An efficient load balancing technique using CAViaR-HHO enabled VM migration and replica management in cloud computing

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Web Intelligence Pub Date : 2023-01-13 DOI:10.3233/web-220081
Shelly Shiju George, R. Pramila
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Abstract

Cloud computing is immense technology that offers distributed resources to a number of users who are present throughout the world. Cloud model is comprised of numerous virtual machines (VMs) and physical machines (PMs) to carry out user tasks effectively in a parallel manner but in some cases, the demand of the users may be high that resulting in the overloading of PMs and this condition deteriorates the performance of cloud network. For achieving effective virtualization in the cloud paradigm, energy and resource utilization are major properties that should be handled effectively and such properties are accomplished through effective management of workload by distributing load equivalently among VMs. By doing so, resource utilization of the network is enhanced and it only requires minimum energy to process the tasks. Numerous load-balancing algorithms have been introduced earlier to maintain load in a cloud environment, nevertheless, they are devoid of mitigating the number of task migrations. Hence, this research proposes an effective load balancing algorithm and replica management method using the proposed Conditional Autoregressive Value at risk by Regression Quantiles-Horse Herd Optimization (CAViaR-HHO) model. Here, the load is computed by considering some factors like Central Processing Unit (CPU), Million Instructions per Second (MIPS), bandwidth, memory, and frequency. VM migration and replica migration is effectively carried out using the proposed CAViaR-HHO model. Meanwhile, the developed method is devised by integration of Conditional Autoregressive Value at risk by Regression Quantiles (CAViaR) with Horse Herd Optimization Algorithm (HOA). However, the proposed CAViaR-HHO has achieved a load with a minimum value of 0.109, capacity with a maximum value of 0.591, resource utilization with a maximum value of 0.467, and minimum cost of 0.344. Using setup-1, when the number of tasks is 500, the capacity of the proposed method is 5.58%, 3.89%, 2.87%, 1.52%, and 0.67% higher when compared to the existing approaches namely, C-FDLA, K-means clustering + LB, Adaptive starvation threshold, EIMORM, and Dynamic replica creation method.
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使用CAViaR-HHO的高效负载平衡技术支持云计算中的VM迁移和副本管理
云计算是一项巨大的技术,它为世界各地的许多用户提供分布式资源。云模型由大量的虚拟机和物理机组成,以并行的方式有效地执行用户任务,但在某些情况下,用户的需求可能很高,导致物理机过载,从而降低云网络的性能。为了在云范式中实现有效的虚拟化,能源和资源利用率是应该有效处理的主要属性,这些属性是通过在vm之间等效地分配负载来有效地管理工作负载来实现的。通过这样做,提高了网络的资源利用率,并且只需要最少的能量来处理任务。之前已经引入了许多负载平衡算法来维护云环境中的负载,然而,它们无法减少任务迁移的数量。因此,本研究提出了一种有效的负载平衡算法和副本管理方法,该方法采用了基于回归分位数-马群优化(CAViaR-HHO)模型的条件自回归风险值。在这里,负载是通过考虑中央处理单元(CPU)、每秒百万指令(MIPS)、带宽、内存和频率等因素来计算的。提出的CAViaR-HHO模型有效地实现了虚拟机迁移和副本迁移。同时,将回归分位数风险条件自回归值(CAViaR)与马群优化算法(HOA)相结合,设计了该方法。而本文提出的CAViaR-HHO实现了最小负荷0.109,容量最大值0.591,资源利用率最大值0.467,成本最小0.344。以set -1为例,当任务数为500时,与C-FDLA、K-means聚类+ LB、Adaptive hunger threshold、EIMORM和动态副本创建方法相比,所提出方法的容量分别提高了5.58%、3.89%、2.87%、1.52%和0.67%。
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来源期刊
Web Intelligence
Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
0.90
自引率
0.00%
发文量
35
期刊介绍: Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]
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