A virtual network migration approach and analysis for enhanced online virtual network embedding

Zangiabady, Mahboobeh; Aguilar-Fuster, Christian; Rubio‐Loyola, Javier

doi:10.1109/cnsm.2016.7818441

Cited by 7 publications

(13 citation statements)

References 16 publications

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“…Some detailed and recent surveys can be found in [8]- [10]. Online solutions for VNE are one of the main common trends among the proposed implementations [11]- [14], where node and link mapping are computed either independently, which most of the times provides an heuristic approach [15]- [17], or in a joint optimization [18], which usually is an higher quality solution. In Section III-A, we explain why online solutions are not the most efficient solution for satellite networks.…”

Section: A Main Vne Trends In Literaturementioning

confidence: 99%

Virtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation

Minardi

Lei

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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Non-Geostationary Orbit satellite (NGSO) is an essential element in 5G Non-Terrestrial Networks (NTNs), which can operate either independently or as complementary parts to terrestrial systems to boost the network capacity, coverage and resilience. Due to the highly dynamic topologies, one of the challenges in NGSO is how to harmonize the network virtualized resources to satisfy diverse quality of service requirements in an efficient manner. In this paper, we investigate Virtual Network Embedding (VNE) for integrated NGSO-terrestrial systems while considering dynamic topologies. We propose a Mixed Binary Linear Programming (MBLP) formulation for a Dynamic Topology-Aware VNE (DTA-VNE) algorithm. Given priori information about the network's evolution over time, DTA-VNE plans the embedding for each Virtual Network Request (VNR) over its lifetime. In a highly dynamic environment, the VNE decision can be varying for different VNRs at the expense of a considerable cost of migrating traffic and reconfiguring resources. DTA-VNE aims at minimizing this migration cost to avoid unnecessary remappings. To tackle the exponential complexity of the MBLP, we propose an efficient algorithm based on relaxation approaches (DTA-R) to solve large-scale problems. In numerical results, the effectiveness of the proposed DTA-R is demonstrated with much lower migration cost than the conventional implementations. The trade-off between the computation time and migration cost of DTA-R is studied. Finally, we test DTA-R and the baselines in our developed MultI-layer awaRe SDN-based testbed for SAtellite-Terrestrial networks (MIRSAT) to precisely quantify the packet lost for each migration. DTA-R proved to reduce the packet lost by ∼ 2.5 − 5% compared to baselines.

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Section: A Main Vne Trends In Literaturementioning

confidence: 99%

Virtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation

Minardi

Lei

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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“…1 The feasibility stage is intended to find feasible paths that meet the delay and hop count constraints of the VLs that are candidate to be migrated. The VLMP heuristic comprehends two main stages, namely, the feasibility stage and the optimality stage.…”

Section: Simulation Setupmentioning

confidence: 99%

“…Nonetheless, for validation purposes, we tried the following thresholds: cpu use thresh = 60% and bw use thresh = 50%, whose positive impact to online VNE has been previously evaluated in the work of Zangiabady et al 1 We used relaxed constraints values dly constr = 500 and hop constr = 6 which, according to empiric observations, ensure good VLMP success ratios regardless of the physical network size and density, allowing the agent to observe the effect of link migrations during the learning process. Nonetheless, for validation purposes, we tried the following thresholds: cpu use thresh = 60% and bw use thresh = 50%, whose positive impact to online VNE has been previously evaluated in the work of Zangiabady et al 1 We used relaxed constraints values dly constr = 500 and hop constr = 6 which, according to empiric observations, ensure good VLMP success ratios regardless of the physical network size and density, allowing the agent to observe the effect of link migrations during the learning process.…”

Section: Figure 4 Summary Of Online Vnementioning

confidence: 99%

“…1 It involves mapping each virtual node to one substrate node and each Virtual Link (VL) to one or more substrate links. 1 It involves mapping each virtual node to one substrate node and each Virtual Link (VL) to one or more substrate links.…”

Section: Introductionmentioning

confidence: 99%

“…Online Virtual Network Embedding (VNE) is the process of allocating physical resources such as CPU and bandwidth (BW) to support Virtual Networks (VNs) meeting both the VN resource requirements and the physical network available capacity at the time of mapping. 1 It involves mapping each virtual node to one substrate node and each Virtual Link (VL) to one or more substrate links. The aim of the VNE problem is to optimize the use of the Substrate Network (SN) resources while allocating resources for VNs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Self‐adaptive online virtual network migration in network virtualization environments

Zangiabady

Garcia‐Robledo

Gorricho

et al. 2019

Trans Emerging Tel Tech

Self Cite

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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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A Holistic Framework for Virtual Network Migration to Enhance Embedding Ratios in Network Virtualization Environments

et al. 2020

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A virtual network migration approach and analysis for enhanced online virtual network embedding

Cited by 7 publications

References 16 publications

Virtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation

Virtual Network Embedding for NGSO Systems: Algorithmic Solution and SDN-Testbed Validation

Self‐adaptive online virtual network migration in network virtualization environments

A Holistic Framework for Virtual Network Migration to Enhance Embedding Ratios in Network Virtualization Environments

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