Link Mapping Stage Research Articles

Load-Balanced Virtual Network Embedding Based on Deep Reinforcement Learning for 6G Regional Satellite Networks

Regional satellite networks are capable of supporting denser coverage and more reliable communications in the target area and hence have been viewed as an essential part of the sixth generation (6G) communication system. Since satellite networks are time-varying and have limited resources, efficient resource management schemes are needed to accommodate massive and ubiquitous service requests. As a remedy, virtual network embedding (VNE) can enable diverse virtual network requests (VNRs) to share the same substrate network resources to improve resource utilization. However, existing works are few and mainly rely on heuristic methods, whose static embedding strategies cannot be optimized according to the resource state. In this paper, we propose a deep reinforcement learning (DRL) aided load-balanced VNE algorithm (DRL-LBVNE) for the regional satellite networks, where we first build a low-cost regional satellite network scenario and derive its multi-fold coverage constraints. Besides, we design a novel preprocessing scheme to reduce mapping failure, where the satellite network is divided into multiple mapping regions, and VNRs are only deployed in the mapping region with the lowest load. In the node mapping stage, the DRL agent can calculate the embedding probabilities of each physical node based on the environment state. Moreover, a comprehensive metric for path selection is presented in the link mapping stage. Simulation results show that the DRL-LBVNE algorithm outperforms the other five state-of-art algorithms in acceptance rate, resource utilization, and average delay, reflecting better adaptability to dynamic satellite networks.

Distributed parallel genetic algorithm for online virtual network embedding

SummaryNetwork virtualization (NV) has emerged as a promising paradigm to address the constraints of implementing new protocols and services in existing network architecture by allowing the simultaneous coexistence of multiple heterogeneous virtual networks on a shared substrate infrastructure. Hence, NV is a critical technology for establishing future network architectures (e.g., 5G network and the smart Internet of Things [IoT]). Virtual network embedding (VNE) is a major challenge in NV since it is acknowledged as ‐hard. Many VNE solutions have been proposed over the past decade. However, the proposed solutions merely centralize VNE node mapping while recommending virtual link embedding for the shortest path method or multicommodity flow (MCF) mechanism. This research paper presents an intelligent virtual network orchestration based on genetic algorithm (GA) for the link mapping stage that implements distributed parallelism to significantly and efficiently reduce the operation time. Our extensive simulations have demonstrated that the proposed algorithm not only outperforms the state‐of‐the‐art VNE algorithm in all performance metrics but also achieves 44.01% faster embedding speed than the most well‐known, fastest link mapping method in VNE.

DSCD: Delay Sensitive Cross-Domain Virtual Network Embedding Algorithm

In the coming 5G era, more and more applications are time-delay sensitive, and the importance of low delay in fields such as telemedicine, autonomous driving and military order transmission is self-evident. However, for different application scenarios, their delay requirements are different. How to meet different Quality of service (QoS) requirements simultaneously becomes a challenge. The emergence of network virtualization (NV) technology can overcome this problem. However, it is difficult for the existing embedding algorithms to meet the requirements of virtual networks on time delay performance. Therefore, in order to study the delay problem of virtual network, we proposed a Delay Sensitive Cross-Domain Virtual Network Embedding Algorithm (DSCD-VNE) in this paper. First of all, different from the traditional virtual network embedding (VNE) algorithm, this algorithm divides the mapping process into a “node-link-node” three-stage embedding algorithm. Second, node resource metrics and set constraints are introduced in the generation phase of candidate link matrix. Third, the algorithm takes candidate link matrix as the core, and uses Kruskal minimum spanning tree to choose the link with the shortest delay for embedding. Fourth, path splitting mechanism and $K$ -shortest path algorithm are introduced with the aim of increasing the utilization of the substrate links in link mapping stage. The simulation results show that the proposed DSCD-VNE algorithm greatly improves the performance of the algorithm compared with the traditional node-link two-stage algorithm, especially in the aspect of delay, the improvement rate is as high as 77%.

Collaborative attributes and resources for single-stage virtual network mapping in network virtualization

Virtual network embedding (VNE) is the virtualized node and link resources allocation problem in network virtualization environment, aiming at achieving the simultaneous optimal node and link mapping assignment per VN. Currently, a large number of mapping algorithms for VNE exist in the academia. Existing algorithms mostly focus on mapping each virtual network (VN) in two ordered stages: First virtual node mapping stage and second virtual link mapping stage, leading to non-optimal VN mapping assignment. Though multiple one-stage algorithms exist, adopting either optimization theory or graph theory, they usually involve much more VN assignment calculation time. Hence, these one-stage algorithms are not suitable for dynamic network scenario. Not to mention practical VNE application. Based on the background and our gained research results, we propose another heuristic mapping algorithm VNE-ARS, completing all nodes and links per VN in one single embedding stage. Each VN embedding assignment can be completed in polynomial time, using our VNE-ARS. Main network attributes and resources are collaborated, serving as the mapping criterion of our VNE-ARS. In order to highlight our VNE-ARS strength, we conduct the evaluation experiments. We compare our VNE-ARS algorithm against existing mapping algorithms.

