Distributed Virtual Network Embedding for Software-Defined Networks Using Multiagent Systems
Autor: | Ali Akbar Nasiri, Farnaz Derakhshan, Shahram Shah Heydari |
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Rok vydání: | 2021 |
Předmět: |
network virtualization
General Computer Science Computer science Heuristic (computer science) Multi-agent system Distributed computing Graph partitioning General Engineering Network virtualization 020206 networking & telecommunications 02 engineering and technology software-defined networking (SDN) Virtualization computer.software_genre Control theory 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing General Materials Science multi-agent systems lcsh:Electrical engineering. Electronics. Nuclear engineering virtual network embedding Software-defined networking lcsh:TK1-9971 computer Virtual network |
Zdroj: | IEEE Access, Vol 9, Pp 12027-12043 (2021) |
ISSN: | 2169-3536 |
Popis: | Virtual Network Embedding (VNE), which provides methods to assign multiple Virtual Networks (VN) to a single physical Substrate Network (SN), is an important task in network virtualization. The main problem in VNE is the efficiency of assigning customers' virtual network requests to the substrate network. This problem is known to be a Non-deterministic Polynomial-time hard (NP-hard) and heuristic solutions have been developed to solve this kind of problem. The current trend toward Software-Defined Networking (SDN) has allowed new possibilities in virtual network embedding. In this work, we propose a distributed virtual network embedding for SDNs called DVSDNE using multi-agent systems. This framework could be used to run a centralized VNE algorithm in a distributed manner to scale these algorithms with respect to network size. DVSDNE uses agents to spread the load across the substrate network. Our simulation results show the effectiveness of the proposed algorithm. Results show that DVSDNE improves execution time of embedding algorithms in large scale substrate networks, while embedding results such as acceptance ratio, revenue to cost ratio, average latency to controller, and maximum latency to controller remain comparable. |
Databáze: | OpenAIRE |
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