Collaborative Learning-based Network Resource Scheduling and Route Management for Multi-Mode Green IoT

Authors

Zhenyu Zhou, North China Electric Power University (Baoding)
Xinyi Chen, Information and Communication Branch of State Grid Fujian Electrie Power Co. Ltd.
Haijun Liao, North China Electric Power University (Baoding)
Zhong Gan, State Grid Shanghai Electric Power Company Power Dispatch Control Centre
Fei Xiao, State Grid Shanghai Electric Power Company Power Dispatch Control Centre
Qi Tu, State Grid Shanghai Electric Power Company Power Dispatch Control Centre
Wenwen Sun, China Electric Power Research Institute
Yun Liu, State Grid Jibei Information and Telecommunication Company
Shahid Mumtaz, Universidade de Aveiro
Mohsen Guizani, Mohamed bin Zayed University of Artificial IntelligenceFollow

Document Type

Article

Publication Title

IEEE Transactions on Green Communications and Networking

Abstract

The multi-mode green internet of things (IoT) provides a communication support for social assets of smart park connecting to power grid for low-carbon operation. Software defined networking (SDN) and network function virtualization (NFV) can flexibly integrate heterogeneous communication modes through network resource scheduling and route management. However, the joint optimization of virtual network functions (VNF) embedding and flow scheduling faces several challenges of differentiated QoS guarantee, coupling and externality of VNF embedding, and route selection conflicts. In this work, a multi-timescale VNF Embedding and floW Scheduling algorithm named NEWS is proposed to maximize throughput while reducing VNF embedding cost and energy consumption. Specifically, the joint optimization problem is transformed into three subproblems, i.e., large-timescale VNF embedding, small-timescale admission control, small-timescale route selection and computation resource allocation. A swap matching-based low-cost VNF embedding algorithm is proposed for the first subproblem. Then, a queue backlog threshold-based admission control strategy is proposed for the second subproblem. Next, the third subproblem is decomposed into two stages, where a collaborative Q-learning-based backpressure-aware algorithm is presented in the first stage, and a greedy-based computation resource allocation algorithm is given in the second stage. Simulations demonstrate that NEWS performs superior in throughput, embedding cost, and energy consumption.

First Page

1

Last Page

1

DOI

10.1109/TGCN.2022.3187463

Publication Date

6-30-2022

Keywords

backpressure awareness, collaborative learning, Costs, Internet of Things, Multi-mode green IoT, multi-timescale optimization, Optimization, Processor scheduling, resource scheduling and route management, Scheduling, Servers, Throughput

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Recommended Citation

Z. Zhou et al., "Collaborative Learning-based Network Resource Scheduling and Route Management for Multi-Mode Green IoT," in IEEE Transactions on Green Communications and Networking, 2022, doi: 10.1109/TGCN.2022.3187463.