Abstract
In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that as a result of the experience gathered and the learning process performed, the decision module has become more efficient in assigning the task to either the mobile device or cloud resources.
Highlights
The rapid development of mobile devices and the growing importance of applications and services that run on these devices has resulted in a need to pay more attention to the quality parameters associated with the use of such solutions
Our analysis concerning existing solutions revealed that there are no systems that use machine learning for optimizing power consumption on mobile devices using Mobile Cloud Computing (MCC)
We present an original concept for an adaptive system enabling the optimization of mobile device power consumption and at the same time taking into account the context of the device’s operation
Summary
The rapid development of mobile devices and the growing importance of applications and services that run on these devices has resulted in a need to pay more attention to the quality parameters associated with the use of such solutions. Increasing mobile device uptime is possible through optimizing power consumption while preserving the best possible quality parameters of mobile services and applications Such optimization can be implemented at the software or hardware levels and should take into account the context in which the mobile device operates, including network connection quality, location and potential time and cost of executing the application or service. The choice of when and what to offload from the mobile device can be made offline during the software development process or dynamically when the device is working Adaptation makes it possible to reduce the time and costs of executing applications/services on mobile devices and to optimize their power consumption online. A few articles describe solutions that use machine learning algorithms but these have some limitations such as requiring the use of models developed previously in offline mode or not taking into account specific applications/services when optimizing the power consumption of a mobile device. The structure of the article is as follows: Sect. 2 presents the analysis of research in the field of power optimization in the context of mobile devices, Sect. 3 presents solutions in the field of machine learning on mobile devices, Sect. 4 describes the adaptive power optimization system developed for mobile devices using context information, Sect. 5 introduces the results of the experiments conducted and Sect. 6 contains conclusions
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