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    Adaptive microservice scaling for elastic applications

    Cruz Coulson, N. and Sotiriadis, Stelios and Bessis, N. (2020) Adaptive microservice scaling for elastic applications. IEEE Internet of Things 7 (5), pp. 4195-4202. ISSN 2327-4662.

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    Today, Internet users expect web applications to be fast, performant and always available. With the emergence of Internet of Things, data collection and the analysis of streams have become more and more challenging. Behind the scenes, application owners and cloud service providers work to meet these expectations, yet, the problem of how to most effectively and efficiently auto-scale a web application to optimise for performance whilst reducing costs and energy usage is still a challenge. In particular, this problem has new relevance due to the continued rise of Internet of Things and microservice based architectures. A key concern, that is often not addressed by current auto-scaling systems, is the decision on which microservice to scale in order to increase performance. Our aim is to design a prototype auto-scaling system for microservice based web applications which can learn from past service experience. The contributions of the work can be divided into two parts (a) developing a pipeline for microservice auto-scaling and (b) evaluating a hybrid sequence and supervised learning model for recommending scaling actions. The pipeline has proven to be an effective platform for exploring auto-scaling solutions, as we will demonstrate through the evaluation of our proposed hybrid model. The results of hybrid model show the merit of using a supervised model to identify which microservices should be scaled up more.


    Item Type: Article
    Additional Information: (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: Stelios Sotiriadis
    Date Deposited: 06 Jan 2020 10:50
    Last Modified: 09 Aug 2023 12:47


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