SELF ADAPTIVE SOFTWARE DENGAN TEKNIK CASE-BASED REASONING UNTUK MENINGKATKAN PERFORMA APPLICATION SERVER

Performance of application server is an important attribute that must be maintained quality and stability, because application server operated in dynamic environment with fluctuating user loads and resource levels, its potentially caused unpredictable errors. In order application server performance still optimal, it is need maintains configuration application parameters. But, do that process need deep observation each environment condition changed and of course the cost and time. The approach that can be used is self-adaptive (autonomic computing). Self-adaptive can configure application parameters automatically. Best combination application parameters configuration can be stored on a repository and used as reference when decide new decision for configuration of application server parameters when similar condition is occurred. Cased-based reasoning can do the process. Best approach will be used is self-adaptive with case-based reasoning. This research implements the approach on an experiment application that deployed on glassfish application server. The results show that self-adaptive with case-based reasoning can improve application server performance with significant improvement

Self-adaptation via concurrent multi-action evaluation for unknown context

Context-aware computing has been attracting growing attention in recent years. Generally, there are several ways for a context-aware system to select a course of action for a particular change of context. One way is for the system developers to encompass all possible context changes in the domain knowledge. Other methods include system inferences and adaptive learning whereby the system executes one action and evaluates the outcome and self-adapts/self-learns based on that. However, in situations where a system encounters unknown contexts, the iterative approach would become unfeasible when the size of the action space increases. Providing efficient solutions to this problem has been the main goal of this research project. Based on the developed abstract model, the designed methodology replaces the single action implementation and evaluation by multiple actions implemented and evaluated concurrently. This parallel evaluation of actions speeds up significantly the evolution time taken to select the best action suited to unknown context compared to the iterative approach. The designed and implemented framework efficiently carries out concurrent multi-action evaluation when an unknown context is encountered and finds the best course of action. Two concrete implementations of the framework were carried out demonstrating the usability and adaptability of the framework across multiple domains. The first implementation was in the domain of database performance tuning. The concrete implementation of the framework demonstrated the ability of concurrent multi-action evaluation technique to performance tune a database when performance is regressed for an unknown reason. The second implementation demonstrated the ability of the framework to correctly determine the threshold price to be used in a name-your-own-price channel when an unknown context is encountered. In conclusion the research introduced a new paradigm of a self-adaptation technique for context-aware application. Among the existing body of work, the concurrent multi-action evaluation is classified under the abstract concept of experiment-based self-adaptation techniques