Journal
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
Volume 17, Issue 4, Pages 2305-2317Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSM.2020.3012588
Keywords
Heuristic algorithms; Substrates; Approximation algorithms; Learning (artificial intelligence); Switches; Bandwidth; Collaboration; Dynamic and collaborative embedding; multi-layer virtual network embedding; multi-dimensional attributes; reinforcement learning
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Funding
- National Natural Science Foundation of China [61602048]
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Most of existing virtual network embedding (VNE) algorithms only consider how to construct virtual networks more efficiently on a physical infrastructure, without considering the possibility that the constructed virtual networks may be further virtualized to multiple smaller ones. We define the former scenario as single-layer VNE and the later as multi-layer VNE. As the increasing popularity of deploying large datacenter networks and wide area networks with Software Defined Network (SDN) architectures, it becomes a new requirement and possibility to provide multi-layer encapsulated network services for large tenants who have hierarchical organizational structures or need fine-grained service isolation. However, existing VNE algorithm are not specifically designed for the above requirement and not flexible enough to deal with mapping virtual network requirements (VNRs) to a physical network and smaller VNRs to a mapped virtual network. In this paper, we aim to propose a unified and flexible multi-layer VNE algorithm combining with reinforcement learning to solve the embedding of multi-layer VNRs, which can better distinguish the differences between VNRs and physical networks. Simulation results show that our algorithm achieves good performance both in single-layer and multi-layer VNE scenarios.
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