Paper Info
Title
An Approach to QoS-based Task Distribution in Edge Computing Networks for IoT Applications
Abstract
Internet of Things (IoT) is emerging as part of thn infrastructures for advancing a large variety of applications involving connection of many intelligent devices, leading to smart communities. Due to the severe limitation on the computing resources of IoT devices, it is common to off load tasks of various applications requiring substantial computing resources to computing systems with sufficient computing resources, such as servers, cloud systems, and/or data centers for processing. However, the off loading method suffers from the difficulties of high latency and network congestion in the IoT infrastructures. Recently edge computing has emerged to reduce the negative impacts of these difficulties. Yet, edge computing has its drawbacks, such as the limited computing resources of some edge computing devices and the unbalanced load among these devices. In order to effectively explore the potential of edge computing to support IoT applications,it is necessary to have efficient task management in edge computing networks. In this paper, an approach is presented to periodically distributing incoming tasks in the edge computing network so that the number of tasks, which can be processed in the edge computing network, is increased, and the quality of-service (QoS) requirements of the tasks completed in the edge computing network are satisfied. Simulation results are presented to show the improvement of using this approach on the increase of the number of tasks to be completed in the edge computing network.
Year
DOI
Venue
2017
10.1109/IEEE.EDGE.2017.50
2017 IEEE International Conference on Edge Computing (EDGE)
Keywords
DocType
ISBN
edge computing,task distribution,quality-of-service,IoT applications
Conference
978-1-5386-2018-2
Citations 
PageRank 
References 
3
0.42
0
Authors
5
Name
Order
Citations
PageRank
Yaozhong Song130.42
Stephen S. Yau2132.96
Ruozhou Yu313013.63
Xiang Zhang41238.97
Guoliang Xue5489.12