Enhancing Metaheuristic-Based Online Embedding in Network Virtualization Environments

Rubio‐Loyola, Javier; Aguilar-Fuster, Christian; Toscano-Pulido, Gregorio; Mijumbi, Rashid; Serrat-Fernández, J.

doi:10.1109/tnsm.2017.2742666

Cited by 18 publications

(9 citation statements)

References 34 publications

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“…We try to find out if this is the case with NEPA, since after our investigations it is the best performing algorithm at our disposal. Note that the reference meta-heuristic used in [17] is Harmony Search, which has comparable performances to UEPSO, as shown by the same authors in [38] (in that article UEPSO is referred to as PSOI and shows very close acceptance ratio with Harmony Search based methods).…”

Section: Appendix A: Comparison Of Results For Nepa With and Without ...mentioning

confidence: 94%

Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding

Elkael¹,

Aba²,

Araldo³

et al. 2022

Preprint

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In this article, we consider the Virtual Network Embedding (VNE) problem for 5G networks slicing. This problem requires to allocate multiple Virtual Networks (VN) on a substrate virtualized physical network while maximizing among others, resource utilization, maximum number of placed VNs and network operator's benefit. We solve the online version of the problem where slices arrive over time. Inspired by the Nested Rollout Policy Adaptation (NRPA) algorithm, a variant of the well known Monte Carlo Tree Search (MCTS) that learns how to perform good simulations over time, we propose a new algorithm that we call Neighborhood Enhanced Policy Adaptation (NEPA). The key feature of our algorithm is to observe NRPA cannot exploit knowledge acquired in one branch of the state tree for another one which starts differently. NEPA learns by combining NRPA with Neighbordhood Search in a frugal manner which improves only promising solutions while keeping the running time low. We call this technique a monkey business because it comes down to jumping from one interesting branch to the other, similar to how monkeys jump from tree to tree instead of going down everytime. NEPA achieves better results in terms of acceptance ratio and revenue-to-cost ratio compared to other state-of-the-art algorithms, both on real and synthetic topologies.

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Section: Appendix A: Comparison Of Results For Nepa With and Without ...mentioning

confidence: 94%

Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding

Elkael¹,

Aba²,

Araldo³

et al. 2022

Preprint

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“…The SDN controller can use the embedding algorithms described in the work of Rubio-Loyola et al 47 to map VNs onto the physical infrastructure. The SDN controller can use the embedding algorithms described in the work of Rubio-Loyola et al 47 to map VNs onto the physical infrastructure.…”

Section: Discussionmentioning

confidence: 99%

Self‐adaptive online virtual network migration in network virtualization environments

Zangiabady

Garcia‐Robledo

Gorricho

et al. 2019

Trans Emerging Tel Tech

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In Network Virtualization Environments, the capability of operators to allocate resources in the Substrate Network (SN) to support Virtual Networks (VNs) in an optimal manner is known as Virtual Network Embedding (VNE). In the same context, online VN migration is the process meant to reallocate components of a VN, or even an entire VN among elements of the SN in real time and seamlessly to end‐users. Online VNE without VN migration may lead to either over‐ or under‐utilization of the SN resources. However, VN migration is challenging due to its computational cost and the service disruption inherent to VN components reallocation. Online VN migration can reduce migration costs insofar it is triggered proactively, not reactively, at critical times, avoiding the negative effects of both under‐ and over‐triggering. This paper presents a novel online cost‐efficient mechanism that self‐adaptively learns the exact moments when triggering VN migration is likely to be profitable in the long term. We propose a novel self‐adaptive mechanism based on Reinforcement Learning that determines the right trigger online VN migration times, leading to the minimization of migration costs while simultaneously considering the online VNE acceptance ratio.

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“…Since VNE can be modeled as a search problem in a larger three-dimensional space, meta heuristic method can be used as an important means to solve VNE. Meta heuristic algorithm is an improvement of heuristic algorithm, which adds local search on the basis of heuristic algorithm [20]. Particle swarm algorithm (PSO) is a meta heuristic algorithm commonly used to solve VNE [21]- [23].…”

Section: A Vne Solution Based On Heuristicmentioning

confidence: 99%

Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning

Zhang¹,

Wang²,

Kumar³

et al. 2022

Preprint

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Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This paper proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.

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Enhancing Metaheuristic-Based Online Embedding in Network Virtualization Environments

Cited by 18 publications

References 34 publications

Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding

Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding

Self‐adaptive online virtual network migration in network virtualization environments

Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning

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