是,过多的VM整合可能会违反服务水平协议(SLA)。本文通过概述研究人员迄今为止使用的许多方法来实现调度和VM整合,确保能源效率和维护系统性能的目标,从而做出贡献。这将使读者更好地理解问题和改进的潜力,同时帮助他们选择理想的调度算法与整合技术。此外,这些技术分为三类:主要关注任务调度的技术;以整合为目标的;完成计算,将任务调度与VM整合在一起。调度算法的进一步分类包括启发式、元启发式、贪心和混合任务调度算法。除了总结所建议算法的优点和缺点外,本文还涵盖了该领域的前瞻性研究方向和最新发展。
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
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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