Context-aware middleware for service adaptation using a fuzzy-based and control-theoretic approach

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Context-aware middleware for service adaptation using a fuzzy-based and control-theoretic approach

 

Author: Yao, Gang
Title: Context-aware middleware for service adaptation using a fuzzy-based and control-theoretic approach
Degree: M.Phil.
Year: 2008
Subject: Hong Kong Polytechnic University -- Dissertations.
Software architecture.
Fuzzy logic.
Control theory.
Department: Dept. of Computing
Pages: 95 p. : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2190039
URI: http://theses.lib.polyu.edu.hk/handle/200/3325
Abstract: As the in-depth advance in the area of mobile computing and wireless communication in recent years, more and more sophisticated requirements have been continuously imposed upon the context-aware applications. This leads to the growing demands for delicate adaptation and control mechanisms to enable better preferred performance according to the application domain. First, due to the dynamic characteristics of mobile environments, a mobile application needs to adapt to the changing contexts to improve performance or resource utilization. We develop a generic context-aware middleware architecture - Adaptive Middleware Infrastructure (AMI) which sits between the mobile applications and the operating system in order to simultaneously satisfy the individual needs of applications while maintaining the overall system performance. Second, as the core part of the inference engine in the middleware, a Fuzzy-based Service Adaptation Model (FSAM) is designed. Our focus is the service adaptation framework, which utilizes fuzzy linguistic variables and membership degrees to define context situations and the policies so as to adopt the most suitable service according to the contexts. Fitness functions are designed to calculate the fitness degree for each policy based on the distance of the fuzzy states between the policy and the current context situation. The decision for service adaptation is made by selecting the policy with the best fitness degree. The fundamental behind this approach is the mapping relationship between the contexts and the most suitable service. The mapping stored in a reference depository is a learning process carried out before the run-time. When the mobile applications are running, the most suitable service can be identified by measuring the distance between the current contexts and the contexts in the reference depository, and then extract the corresponding service, which is considered to be the most suitable in term of user preference in the application domain. Such a method is an analogy to the "fingerprints". However, further experimental results indicate certain limitations of the FSAM model, which is when it comes to large-granular fluctuations, the FSAM model cannot handle the adaptation decisions effectively in the event of such dramastic changes. In our work, we combine control-theoretic and fuzzy-based approaches together and propose a Self-Adaptive Fuzzy-based Service Adaptation Model (SA-FSAM) by taking historical adaptation information into account, and utilizing a closed-loop control mechanism to fine-tune adaptation decisions. In this manner, the self-adaptivity and the resistance to dramastic undesirable adaptations are achieved in the time series.

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