云中的QoS转型:创新资源调度,提升服务质量

IF 1.5 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
P. Tamilarasu, G. Singaravel, Premkumar Manoharan, Shitharth Selvarajan
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引用次数: 0

摘要

云计算(CC)已经成为一种变革性的技术,为客户提供了前所未有的访问广泛计算资源和托管各种应用程序的各种服务的能力。然而,这种环境也带来了一些挑战。虽然云用户寻求最优资源来满足他们的特定需求,但普遍的场景通常涉及用更多的货币资源换取更少的计算时间。现有的算法主要集中于优化单个变量,缺乏整体方法。要解决这些问题,就必须采取新的办法,把这些相互冲突的目标结合起来。本研究的重点是开发和改进一个动态任务处理框架,该框架可以实时发现和使用最优资源。重点扩展到使用多目标自适应粒子群优化(MAPSO)算法在虚拟机上运行不同类型和复杂程度的应用程序。MAPSO采用加权和方法处理多目标问题。系统在预定义的约束条件下运行,以满足用户的特定时间限制。通过对大范围数据集的综合模拟,提出的方法产生了一组非支配的最优解。这个结果有助于改进关键的服务质量(QoS)指标,包括处理时间、执行成本、吞吐量和任务拒绝率。基于mapso的方法在处理时间、执行成本、吞吐量和任务拒绝率等众多QoS方面的改进能力是显而易见的,并且清楚地表明它优于现有算法,如蚁群优化(ACO)、蝙蝠优化算法和粒子群优化(BOA+PSO)的混合版本,以及灰狼优化和人工蜂群(GWO+ABC)的混合算法。与ACO、BOA+PSO和GWO+ABC相比,MAPSO算法完成任务的时间复杂度降低了5%,每个调度任务的执行速度提高了5% ~ 13%,计算执行成本也降低了。此外,所建议的方法在计算性能方面令人信服地优于现有的最先进的方法。本研究通过在实时资源分配框架内集成多目标优化,开创了云服务提供的独特解决方案。智能资源分配和增强的QoS指标相结合的结果有望改变基于云的应用程序部署的方式。最终,这项工作建立了云计算环境中平衡资源分配和以用户为中心的QoS优化的范式转变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

QoS Transformation in the Cloud: Advancing Service Quality Through Innovative Resource Scheduling

QoS Transformation in the Cloud: Advancing Service Quality Through Innovative Resource Scheduling

Cloud computing (CC) has emerged as a transformative technology, offering customers unprecedented access to extensive computing resources and the diverse services for hosting various applications. However, this environment comes with several challenges. While cloud users seek optimal resources to cater to their specific requirements, the prevalent scenario often involves trading more monetary resources for less computational time. Existing algorithms, mostly focused on optimizing individual variables, lack a holistic approach. Addressing these issues necessitates a new approach to combine these conflicting objectives. This research focuses on developing and improving a dynamic task-processing framework that can find and use the optimal resources in real-time. The focus extends to running applications of different types and levels of complexity on virtual machines (VMs) using the multi-objective adaptive particle swarm optimization (MAPSO) algorithm. The MAPSO handles the multi-objective problem using the weighted-sum approach. The system operates within predefined constraints to meet users' specific time limitations. Through comprehensive simulations on a wide range of datasets, the proposed methodology yields a set of non-dominated optimal solutions. This outcome is instrumental in improving critical quality of service (QoS) metrics, including processing time, execution costs, throughput, and task rejection ratios. The effectiveness of the MAPSO-based approach are evident in its capacity to improve these numerous QoS aspects, including processing time, execution cost, throughput, and task rejection ratio compared and clearly shows that it is superior to the existing algorithms, such as ant colony optimization (ACO), hybrid version of bat optimization algorithm and particle swarm optimization (BOA+PSO), and hybrid grey wolf optimization and artificial bee colony (GWO+ABC). The time complexity for completing the tasks of the MAPSO algorithm is reduced by 5%, executes each schedule's tasks faster by 5% to 13%, and calculated execution costs also get reduced when compared to ACO, BOA+PSO, and GWO+ABC. Moreover, the suggested methodology convincingly outperforms existing state-of-the-art methods in terms of computational performance. This study pioneers a unique solution in cloud service provisioning by integrating multi-objective optimization within a real-time resource allocation framework. The resulting combination of intelligent resource allocation and enhanced QoS metrics promises to change the way cloud-based application deployment is done. Ultimately, this work establishes a paradigm shift in balancing resource allocation and user-centric QoS optimization in cloud computing environments.

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来源期刊
IET Communications
IET Communications 工程技术-工程:电子与电气
CiteScore
4.30
自引率
6.20%
发文量
220
审稿时长
5.9 months
期刊介绍: IET Communications covers the fundamental and generic research for a better understanding of communication technologies to harness the signals for better performing communication systems using various wired and/or wireless media. This Journal is particularly interested in research papers reporting novel solutions to the dominating problems of noise, interference, timing and errors for reduction systems deficiencies such as wasting scarce resources such as spectra, energy and bandwidth. Topics include, but are not limited to: Coding and Communication Theory; Modulation and Signal Design; Wired, Wireless and Optical Communication; Communication System Special Issues. Current Call for Papers: Cognitive and AI-enabled Wireless and Mobile - https://digital-library.theiet.org/files/IET_COM_CFP_CAWM.pdf UAV-Enabled Mobile Edge Computing - https://digital-library.theiet.org/files/IET_COM_CFP_UAV.pdf
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