Paper Info
Title
Enhanced Online Q-Learning Scheme for Resource Allocation with Maximum Utility and Fairness in Edge-IoT Networks
Abstract
Internet of Things (IoT) is experiencing an explosion in the data traffic due to the increase in the number of heterogeneous applications. The existing cloud computing models will not be capable to support the IoT applications that are delay-sensitive and using high bandwidth. The Edge-IoT systems represented by shared edge clouds support a wide range of IoT applications. Edge clouds provide resources closer to the IoT devices to tackle the delay sensitivity and bandwidth issues. However, the allocation of these resources with guaranteed application's utility in the context of Edge-IoT with multiple heterogeneous IoT applications, various resource demands, and limited resource availability is challenging. In this paper, we propose a novel enhanced online Q-learning scheme to allocate resources from edge clouds to IoT applications to maximize their utility and maintain allocation fairness among them. The developed online Q-learning scheme approximates its Q-value to tackle the problem of large state space, reduce the required learning computation, and expedite the system convergence. It is implemented using two settings: centralized using a dedicated controller at the edge cloud and distributed where edge servers learn cooperatively to achieve a common goal of finding joint resource allocation policy that maximizes the IoT applications' utilities. Extensive numerical results demonstrate the capability of the proposed scheme in improving applications' utilities and allocation fairness.
Year
DOI
Venue
2020
10.1109/TNSE.2020.3015689
IEEE Transactions on Network Science and Engineering
Keywords
DocType
Volume
Edge computing,internet of things,resource allocation,online q-learning.
Journal
7
Issue
ISSN
Citations 
4
2327-4697
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Ismail AlQerm1335.06
Jianli Pan247133.61