Home > Research > Publications & Outputs > Self-awareness for dynamic knowledge management...

Electronic data

  • Self-awareness for dynamic knowledge management in self-adaptive volunteer services

    Rights statement: ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 657 KB, PDF-document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Self-awareness for dynamic knowledge management in self-adaptive volunteer services

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Published
Close
Publication date11/09/2017
Host publication2017 IEEE International Conference on Web Services (ICWS)
PublisherIEEE
Pages180-187
Number of pages8
ISBN (Electronic)9781538607527
ISBN (Print)9781538607534
Original languageEnglish

Abstract

Engineering volunteer services calls for novel self-adaptive approaches for dynamically managing the process of selecting volunteer services. As these services tend to be published and withdrawn without restrictions, uncertainties, dynamisms and 'dilution of control' related to the decisions of selection and composition are complex problems. These services tend to exhibit periodic performance patterns, which are often repeated over a certain time period. Consequently, the awareness of such periodic patterns enables the prediction of the services performance leading to better adaptation. In this paper, we contribute to a self-adaptive approach, namely time-awareness, which combines self-aware principles with dynamic histograms to dynamically manage the periodic trends of services performance and their evolution trends. Such knowledge can inform the adaptation decisions, leading to increase in the precision of selecting and composing services. We evaluate the approach using a volunteer storage composition scenario. The evaluation results show the advantages of dynamic knowledge management in self-adaptive volunteer computing in selecting dependable services and satisfying higher number of requests.

Bibliographic note

©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.