NDLI: A reinforcement learning based self-healing algorithm for managing context adaptation

Content Provider	IEEE Xplore Digital Library
Author	Cioara, Tudor Anghel, Ionut Salomie, Ioan Dinsoreanu, Mihaela Georgiana, Copil Daniel, Moldovan
Copyright Year	2010
Description	Author affiliation: Technical University of Cluj-Napoca, Romania (Cioara, Tudor; Anghel, Ionut; Salomie, Ioan; Dinsoreanu, Mihaela; Georgiana, Copil; Daniel, Moldovan)
Abstract	In this paper we approach the context adaptation problem by defining a self-healing model that uses a policy-driven reinforcement learning mechanism to take run-time decisions. The situation calculus and information system theory are used to define and formalize the self-healing concepts such as context situation entropy and equivalent context situations. The self-healing property is enforced by monitoring the system's execution environment for evaluating the degree of fulfilling the context policies for the current context situation, and determining the actions to be executed in order to keep the system in consistent functional states. The action selection phase has three main steps: selecting the context policy having the highest contribution to the context entropy, determining its associated resources in order to create inter-independent resource groups and using reinforcement learning to generate the best sequence of actions to be taken for keeping the resources as close as possible to a policy compliant state.
Starting Page	244
Ending Page	249
File Size	927724
Page Count	6
File Format	PDF
ISBN	9781424466054
e-ISBN	9781424466061
DOI	10.1109/PERCOMW.2010.5470660
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-29
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Algorithm design and analysis Monitoring equivalent context situations self-healing context adaptation reinforcement learning context entropy
Content Type	Text
Resource Type	Article

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