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A Taxonomy of Performance Forecasting Systems in the Serverless Cloud Computing Environments

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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

The Serverless Clouds Computing environment (or platform) manages the resource management of its respective clients who generally submit their respective applications as sets of functions (tasks). A client may submit his application as a set of tasks (functions) or as a monolithic task (single function). Each set of functions (tasks) compiled in the form of Directed Acyclic Graph (DAG), where each node is a function representing a fine-grained task and each edge represents a dependency among two functions. The decisions made through performance forecasting systems (PFS) or resource forecasting engines are of immense importance to such resource management systems. However, the forecasting of future resources is a complex problem. Several of PFS projects span over several computer resources in several dimensions. The most of the PFS projects have already been designed for performance forecasting of resources on the Distributed Computing Environments such as Peer-Peer, Queue systems, Clusters, Grids, Virtual machine organizations and Cloud systems and therefore in software engineering point of view, the new code can be written to integrate their forecasting services on the Serverless (Edge) Clouds platforms. In this chapter the taxonomy for describing the PFS architecture is discussed. The taxonomy is used to classify and identify approaches which are followed in the implementation of the existing PFSs in the Distributed Computing Environments and to realise their adaptation in the Serverless (Edge) Cloud Computing.

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Acknowledgements

We thank Prof David Abramson of the Queensland University (who was the former head of the school of Computer Science & Engineering, Monash University) and Redmond Barry Distinguished Prof Rajkumar Buyya of the Melbourne University for their invaluable guidance to Dr Sena Seneviratne in the fields of the Distributed Computer Systems.

We acknowledge that some of the classification methods which are applied on the prediction algorithms of the Serverless Cloud Computing in this chapter had been previously used by Dr Sena Seneviratne in the field of Grid computing in the book chapter namely “Taxonomy of Performance Prediction Systems for Parallel and Distributed Computing Systems” which was published in 2015, In: BARBOSA, J. G. (ed.) Grid Computing Techniques and Future Prospects. New York: Nova Science Publishers. We acknowledge that he had submitted to ARXIV repository a similar article namely “A Taxonomy of Performance Prediction Systems in the Parallel and Distributed Computing Grids”. Further, we have used the same classification techniques in the paper namely “Taxonomy & Survey of Performance Prediction Systems For the Distributed Systems Including the Clouds” which was presented by him at 2021 IEEE International Conference CPSCom which was held in Melbourne, Australia. Further, we acknowledge that some of the Machine Learning Algorithms which are contained in this chapter had been discussed by him in the paper namely “Adapting the Machine Learning Grid Prediction Models for Forecasting of Resources on the Clouds” and presented at the 2019 IEEE conference namely “Advances in Science and Engineering Technology” which was held in Dubai, United Arab Emirates.

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Authors and Affiliations

  1. Computer Engineering Laboratory, School of Electrical and Information Engineering, The University of Sydney, Camperdown, Australia

    Sena Seneviratne & David C. Levy

  2. School of Digital Science (SDS), University of Brunei Darussalam, Bandar Seri Begawan, Brunei

    Liyanage C. De Silva

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  1. Sena Seneviratne
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  3. Liyanage C. De Silva
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Correspondence to Sena Seneviratne .

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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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Seneviratne, S., Levy, D.C., De Silva, L.C. (2023). A Taxonomy of Performance Forecasting Systems in the Serverless Cloud Computing Environments. 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_4

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