Abstract
Service-Based Applications (SBAs) have become increasingly pervasive. These applications rely on the third-parties services available on the cloud, and services must be aware of and adapt to their changing contexts in highly dynamic environments. SBAs with context-aware capabilities have provided the users with personalized services based on their user's (intrinsic) and device's (extrinsic) contextual information, as well as the Quality of Services (QoS). The correctness of service substitution in runtime adaptation is substantial for the continuity of user activity on the system. In Mobile Cloud Learning (MCL) environment most works only focus on intrinsic context factors such as learner's profile, learner's location, etc. We then introduce a comprehensive Dynamic Service Adaptation of Context-Aware Mobile Cloud Learning (DACAMoL), which is designed to reason for bothcontextual factors and QoS inservice discovery, ranking, and selection. The framework represents the contextual information, service descriptions, and QoS using a semantic-based approach to improve the correctness of service substitution. In this paper, wepresent a quasi-experiment study to demonstrate the DACAMoL framework with a mobile app called Mudahnya BM. Mudahnya BM is a learning app to learn basic knowledge of Malay language that build using RESTful backend services. The study involved 30 participants and 33 randomized scenarios tested using One-Sample Wilcoxon Signed Rank test. The results show significantly better service substitutions with 32 out of 33educational servicesare correctly adapted (i.e. 95% of the population).
Highlights
The context-aware service-based application is made up of a composition of services where it needs to be closely monitored depending on the changes in requirements or contextual information during runtime
We introduce a comprehensive Dynamic Service Adaptation of Context-Aware Mobile Cloud Learning (DACAMoL), which is designed to reason for bothcontextual factors and Quality of Services (QoS) inservice discovery, ranking, and selection
An adaptation of service is required if the network is considered as poor when it is below 66 kilobits per second (Kbps) and the device’s battery level is considered low if it is below 50 percent [15], [16]
Summary
The context-aware service-based application is made up of a composition of services where it needs to be closely monitored depending on the changes in requirements or contextual information during runtime. The dynamic service adaptation process that inclusive of service discovery, ranking, and selection is needed to operate the changes. Selecting a correct equivalent service to replace the unavailable service due to changing requirements or contextual is still limitedly addressed by the current related works. There are several factors to be considered while measuring the correctness of the substituted services such as the learner's context, device's context, and QoS [2]. This paper presents a quasi-experiment study to measure the correctness of the substituted services using the designed framework named Dynamic Adaptation of Context Aware Mobile Cloud Learning (DACAMoL) [3]. As a result of using context representation and service descriptions with semantic enrichment in the reasoning process, specific learning resources are able to be adapted for the learners.
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