A survey of autonomic computing methods in digital service ecosystems

Dhaminda B. Abeywickrama (Corresponding Author), Eila Ovaska

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)

Abstract

Service engineering of digital service ecosystems can be associated with several challenges, such as change and evolution of requirements; gathering of quality requirements and assessment; and uncertainty caused by dynamic nature and unknown deployment environment, composition and users. Therefore, the complexity and dynamics in which these digital services are deployed call for solutions to make them autonomic. Until now there has been no upto-date review of the scientific literature on the application of the autonomic computing initiative in the digital service ecosystems domain. This article presents a review and comparison of autonomic computing methods in digital service ecosystems from the perspective of service engineering, i.e., requirements engineering and architecting of services. The review is based on systematic queries in four leading scientific databases and Google Scholar, and it is organized in four thematic research areas. A comparison framework has been defined which can be used as a guide for comparing the different methods selected. The goal is to discover which methods are suitable for the service engineering of digital service ecosystems with autonomic computing capabilities, highlight what the shortcomings of the methods are, and identify which research activities need to be conducted in order to overcome these shortcomings. The comparison reveals that none of the existing methods entirely fulfills the requirements that are defined in the comparison framework.
Original languageEnglish
Pages (from-to)1-31
JournalService Oriented Computing and Applications
Volume11
Issue number1
DOIs
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • autonomous systems
  • digital ecosystems
  • service engineering
  • self- features
  • quality attributes

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