Abstract
As the concept of merging the capabilities of mobile devices and cloud computing is becoming increasingly popular, an important question arises: how to optimally schedule services/tasks between the device and the cloud. The main objective of this paper is to investigate the possibilities for using a decision module on mobile devices in order to autonomously optimize the execution of services within the framework of Mobile Cloud Computing while taking context into account. A novel model of the decision module with learning capabilities, service-oriented architecture, and service selection optimization algorithm are proposed to solve this problem. To achieve autonomous, online learning on mobile devices, we apply supervised learning. Information about the context, task description, the decision made and its results such as calculation time or power consumption are stored and form training data for a supervised learning algorithm, which updates the knowledge used by the decision module to determine the optimal place for the execution of a given type of task. To verify the solution proposed, service-oriented mobile processing systems for multimedia file conversion have been developed and series of experiments have been executed. Results show that the decision module has become more efficient in assigning the task to either the mobile device or cloud resources.
Highlights
Introduction and MotivationIn recent years, the importance of mobile devices in computer systems has increased [1]
In order to verify the possibility of using a decision module with learning capabilities to optimize the practical execution of services in the Mobile Cloud Computing (MCC) environment, the authors developed a Service-oriented Mobile Processing System (SMPS) that enables the processing of multimedia files
We have proposed a novel solution for autonomous context-based service optimization in the MCC environment which includes a formal model of the learning decision module, service-oriented architecture, and service selection optimization algorithm
Summary
The importance of mobile devices in computer systems has increased [1]. In paper [7], a novel distributed Energy Efficient Computational Offloading Framework (EECOF) is proposed for the processing of intensive mobile applications in MCC. Optimization criteria represented by the expenses correspond to the Quality of Experience (QoE) parameter [22] and may involve, inter alia, execution time, energy consumption by the mobile device and user satisfaction. We demonstrate that this optimization can be ensured by using a novel decision module with learning capabilities in the Mobile Cloud Computing environment. This paper is structured as follows: Section 2 contains a description of related work, Section 3 is concerned with employing the learning decision module in the process of optimizing the service selection strategy on mobile devices, Section 4 describes in detail the decision module, Section 5 presents the case study, Section 6 describes performance evaluation and Section 7 contains the conclusion
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