Abstract As a key issue of network virtualisation, virtual network embedding (VNE) aims to embed multiple virtual network requests (VNRs) from different applications onto the substrate network effectively. In real networks, about 90% of traffic is generated by different quality of service (QoS) sensitive applications. However, most existing VNE algorithms do not account for the difference. Although several VNE algorithms considered the delay metric of applications, they usually provide strict delay guarantees for all VNRs, leading to a low VNR acceptance ratio. In this paper, we focus on the VNE problem involving multiple QoS metrics and propose a multiple QoS metrics-aware VNE algorithm based on reinforcement learning (RLQ-VNE). We first classify VNRs according to their different requirements for multiple QoS metrics including delay, jitter and packet loss rate, and then introduce reinforcement learning to implement differentiated VNE. Specifically, RLQ-VNE provides strict QoS guarantees for the VNRs with high-level QoS requirements and provides lower QoS guarantees for the VNRs with low-level QoS requirements, thus balancing the QoS guarantee and request acceptance ratio. Simulation results from multiple experimental scenarios show that RLQ-VNE improves the request acceptance ratio and network resource utilisation by sacrificing less QoS.

Abstract Virtual network embedding (VNE) algorithms dominate the effectiveness of resource sharing in network virtualization. Heuristic embedding algorithms generally make embedding decisions by artificially specified strategies, in which the node importance is measured by simply summing or multiplying several node attributes. However, the contributions of different attributes may be combined through complex functional relationships. The reinforcement learning-based VNE algorithms can optimize node embedding. However, the existing algorithms only consider the local node attributes, and only simple shortest path-based embedding policy is adopted for link embedding, resulting limited embedding effects. To overcome the above defects, we propose a double-layer reinforcement learning-based VNE algorithm (DRL-VNE). In DRL-VNE, both the global and local node attributes are extracted to represent the status of network nodes, then a policy network is constructed to optimize node embedding, and the other policy network is designed to optimize link embedding. The performance of DRL-VNE is evaluated under different network scenarios and is compared with that of heuristic and machine learning-based VNE algorithms. Simulation results show that in hierarchical network scenario, the request acceptance ratio and the resource utilization of DRL-VNE are respectively improved by 14% and by 27% compared with the best performance comparison algorithm.

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.

Overlapping community structure is a significant feature of large-scale complex networks. Some existing community detection algorithms cannot be applied to large-scale complex networks due to their high time or space complexity. Label propagation algorithms were proposed for detecting communities in large-scale networks because of their linear time complexity, however most of which can only detect non-overlapping communities, or the results are inaccurate and unstable. Aimed at the defects, we proposed an improved overlapping community detection algorithm, LPANNI (Label Propagation Algorithm with Neighbor Node Influence), which detects overlapping community structures by adopting fixed label propagation sequence based on the ascending order of node importance and label update strategy based on neighbor node influence and historical label preferred strategy. Extensive experimental results in both real networks and synthetic networks show that, LPANNI can significantly improve the accuracy and stability of community detection algorithms based on label propagation in large-scale complex networks. Meanwhile, LPANNI can detect overlapping community structures in large-scale complex networks under linear time complexity.

Ternary Content Addressable Memory (TCAM) is widely used for caching flow entries in Software Defined Network (SDN). However, limited by the current technology level and hardware cost, the capacity of TCAM is difficult to meet the needs for caching flow entries of large scale SDN, which would seriously affect the scalability of SDN. Aimed at the problem, some researches improve the efficiency of TCAM capacity by setting different lifecycle for different flow entries. Such as Openflow protocol proposed the default flow entry lifecycle, AHTM scheme dynamicly adjusted flow entry lifecycle according to the number of interrupted flows. However, these schemes didn't consider the dynamic features of network traffic in SDN, and hardly conform to the actual network demands. In order to effectively utilize TCAM capacity and thus enhance the scalability of SDN, we propose TF-IdleTimeout scheme to dynamically set flow entry lifecycle according to real-time network traffic in SDN-based data center network. Two criterion, called Flow Entry Missing Number and Flow Dropping Number, are used to evaluate the effect. The results show that, compared with existing schemes, TF-IdleTimeout scheme can greatly improve the utilization efficiency of TCAM capacity in SDN.