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Proactive planning of bandwidth resource using simulation-based what-if predictions for Web services in the cloud

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Abstract

Resource planning is becoming an increasingly important and timely problem for cloud users. As more Web services are moved to the cloud, minimizing network usage is often a key driver of cost control. Most existing approaches focus on resources such as CPU, memory, and disk I/O. In particular, CPU receives the most attention from researchers, but the bandwidth is somehow neglected. It is challenging to predict the network throughput of modern Web services, due to the factors of diverse and complex response, evolving Web services, and complex network transportation. In this paper, we propose a methodology of what-if analysis, named Log2Sim, to plan the bandwidth resource of Web services. Log2Sim uses a lightweight workload model to describe user behavior, an automated mining approach to obtain characteristics of workloads and responses from massive Web logs, and traffic-aware simulations to predict the impact on the bandwidth consumption and the response time in changing contexts. We use a real-life Web system and a classic benchmark to evaluate Log2Sim in multiple scenarios. The evaluation result shows that Log2Sim has good performance in the prediction of bandwidth consumption. The average relative error is 2% for the benchmark and 8% for the real-life system. As for the response time, Log2Sim cannot produce accurate predictions for every single service request, but the simulation results always show similar trends on average response time with the increase of workloads in different changing contexts. It can provide sufficient information for the system administrator in proactive bandwidth planning.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2018YFB1003302) and the National Natural Science Foundation of China (Grant No. 61472241).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jianpeng Hu, Linpeng Huang & Hao Zhong

  2. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Jianpeng Hu, Tianqi Sun, Ying Fan & Wenqiang Hu

Authors
  1. Jianpeng Hu
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  2. Linpeng Huang
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  3. Tianqi Sun
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  4. Ying Fan
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  5. Wenqiang Hu
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  6. Hao Zhong
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Corresponding author

Correspondence to Linpeng Huang.

Additional information

Jianpeng Hu received his BS and MS degrees from East China University of Science and Technology, Donghua University, China in 2003 and 2006, respectively. He is an associate professor of computer science in the School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, China. He has just received his PhD degree in computer science from Shanghai Jiao Tong University, China. His research interests include software engineering, modeling and simulation, software architecture, and system of systems.

Linpeng Huang received his MS and a PhD degree in computer science from Shanghai Jiao Tong University, China in 1989 and 1992, respectively. He is a professor of computer science in the department of computer science and engineering, Shanghai Jiao Tong University, China. His research interests lie in the area of distributed systems, data-driven software development, big data analysis, and in-memory computing.

Tianqi Sun received the BS degree in electronic information engineering from Anhui University of Finance and Economics, China in 2017. He is currently working toward the MS degree with the School of Electronic and Electrical Engineering at the Shanghai University of Engineering Science, China. His research interests include service computing, data mining and machine learning.

Ying Fan received her BS degrees in the School of Physics and Electrical Engineering, Anyang Normal University, China. Also, she is currently working toward the MS degree in the School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, China. Her research interests include data mining and cloud computing.

Wenqiang Hu is now studying for an MS degree at Shanghai University of Engineering Science, China. He received his BS degree in South-Central Minzu University, China. His current research interests include positioning in wireless cellular network and distributed computing.

Hao Zhong received his PhD degree from Peking University, China in 2009. His PhD dissertation was nominated for the distinguished PhD dissertation award of China Computer Federation. After graduation, he worked as an assistant professor at Institute of Software, Chinese Academy of Sciences, and was promoted as an associate professor in 2012. From 2013 to 2014, he was a visiting scholar at the University of California, USA. Since 2014, he had become an associate professor at Shanghai Jiao Tong University. His research interest is the area of software engineering, with an emphasis on empirical software engineering and mining software repositories. He served on the program committees of several reputable conferences such as ICSE, ESEC/FSE, ASE, OOPSLA, ICSME, MSR, and COMPSAC. He is a recipient of ACM SIGSOFT Distinguished Paper Award 2009, the best paper award of ASE 2009, and the best paper award of APSEC 2008.

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Hu, J., Huang, L., Sun, T. et al. Proactive planning of bandwidth resource using simulation-based what-if predictions for Web services in the cloud. Front. Comput. Sci. 15, 151201 (2021). https://doi.org/10.1007/s11704-019-9117-x

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