DocumentCode :
2577787
Title :
Towards a Framework for Self-Adaptive Reliable Network Services in Highly-Uncertain Environments
Author :
Ceccarelli, A. ; Grønbæk, Jesper ; Montecchi, L. ; Schwefel, H.-P. ; Bondavalli, A.
Author_Institution :
Univ. of Firenze, Firenze, Italy
fYear :
2010
fDate :
4-7 May 2010
Firstpage :
184
Lastpage :
193
Abstract :
In future inhomogeneous, pervasive and highly dynamic networks, end-nodes may often only rely on unreliable and uncertain observations to diagnose hidden network states and decide upon possible remediation actions. Inherent challenges exists to identify good and timely decision strategies to improve resilience of end-node services. In this paper we present a framework, called ODDR (Observation, Diagnosis, Decision, Remediation), for improving resilience of network based services through integration of self-adaptive monitoring services, network diagnosis, decision actions, and finally execution (and monitoring) of remediation actions. We detail the motivations to the ODDR design, then we present its architecture, and finally we describe our current activities towards the realization and assessment of the framework services and the main results currently achieved.
Keywords :
fault diagnosis; self-adjusting systems; ubiquitous computing; uncertain systems; highly-uncertain environments; network diagnosis; remediation actions; self-adaptive monitoring services; self-adaptive reliable network services; Ad hoc networks; Bonding; Conferences; Convergence; Distributed computing; Measurement uncertainty; Monitoring; Resilience; Sensor systems; Training data; ODDR; adaptive model generation; decision; diagnosis; measurement uncertainty; monitoring; pervasive systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Object/Component/Service-Oriented Real-Time Distributed Computing Workshops (ISORCW), 2010 13th IEEE International Symposium on
Conference_Location :
Carmona, Seville
Print_ISBN :
978-1-4244-7218-5
Type :
conf
DOI :
10.1109/ISORCW.2010.21
Filename :
5479511
Link To Document :
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