A Cost-Effective Hybrid Cloud Resource Scaling Framework for Batch Processing Services

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-11-19 DOI:10.1109/TNSE.2024.3502503

Qinzhi Zhang;Li Pan;Shijun Liu

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引用次数: 0

Abstract

Batch processing services, like offline video processing, are pivotal in modern data analysis. Software as a Service (SaaS) providers typically purchase virtual machines (VMs) or Function as a Service (FaaS) instances, also known as function instances, from cloud providers to provision computational resources for their services. VMs offer stable performance and cost-effectiveness for continuous workloads but may incur resource waste due to idleness. Conversely, function instances, with rapid auto-scaling and fine-grained billing, excel in handling discrete workloads, albeit at a higher unit price. SaaS providers can leverage the advantages of both VMs and function instances, to achieve cost-effective service delivery while ensuring overall performance. However, due to the complexity and unpredictability of batch processing service workloads, achieving this goal is challenging. To address these issues, in this paper we propose a proximal policy optimization (PPO) based hybrid resource scaling algorithm and design a hybrid resource scaling framework. The proposed scaling framework considers the workload characteristics and performance requirements of batch processing services, adaptively making cost-optimal resource scaling decisions based on current workloads and configuration of computational resources, while ensuring the overall performance of the service. We conduct extensive simulation experiments on multiple workloads with different levels of discreteness extracted from Microsoft and Huawei datasets, and the results demonstrate that our framework can achieve optimal service cost while ensuring overall performance.

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批处理服务的低成本混合云资源扩展框架

批处理服务，如离线视频处理，是现代数据分析的关键。软件即服务（SaaS）提供商通常从云提供商处购买虚拟机（vm）或功能即服务（FaaS）实例（也称为功能实例），以便为其服务提供计算资源。对于持续的工作负载，虚拟机提供了稳定的性能和成本效益，但可能由于空闲而导致资源浪费。相反，具有快速自动伸缩和细粒度计费的功能实例在处理离散工作负载方面表现出色，尽管其单价较高。SaaS提供商可以利用虚拟机和功能实例的优势，在确保整体性能的同时实现经济高效的服务交付。然而，由于批处理服务工作负载的复杂性和不可预测性，实现这一目标具有挑战性。为了解决这些问题，本文提出了一种基于近端策略优化（PPO）的混合资源缩放算法，并设计了混合资源缩放框架。提出的扩展框架考虑了批处理服务的工作负载特征和性能需求，在保证服务整体性能的同时，根据当前工作负载和计算资源配置自适应地做出成本最优的资源扩展决策。我们对从微软和华为数据集中提取的不同离散程度的多个工作负载进行了广泛的仿真实验，结果表明我们的框架可以在保证整体性能的同时实现最优的服务成本。

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

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.