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24.02.2017 | Ausgabe 1/2018 Open Access

Journal of Network and Systems Management 1/2018

Adaptive Service Management in Mobile Cloud Computing by Means of Supervised and Reinforcement Learning

Zeitschrift:
Journal of Network and Systems Management > Ausgabe 1/2018
Autoren:
Piotr Nawrocki, Bartlomiej Sniezynski
Wichtige Hinweise
The research presented in this paper was supported by the Polish Ministry of Science and Higher Education under AGH University of Science and Technology Grant 11.11.230.124. We thank Małgorzata Płażek, Jakub Czyżewski and Michał Janiec for assistance with implementation and testing.
Neither the entire paper nor any part of its content has been published or has been accepted for publication elsewhere. It has not been submitted to any other journal.

Abstract

Since the concept of merging the capabilities of mobile devices and cloud computing is becoming increasingly popular, an important question is how to optimally schedule services/tasks between the device and the cloud. The main objective of this article is to investigate the possibilities for using machine learning on mobile devices in order to manage the execution of services within the framework of Mobile Cloud Computing. In this study, an agent-based architecture with learning possibilities is proposed to solve this problem. Two learning strategies are considered: supervised and reinforcement learning. The solution proposed leverages, among other things, knowledge about mobile device resources, network connection possibilities and device power consumption, as a result of which a decision is made with regard to the place where the task in question is to be executed. By employing machine learning techniques, the agent working on a mobile device gains experience in determining the optimal place for the execution of a given type of task. The research conducted allowed for the verification of the solution proposed in the domain of multimedia file conversion and demonstrated its usefulness in reducing the time required for task execution. Using the experience gathered as a result of subsequent series of tests, the agent became more efficient in assigning the task of multimedia file conversion to either the mobile device or cloud computing resources.

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