Network-Compute Co-Optimization for Service Chaining in Cloud-Edge-Radio 5G Networks

Flexible resource management can be facilitated by recent drastic advances in software-defined networking (SDN) and network function virtualization (NFV) technologies. Most of recent studies on resource management have mainly focused on the independent optimization of network function and computing...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2023-10, Vol.72 (10), p.1-17
Main Authors: Kim, Yeongjin, Choi, Pyeongjun, Lim, Jeong-A, Kwak, Jeongho
Format: Article
Language:English
Subjects:
5G mobile communication
Algorithms
cloud
Cloud computing
Computer architecture
Decisions
Dynamic scheduling
edge
Heuristic algorithms
Network function virtualization
Optimization
radio access networks
Resource management
Resource orchestration
Routing
SDN/NFV
Servers
service chaining
Software-defined networking
Wireless networks
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Summary:Flexible resource management can be facilitated by recent drastic advances in software-defined networking (SDN) and network function virtualization (NFV) technologies. Most of recent studies on resource management have mainly focused on the independent optimization of network function and computing function on top of a single-layer SDN architecture. In this paper, we focus on networking and processing resource orchestration to enhance the performance of network/computing functions in a hierarchical cloud-edge-radio 5G network architecture. Specifically, we propose a dynamic resource management algorithm, namely RACER , to process network/computing functions by opportunistically exploiting a tradeoff between the characteristics of the cloud and edge with respect to the amount of available resources and transmission delay. Herein, we decompose an original long-term problem including complex decisions in all network layers (that is, cloud, edge, and radio) into spatio-temporal subproblems without loss of optimality by leveraging Lyapunov optimization. Then, we derive the RACER algorithm to find the decisions in each layer and each time slot. This online-fashioned algorithm is pragmatic because it not only exploits information obtained from instantaneous time slots, but also operates in a distributed manner with small message exchanges between the different layers. Moreover, we demonstrate the excellent performance improvement of the proposed dynamic and holistic RACER algorithm compared to static and independent policies in the 5G network environment.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2023.3271693