A strategy learning model for autonomous agents based on classification

2017

J Grid Computing

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

Section: Introductionmentioning

confidence: 83%

Autonomous Context-Based Service Optimization in Mobile Cloud Computing

Nawrocki

2017

J Grid Computing

“…Nevertheless, if the state space is larger, i.e. we have more attributes describing tasks or context (or they have larger domains), supervised learning yields improvements faster than reinforcement learning [62]. Knowledge learned may be also represented in a human readable form, if an appropriate learning algorithm is used (e.g.…”

Section: Discussionmentioning

confidence: 99%

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

Nawrocki

2017

J Netw Syst Manage

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 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 Pła_ zek, Jakub Czy_ zewski 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. 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.

“…Luckily, proposed architecture allows agents to apply supervised learning autonomously and on-line, so they can tackle such problems too. 6. Conclusion…”

Section: State Of the Artmentioning

confidence: 94%

“…Our solution takes usage of approach proposed by nie»y«ski in [6]. Namely, we will try to employ supervised learning algorithms to solve problem previously dened in section 2.…”

Section: Problem Representationmentioning

confidence: 99%

See 1 more Smart Citation

Online Supervised Learning Approach for Machine Scheduling

Sądel

2017

Self Cite

Abstract. Due to rapid growth of computational power and demand for faster and more optimal solution in today's manufacturing, machine learning has lately caught a lot of attention. Thanks to it's ability to adapt to changing conditions in dynamic environments it is perfect choice for processes where rules cannot be explicitly given. In this paper proposes on-line supervised learning approach for optimal scheduling in manufacturing. Although supervised learning is generally not recommended for dynamic problems we try to defeat this conviction and prove it's viable option for this class of problems. Implemented in multi-agent system algorithm is tested against multi-stage, multi-product ow-shop problem. More specically we start from dening considered problem. Next we move to presentation of proposed solution. Later on we show results from conducted experiments and compare our approach to centralized reinforcement learning to measure algorithm performance.