Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-10-17 DOI:10.1145/3700875
Muhammed GOLEC, GUNEET KAUR WALIA, MOHIT KUMAR, FELIX CUADRADO, Sukhpal Singh Gill, STEVE UHLIG
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Abstract

Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability and an economic model. In serverless computing, users only pay for the time they actually use resources, enabling zero scaling to optimise cost and resource utilisation. However, this approach also introduces the serverless cold start problem. Researchers have developed various solutions to address the cold start problem, yet it remains an unresolved research area. In this article, we propose a systematic literature review on clod start latency in serverless computing. Furthermore, we create a detailed taxonomy of approaches to cold start latency, which we use to investigate existing techniques for reducing the cold start time and frequency. We have classified the current studies on cold start latency into several categories such as caching and application-level optimisation-based solutions, as well as Artificial Intelligence (AI)/Machine Learning (ML)-based solutions. Moreover, we have analyzed the impact of cold start latency on quality of service, explored current cold start latency mitigation methods, datasets, and implementation platforms, and classified them into categories based on their common characteristics and features. Finally, we outline the open challenges and highlight the possible future directions.
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无服务器计算中的冷启动延迟:系统回顾、分类和未来方向
最近,学术界和企业界都开始关注无服务器计算,因为它可以实现动态可扩展性和经济模式。在无服务器计算中,用户只需为实际使用资源的时间付费,从而实现零扩展,优化成本和资源利用率。然而,这种方法也带来了无服务器冷启动问题。研究人员已经开发了各种解决方案来解决冷启动问题,但它仍然是一个尚未解决的研究领域。在本文中,我们对无服务器计算中的冷启动延迟进行了系统的文献综述。此外,我们还为解决冷启动延迟问题的方法创建了一个详细的分类法,并以此来研究现有的减少冷启动时间和频率的技术。我们将当前有关冷启动延迟的研究分为几类,如基于缓存和应用级优化的解决方案,以及基于人工智能(AI)/机器学习(ML)的解决方案。此外,我们还分析了冷启动延迟对服务质量的影响,探索了当前的冷启动延迟缓解方法、数据集和实施平台,并根据它们的共同特点和特征将其分类。最后,我们概述了有待解决的挑战,并强调了未来可能的发展方向。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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