A Study on the Node Allocation Model Using Time Information in a Mobile Cloud Environment

Abstract

Background/Objectives: Recently, mobile cloud, which integrates P2P technology into mobile environments, has been actively researched due to improved performance of mobile devices. In addition, demand for real-time multimedia streaming services such as movies, remote education, and digital libraries based on wireless network environments is increasing.

Methods/Statistical analysis: In this paper, we look at the network mechanism when new nodes are added in cloud-based mobile streaming systems, and propose efficient tightening techniques for mobile nodes with limited resources and battery capacity.

Findings: This approach takes into account peer latency and proposes peer-to-peer selection techniques based on download times when the parent node's choice for the node being joined remains. And the method of determining pervasive node selection uses nonlinear classification based on machine learning.

Improvements/Applications: As a result, an improved system can be built to reduce the proportion of churning and to provide a more robust and resilient topology. 

Keywords: p2pnode, wireless network, cloud mobile, machine learning, nonlinear classification