Paper Info
Title
Delay-oriented Task Scheduling and Bandwidth Allocation in Fog Computing Networks
Abstract
Fog computing can aggregate the computing resources to handle the unprecedented amounts of data and becomes a promising technology in the future 5G smart Internet of Things (IoT) networks. This paper considers an IoT video data analysis system where smart IoT cameras can transmit all data to the base station or analyze the data locally. After receiving smart cameras offloading data, the base station can partially redistribute the analyzing task to the smart user equipment. The smart cameras and base station task offloading scheme and the uplink-downlink bandwidth allocation are jointly optimized to minimize the system level delay. The problem is a mixed integer non-linear problem, and the objective function contains the sum of several segmented maximum, which makes it very challenging to solve. Firstly, the smart device 0-1 binary task offloading is relaxed into a continuous form, with adding an upper bound to guarantee the solution can be as close as possible to the integer. Then introduced by a change of variables in handling the segmented maximum, all non-convex constraints are transformed with slack variables and successive convex approximation. To further ensure the iteration algorithm convergence, the disciplined iteration algorithm is proposed to prevent the iteration from getting stuck. The simulation results verify that the assisted smart user equipment can reduce the system delay combining with the proposed resource allocation algorithm.
Year
DOI
Venue
2019
10.1109/GLOBECOM38437.2019.9013494
IEEE Global Communications Conference
DocType
ISSN
Citations 
Conference
2334-0983
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Zixuan Fei122.40
Ying Wang288.95
Ruijin Sun3278.37
Yuanfei Liu421.71