Public cloud computing for Software as a Service platforms

Annals of Computer Science and Information Systems

Jakubowski

Godzik

2015

Abstract-Recently, there has been an increasing need for secure, efficient and simple notification methods for mobile systems. Such systems are meant to provide users with precise tools best suited for work or leisure environments and a lot of effort has been put into creating a multitude of mobile applications. However, not much research has been put at the same time into determining which of the available protocols are best suited for individual tasks. Here a number of basic notification methods are presented and tests are performed for the most promising ones. An attempt is made to determine which methods have the best throughput, latency, security and other characteristics. A comprehensive comparison is provided, which can be used to select the right method for a specific project. Finally, conclusions are provided and the results of all the tests conducted are discussed.

Section: Related Workmentioning

confidence: 99%

Analysis of notification methods with respect to mobile system characteristics

Annals of Computer Science and Information Systems

Jakubowski

Godzik

2015

Autonomous Context-Based Service Optimization in Mobile Cloud Computing

Śnieżyński

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

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

Śnieżyński

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.