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QoS Analysis for Serverless Computing Using Machine Learning

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Serverless Computing: Principles and Paradigms

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 162))

Abstract

Large-scale computing systems are becoming more popular as the need for computing power increases every year. Serverless computing has emerged as a powerful and compelling paradigm for the hosting services and applications because of the rapid shift in business application architectures for containers and microservices. Further, Serverless computing offers economical services and scalability to fulfil the growing demand of computing in a timely manner. Therefore, it is important to analyse the Quality of Service (QoS) of Serverless Computing systems to monitor its performance. In this chapter, we used the latest machine learning models to predict system configurations in Serverless computing environments. Knowing about system configurations in advance helps to maintain the performance of the system by analysing QoS. Further, a no-cost model is proposed to examine and compare different configurations of workstations in serverless computing environments. To achieve this, we deployed Theoretical Moore’s, Fitted Moore’s, 2-D poly regression and 3-D poly regression machine learning models following Graphics Processing Unit (GPU) requirements and compare the results. The experimental results demonstrated that Fitted Moore was the best model with an R2 score of 0.992.

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Author information

Authors and Affiliations

  1. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK

    Muhammed Golec, Sundas Iftikhar, Pratibha Prabhakaran, Sukhpal Singh Gill & Steve Uhlig

Authors
  1. Muhammed Golec
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  2. Sundas Iftikhar
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  3. Pratibha Prabhakaran
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  4. Sukhpal Singh Gill
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  5. Steve Uhlig
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Corresponding author

Correspondence to Muhammed Golec .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India

    Rajalakshmi Krishnamurthi

  2. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

    Adarsh Kumar

  3. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK

    Sukhpal Singh Gill

  4. School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

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Golec, M., Iftikhar, S., Prabhakaran, P., Gill, S.S., Uhlig, S. (2023). QoS Analysis for Serverless Computing Using Machine Learning. In: Krishnamurthi, R., Kumar, A., Gill, S.S., Buyya, R. (eds) Serverless Computing: Principles and Paradigms. Lecture Notes on Data Engineering and Communications Technologies, vol 162. Springer, Cham. https://doi.org/10.1007/978-3-031-26633-1_7

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