NDLI: CRATER: Case-based Reasoning Framework for Constructing An Adaptation Engine in Self-Adaptive Software Systems

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CRATER: Case-based Reasoning Framework for Constructing An Adaptation Engine in Self-Adaptive Software Systems

Content Provider	Semantic Scholar
Author	Abufouda, Mohammed
Copyright Year	2012
Abstract	Self-adaptivity in software systems is a property that allows software systems to autonomously adjust their behaviour during run-time to keep satisfying system's goal. This adjustment is performed with minimal or without human intervention. The motivation behind developing self-adaptive software systems is to enable the managed system to run independently which reduces the cost of complexities caused by manual handling of maintenance. Efficient self-adaptive software system should handle all possible operating scenarios that violate system's goals and react to these violations properly. This requires Adaptation Engine that receives adaptation request during the monitoring process of the managed system and responds with the appropriate adaptation response. In this thesis CRATER is presented which is a framework for building an external adaptation engine for self-adaptive software systems that is built on CaseBased Reasoning (CBR). CBR is an artificial intelligence branch constructed mainly on the idea of ”similar problems have similar solutions”. CRATER handles two of the challenging problems in self-adaptive software system: (1) Handling uncertainty that hinders the adaptation process and (2) Managing the adaptation space complexity efficiently. CRATER provides an effective mechanism for producing applicable adaptation response with effective handling for both aforementioned challenges. This thesis presents an experiment illustrating how CRATER is utilized along with results and evaluation that show a significant potential for response time, adaptation expediency and adaptation under uncertainty.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://aalab.cs.uni-kl.de/gruppe/abufouda/PDFs/Master_thesis.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Thesis

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