GUISET is a proposed middleware engine currently under study in South Africa. The goal is to provide utility services for small, medium, and macroenterprises in the context of mobile e-services. Three things are important to make this engine effective and efficient: the implementation, performance, and the pricing strategy. The literature has delved richly into implementation issue of similar projects. Both the performance and the pricing strategy issues have not been fully discussed especially in the context of mobile healthcare services. Some literature has addressed the performance issue using the exogenous nonpriority and the preemptive model. However, with providers offering different services using that approach may prove to be difficult to implement. This work extends existing and widely adopted theories to non-preemptive model by using the queuing theory and the simulation model in the context of mobile healthcare services. Our evaluation is based on non-preemptive priority and nonpriority discipline. Our results reveal that the unconditional average waiting time remains the same with reduction in waiting time over the nonpreemptive priority model in four out of the five classes observed. This is envisaged to be beneficial in mobile healthcare services where events are prioritized and urgent attention is needed to be given to urgent events.
The ad-hoc mobile markets are increasing daily with providers and consumers ready to participate in these markets. Issues like service discovery and selection are major challenges in these markets. As these markets grow, researchers are working on discovery while little is done on selection. Most scholars have developed different selection mechanisms considering only consumer's quality of service. As members increase and most services are offering the same functionality, one major challenge is when the numbers of producers that meet that requirement are more than one. This is because, in certain operations, time is precious and the selection must be as fast as possible for example, in emergency situation. To tackle this, we propose Multi-Dynamic Decision algorithms as a solution approach. We consider a typical M-Health scenario in our GUISET project. We use the Multi-Criteria Decision (MCD) for selection purpose and the Dynamic Programming to search for the optimal route when there are tie in services. We use hypothetical data to run this experiment by considering 15 sublocations under the M-health scenario. Our results prove to have a better performance on time based on the shortest route. This research will be useful in a time critical situations like the Mhealth.
The increase in the level of awareness by consumers about the Cloud E-marketplaces has brought rapid growth in these competitive markets. As the markets grow, most providers are implementing different service offerings based on consumers' demand. One major challenge which has not been fully discussed in these markets is the performance impact of these offerings on consumers'. The goal of this research is to model a typical cloud marketplace and evaluate the performance impact on consumers' waiting time. To address this, we model a typical Cloud under a non-preemptive multi class discipline using Queuing theory to formulate our mathematical model and then use discrete event simulator to demonstrate a real scenario. Our evaluation was based on Non preemptive priority and non-priority discipline. Our results reveal that the unconditional average waiting time remains the same but we recorded a reduction delay which is especially profitable due to the less time in the non preemptive priority over the non priority model in four out of the five classes observed. Therefore, if the overriding requirement in the Cloud design is the reduction of the delay in some classes, then these classes should be given priority.
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