Multiple objectives dynamic VM placement for application service availability in cloud networks

Yanal Alahmad, Anjali Agarwal
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Abstract

Ensuring application service availability is a critical aspect of delivering quality cloud computing services. However, placing virtual machines (VMs) on computing servers to provision these services can present significant challenges, particularly in terms of meeting the requirements of application service providers. In this paper, we present a framework that addresses the NP-hard dynamic VM placement problem in order to optimize application availability in cloud computing paradigm. The problem is modeled as an integer nonlinear programming (INLP) optimization with multiple objectives and constraints. The framework comprises three major modules that use optimization methods and algorithms to determine the most effective VM placement strategy in cases of application deployment, failure, and scaling. Our primary goals are to minimize power consumption, resource waste, and server failures while also ensuring that application availability requirements are met. We compare our proposed heuristic VM placement solution with three related algorithms from the literature and find that it outperforms them in several key areas. Our solution is able to admit more applications, reduce power consumption, and increase CPU and RAM utilization of the servers. Moreover, we use a deep learning method that has high accuracy and low error loss to predict application task failures, allowing for proactive protection actions to reduce service outage. Overall, our framework provides a comprehensive solution by optimizing dynamic VM placement. Therefore, the framework can improve the quality of cloud computing services and enhance the experience for users.
云网络中应用服务可用性的多目标动态虚拟机安置
确保应用服务可用性是提供优质云计算服务的一个重要方面。然而,在计算服务器上放置虚拟机(VM)以提供这些服务会带来巨大挑战,尤其是在满足应用服务提供商的要求方面。在本文中,我们提出了一个框架来解决 NP 难度的动态虚拟机放置问题,以优化云计算范例中的应用可用性。该问题被建模为具有多个目标和约束条件的整数非线性编程(INLP)优化。该框架由三个主要模块组成,使用优化方法和算法来确定应用部署、故障和扩展情况下最有效的虚拟机放置策略。我们的主要目标是最大限度地减少能耗、资源浪费和服务器故障,同时确保满足应用程序的可用性要求。我们将所提出的启发式虚拟机放置解决方案与文献中的三种相关算法进行了比较,发现它在几个关键方面优于它们。我们的解决方案能够接纳更多应用,降低功耗,提高服务器的 CPU 和 RAM 利用率。此外,我们还使用了一种深度学习方法,该方法预测应用任务故障的准确性高、误差损失小,可采取主动保护措施,减少服务中断。总之,我们的框架通过优化动态虚拟机放置提供了一个全面的解决方案。因此,该框架可以提高云计算服务的质量,增强用户体验。
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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