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A Knowledge-based Approach for Self-healing Service-oriented Applications

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Date
2016
Dewey
Informatique générale
Sujet
Service Agents; Rules; Composite service execution
DOI
http://dx.doi.org/10.1145/3012071.3012100
Conference name
8th International Conference on Management of Digital EcoSystems (MEDES 16)
Conference date
11-2016
Conference city
Biarritz
Conference country
France
Book title
MEDES 16 - Proceedings of the 8th International Conference on Management of Digital EcoSystems
Author
Chbeir, Richard; Agrawal, Rajeev; Biskri, Ismail
Publisher
ACM Press
Publisher city
New York
Year
11-2016
Pages number
236
ISBN
978-1-4503-4267-4
URI
https://basepub.dauphine.fr/handle/123456789/16140
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Angarita Arocha, Rafaël
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Rukoz, Marta
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Manouvrier, Maude
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Cardinale, Yudith
status unknown
Type
Communication / Conférence
Item number of pages
1-8
Abstract (EN)
In the context of service-oriented applications, the self-healing property provides reliable execution in order to support failures and assist automatic recovery techniques. This paper presents a knowledge-based approach for self-healing Composite Service (CS) applications. A CS is an application composed by a set of services interacting each other and invoked on the Web. Our approach is supported by Service Agents, which are in charge of the CS fault-tolerance execution control, making decisions about the selection of recovery and proactive strategies. Service Agents decisions are based on the information they have about the whole application, about themselves, and about what it is expected and what it is really happening at run-time. Hence, application knowledge for decision making comprises off-line precomputed global and local information, user QoS preferences, and propagated actual run-time information. Our approach is evaluated experimentally using a case study.

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