Computer Science

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Browsing Computer Science by Author "Akinola, Ayotuyi Tosin"

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    QoS-Based Web Service Selection Mechanism for Ad-hoc Mobile Cloud Computing
    (University of Zululand, 2017) Akinola, Ayotuyi Tosin; Adigun, M.O; Xulu, S.S
    The Ad-hoc Mobile Cloud Computing (AMC) paradigm came into existence to settle one of the major challenges of Mobile Cloud computing especially low internet connectivity. The AMC is formed when mobile devices in the same proximity connect through a wireless connection or any other means; enabling the devices to request web services from one another within the Mobile Cloud. However, the user dissatisfaction experienced in the course of requesting for a Web Service is a challenge that needs to be addressed. This is because existing service selection approaches emphasized functional rather than non-functional qualities of matched services. Moreover, effective selection approaches must avoid quality computation during service selection that produces similar or redundant results at runtime. In an attempt to address this service selection challenge, a service selection mechanism for Ad-hoc Mobile Cloud was developed in this work. This mechanism was synthesized from existing service selection approaches. The mechanism was evaluated using the experimental research method. The evaluation of the mechanism accesses the suitability of the selected web services for all requesting users. The quantitative evaluation aspect entails the use of execution time, throughput and service availability to analyse the performance of the selection approach. The approach employs the use of selected Quality of Service (QoS) properties and user feedback to determine the most appropriate service to be selected for any request. Experiments affirm a continuous updated and unlimited range of users’ Web Service assessment (Feedback) as part of QoS properties enhances more optimal service selection within AMC computing paradigm. The is because non- feedback enabled attains one optimal selection out of seven thus guaranteeing 0.13 probability of optimal selection against a probability of 1 depicted in feedback-based selection mechanism.