A dynamic and adaptable system for service interaction in mobile grid

Abstract

Mobile and pervasive computing with its peculiar feature of providing services at anywhere anytime basis has been at the centre of major computing researches in recent times. The device resource poverty and network instability have been reasons behind unsuccessful use of handheld technology for service request and delivery. However, interaction of these mobile service components can be adapted to further improve on the quality of service experienced by service consumers. Content, user interfaces and other adaptation mechanisms have been explored, but these have not provided needed service qualities. However, one of the challenges of designing an adaptable system is on making adaptation decisions. This dissertation, therefore, presents a dynamic and adaptable system for service interaction. A context-aware utility-based adaptation model that uses service reconfiguration pattern to effect adaptation based on contexts was developed. It was assumed that developers of mobile services design services with variants that can be selected at runtime to fit the prevailing context situation of the environment. All variants differ in required context utilities. The service variants selection decision is based on a heuristic algorithm developed for this purpose. A prototype of the model was built to validate the concept. Experiments were then conducted to evaluate the proposed model purposely to measure the interaction adaptation quality, the overall response time with or without adaptation, and the effect of service consumer preference for a given service variant on adaptation process. Results from the experiments showed that though the adaptation process comes with additional overheads in terms of variation in response time, the adaptation of service interaction is beneficial. It was observed that the overall response time increased initially as the number of service variants increased which was due to overheads by the adaptation process. However, as the number of variants increased, the response time began to fall sharply and then became steady. This proved that adaptation can actually help reduce service response time. We also found that adaptation quality degraded with increased number of service variants. The lesson learnt was that adaptation can help reduce overall response time and can improve service quality perceived by the service consumers.