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

Taherizadeh, Salman, Jones, Andrew Clifford, Taylor, Ian James ORCID: https://orcid.org/0000-0001-5040-0772, 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

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

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:	05 May 2023 21:45
URI:	https://orca.cardiff.ac.uk/id/eprint/108407

Citation Data

Cited 104 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item