A 2-Layers Virtual Network Mapping Algorithm Based on Node Attribute and Network Simplex

Network virtualization is one of the most important techniques to overcome Internet ossification, which can enable multiple networks to run on a common infrastructure. Virtual network mapping is a key process of network virtualization to efficiently map virtual network onto the substrate network within constraints of node and link resource. However, previous VNM heuristic algorithms completely separate the node mapping stage from link mapping stage and ignore the effect of link mapping results on physical node ranking, which may lead to higher mapping cost and distributed distribution of virtual nodes on the substrate network. The shortest path algorithm is used at most to resolve virtual link mapping which results in longer mapping time. To address these issues, a two-layer virtual network mapping algorithm named simplex-VNM is proposed based on node attribute and network simplex in this paper. In node mapping layer, a new node ranking approach which considers topology attributes, global network resources, and mapped physical node information provides candidate nodes and node mapping cost for link mapping layer. Then, in link mapping layer, network simplex algorithm which has smaller time complexity compared to traditional shortest path algorithms has been introduced to find the optimal link mapping solution under the result of node mapping layer. Finally, according to the results of two layers, the virtual node and relevant links will be mapped with minimal total mapping cost. Extensive simulation results have demonstrated that our proposed method behaves better than four other algorithms that consider node attribute and use shortest path algorithm in terms of total mapping cost and mapping time.

Virtual network embedding based on modified genetic algorithm

To fend off the ossification of Internet architecture, virtual network embedding has been propounded as one of the most important techniques to address this issue. Virtual network embedding is a process that consists of two stages including node mapping stage and link mapping stage, the aim of node mapping stage is to map the virtual nodes from virtual network requests (VNRs) onto the substrate nodes meanwhile satisfying the CPU capacity constraints on nodes, the goal of link mapping stage is to map the virtual links from VNRs onto the substrate paths while satisfying the bandwidth resource constraints on links. This paper proposed a virtual network embedding algorithm based on modified genetic algorithm, improved the classical genetic algorithm from three aspects: population initialization strategy, improved mutation operation and improvement operation, took advantage of the selection operation, crossover operation, mutation operation, feasibility checking operation, and utilized the fitness function to choose the best chromosome. Simulation results indicated that our proposed method has significantly increased the acceptance ratio of VNRs and the long-term average revenue of Infrastructures (InPs) compared with other two state-of-the-art algorithms.

Resource allocation with multi-factor node ranking in data center networks

In data center networks, resource allocation refers to mapping a large number of workloads to substrate networks. Existing heuristic mapping algorithms evaluate the resources of the nodes according to one resource factor or a product of resource factors, which will probably lead to an imbalance of the resource allocation. Furthermore, neglecting the hops of the substrate paths in the resource allocation may result in low resource utilization. In this paper, we adopt a top-k dominating model to rank the nodes, aiming at balancing these factors to improve resource allocation. Moreover, we propose a novel mapping algorithm TK-Match, which consists of a node mapping stage and link mapping stage. In the node mapping stage, TK-Match maps the virtual nodes to the substrate nodes in terms of the node ranking and the hops of the substrate paths. In the link mapping stage, TK-Match adopts the k-shortest path algorithm to map the virtual links. Extensive simulation experiments show that TK-Match can greatly increase the long-term average revenue and the acceptance ratio.

A novel paths algebra-based strategy to flexibly solve the link mapping stage of VNE problems

One of the main challenges of network virtualization is the virtual network embedding problem (VNE) that consists of mapping virtual network demands in physical network resources. VNE can be decomposed into two stages: virtual node and virtual link mapping. In the first stage, each virtual node is mapped to a suitable node in the physical network whereas the second stage is in charge of mapping the links connecting virtual nodes to paths in the physical network that suit the virtual network demands.In this paper we propose the utilization of a mathematical multi-constraint routing framework called “paths algebra” to solve the virtual link mapping stage. Paths algebra provides the flexibility to introduce an unlimited number of linear and non-linear constraints and metrics to the problem while finding all the eligible paths in the physical network to perform the virtual link mapping resulting in better and more flexible embeddings.

A unified enhanced particle swarm optimization‐based virtual network embedding algorithm

SUMMARYVirtual network (VN) embedding is a major challenge in network virtualization. In this paper, we aim to increase the acceptance ratio of VNs and the revenue of infrastructure providers by optimizing VN embedding costs. We first establish two models for VN embedding: an integer linear programming model for a substrate network that does not support path splitting and a mixed integer programming model when path splitting is supported. Then we propose a unified enhanced particle swarm optimization‐based VN embedding algorithm, called VNE‐UEPSO, to solve these two models irrespective of the support for path splitting. In VNE‐UEPSO, the parameters and operations of the particles are well redefined according to the VN embedding context. To reduce the time complexity of the link mapping stage, we use shortest path algorithm for link mapping when path splitting is unsupported and propose greedy k‐shortest paths algorithm for the other case. Furthermore, a large to large and small to small preferred node mapping strategy is proposed to achieve better convergence and load balance of the substrate network. The simulation results show that our algorithm significantly outperforms previous approaches in terms of the VN acceptance ratio and long‐term average revenue. Copyright © 2012 John Wiley & Sons, Ltd.