TY - JOUR
T1 - Dynamic Resource Scheduling Optimization with Network Coding for Multi-User Services in the Internet of Vehicles
AU - Huang, Chen
AU - Cao, Jiannong
AU - Wang, Shihui
AU - Zhang, Yan
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61977021, in part by the Natural Science Foundation of Hubei province under Grant 2018CFB692, in part by the Science and Technology Innovation Program of Hubei Province under Grant 2018ACA13, and in part by the Science and Technology Innovation Major Program of Hubei Province under Grant 2019ACA144.
Publisher Copyright:
© 2013 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - For Internet of Vehicles (IoV) systems with multiple users, network coding can be introduced to provide efficient error control and throughput improvement services. However, if the heterogeneity characteristics and requirements of the end users (vehicles) are neglected, it will be difficult for an IoV system to provide each end user with fair system services, without which the advantages of network coding cannot be fully achieved and the performance of the multi-user diversity system will be degraded. In this paper, we propose a Dynamic Resource Scheduling Optimization (DRSO) algorithm, a dynamic fair scheduling algorithm combined with network coding for system resource allocation in a multi-user IoV system. We construct a general solution framework for service scheduling: first, we estimate the fairness index for each end user (vehicle) with the key information on Quality of Service (QoS). Second, we construct a service scheduling control model based on the service capability of control entities (multi-access edge computing servers), and propose a new utility evaluation function. Third, based on the fairness index, we select end users into multiple network coding sets. Network coding sets are the basic units of service scheduling. The optimization objective of the scheduling service is to maximize the total utility of all the network coding sets (the utility of the control entity). Finally, we establish a coding cache queue in the control entity based on the scheduling decision. To obtain the global optimal solution for active queue control, we combine a Quantum Particle Swarm Optimization (QPSO) algorithm with a Proportional Integral (PI) model. Then, the optimal scheduling decision can be made. Extensive simulation results show that DRSO outperforms related scheduling algorithms in varying traffic loads, demonstrating that DRSO can effectively guide service resource allocation.
AB - For Internet of Vehicles (IoV) systems with multiple users, network coding can be introduced to provide efficient error control and throughput improvement services. However, if the heterogeneity characteristics and requirements of the end users (vehicles) are neglected, it will be difficult for an IoV system to provide each end user with fair system services, without which the advantages of network coding cannot be fully achieved and the performance of the multi-user diversity system will be degraded. In this paper, we propose a Dynamic Resource Scheduling Optimization (DRSO) algorithm, a dynamic fair scheduling algorithm combined with network coding for system resource allocation in a multi-user IoV system. We construct a general solution framework for service scheduling: first, we estimate the fairness index for each end user (vehicle) with the key information on Quality of Service (QoS). Second, we construct a service scheduling control model based on the service capability of control entities (multi-access edge computing servers), and propose a new utility evaluation function. Third, based on the fairness index, we select end users into multiple network coding sets. Network coding sets are the basic units of service scheduling. The optimization objective of the scheduling service is to maximize the total utility of all the network coding sets (the utility of the control entity). Finally, we establish a coding cache queue in the control entity based on the scheduling decision. To obtain the global optimal solution for active queue control, we combine a Quantum Particle Swarm Optimization (QPSO) algorithm with a Proportional Integral (PI) model. Then, the optimal scheduling decision can be made. Extensive simulation results show that DRSO outperforms related scheduling algorithms in varying traffic loads, demonstrating that DRSO can effectively guide service resource allocation.
KW - cache queue
KW - fairness control
KW - internet of vehicles
KW - Multi-user
KW - network coding set
UR - http://www.scopus.com/inward/record.url?scp=85089537817&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3001140
DO - 10.1109/ACCESS.2020.3001140
M3 - Journal article
AN - SCOPUS:85089537817
SN - 2169-3536
VL - 8
SP - 126988
EP - 127003
JO - IEEE Access
JF - IEEE Access
M1 - 9112145
ER -