research-article

FaaSLight: General Application-level Cold-start Latency Optimization for Function-as-a-Service in Serverless Computing

Authors:
Xuanzhe Liu

Peking University, China

Peking University, China

0000-0002-7908-8484
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,
Jinfeng Wen

Peking University, China

Peking University, China

0000-0003-3023-1005
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,
Zhenpeng Chen

University College London, United Kingdom

University College London, United Kingdom

0000-0002-4765-1893
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,
Ding Li

Peking University, China

Peking University, China

0000-0001-7558-9137
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,
Junkai Chen

Peking University, China

Peking University, China

0000-0002-9981-8616
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,
Yi Liu

Advanced Institute of Big Data, Beijing, China

Advanced Institute of Big Data, Beijing, China

0000-0002-1766-2444
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,
Haoyu Wang

Huazhong University of Science and Technology, China

Huazhong University of Science and Technology, China

0000-0003-1100-8633
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,
Xin Jin

Peking University, China

Peking University, China

0000-0001-8741-5847
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ACM Transactions on Software Engineering and Methodology Volume 32 Issue 5Article No.: 119pp 1–29https://doi.org/10.1145/3585007

Published:22 July 2023Publication History

ACM Transactions on Software Engineering and Methodology

Abstract

Serverless computing is a popular cloud computing paradigm that frees developers from server management. Function-as-a-Service (FaaS) is the most popular implementation of serverless computing, representing applications as event-driven and stateless functions. However, existing studies report that functions of FaaS applications severely suffer from cold-start latency.

In this article, we propose an approach, namely, FaaSLight, to accelerating the cold start for FaaS applications through application-level optimization. We first conduct a measurement study to investigate the possible root cause of the cold-start problem of FaaS. The result shows that application code loading latency is a significant overhead. Therefore, loading only indispensable code from FaaS applications can be an adequate solution. Based on this insight, we identify code related to application functionalities by constructing the function-level call graph and separate other code (i.e., optional code) from FaaS applications. The separated optional code can be loaded on demand to avoid the inaccurate identification of indispensable code causing application failure. In particular, a key principle guiding the design of FaaSLight is inherently general, i.e., platform- and language-agnostic. In practice, FaaSLight can be effectively applied to FaaS applications developed in different programming languages (Python and JavaScript), and can be seamlessly deployed on popular serverless platforms such as AWS Lambda and Google Cloud Functions, without having to modify the underlying OSes or hypervisors, nor introducing any additional manual engineering efforts to developers. The evaluation results on real-world FaaS applications show that FaaSLight can significantly reduce the code loading latency (up to 78.95%, 28.78% on average), thereby reducing the cold-start latency. As a result, the total response latency of functions can be decreased by up to 42.05% (19.21% on average). Compared with the state-of-the-art, FaaSLight achieves a 21.25× improvement in reducing the average total response latency.

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Index Terms

FaaSLight: General Application-level Cold-start Latency Optimization for Function-as-a-Service in Serverless Computing
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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In recent years, serverless computing has emerged as a new cloud service model that provides an elegant computing framework for application development. It allows developers to focus solely on application logic without having to worry about the ...
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Holistic cold-start management in serverless computing cloud with deep learning for time series
Abstract
Serverless computing is the most innovative and affordable cloud service model that improves three strategic business objectives: flexibility, efficiency, and innovation. Compared to monolithic applications, serverless computing applications ...
Highlights
- The complex cold-start problem involves many layers of the serverless cloud stack.
- Temporal Convolution Network models offer 5- to 15-minute forecast windows.
- Forecast windows enable a more holistic cold-start management policy.
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Published in
ACM Transactions on Software Engineering and Methodology Volume 32, Issue 5
September 2023
905 pages
ISSN:1049-331X
EISSN:1557-7392
DOI:10.1145/3610417
Editor:
Mauro Pezzè
USI Università della Svizzera italiana and SIT Schaffhausen Institute of Technology, Switzerland
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
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Association for Computing Machinery
New York, NY, United States
Publication History
- Published: 22 July 2023
- Online AM: 22 February 2023
- Accepted: 24 January 2023
- Revised: 3 December 2022
- Received: 16 May 2022
Published in tosem Volume 32, Issue 5

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Author Tags
Serverless computing
cold start
performance optimization
optional function elimination
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FaaSLight: General Application-level Cold-start Latency Optimization for Function-as-a-Service in Serverless Computing

ACM Transactions on Software Engineering and Methodology

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Holistic cold-start management in serverless computing cloud with deep learning for time series