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Monitoring self-adaptive applications within edge computing frameworks: A state-of-the-art review

Taherizadeh, Salman, Jones, Andrew Clifford, Taylor, Ian James, Zhao, Zhiming and Stankovski, Vlado 2018. Monitoring self-adaptive applications within edge computing frameworks: A state-of-the-art review. Journal of Systems and Software 136 , pp. 19-38. 10.1016/j.jss.2017.10.033

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Abstract

Recently, a promising trend has evolved from previous centralized computation to decentralized edge computing in the proximity of end-users to provide cloud applications. To ensure the Quality of Service (QoS) of such applications and Quality of Experience (QoE) for the end-users, it is necessary to employ a comprehensive monitoring approach. Requirement analysis is a key software engineering task in the whole lifecycle of applications; however, the requirements for monitoring systems within edge computing scenarios are not yet fully established. The goal of the present survey study is therefore threefold: to identify the main challenges in the field of monitoring edge computing applications that are as yet not fully solved; to present a new taxonomy of monitoring requirements for adaptive applications orchestrated upon edge computing frameworks; and to discuss and compare the use of widely-used cloud monitoring technologies to assure the performance of these applications. Our analysis shows that none of existing widely-used cloud monitoring tools yet provides an integrated monitoring solution within edge computing frameworks. Moreover, some monitoring requirements have not been thoroughly met by any of them.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 0164-1212
Funders: EC Horizon 2020
Date of First Compliant Deposit: 23 January 2018
Date of Acceptance: 31 October 2017
Last Modified: 31 Mar 2018 20:44
URI: http://orca.cf.ac.uk/id/eprint/108407

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