VNR Algorithm: A Greedy Approach for Virtual Networks Reconfigurations

Fajjari, Ilhem; Aitsaadi, Nadjib; Pujolle, Guy; Zimmermann, Hubert

doi:10.1109/glocom.2011.6134006

Cited by 88 publications

(70 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some other researchers aim at virtual network reconfiguration [9][10][11] which means to adjust running virtual networks to re-allocate resource of substrate resource. Their ultimate goal is also to improve resource utilization.…”

Section: Related Workmentioning

confidence: 99%

Virtual Network Embedding with Virtual Topology Pre-Pruning

Yang¹

2013

Advances in Intelligent Systems Research

View full text Add to dashboard Cite

Abstract-Efficient network resource utilization is crucial in the field of Virtual Network Embedding (VNE). The diversity of virtual topologies belong to various services providers (SPs) severely affects the efficiency of VNE algorithms and fairness between SPs. To achieve high resource utilization of the physical substrate network and leverage repeatable features in virtual machine deployment, this paper proposes a VNE algorithm with a pre-pruning mechanism to virtual topologies. Before embedding a virtual network of a SP, if the virtual topology is too complex, i.e. has too many virtual nodes or high connectivity, such mechanism will reconfigure the topology to reduce complexity under the premise to guarantee isomorphism. Then the algorithm will felicitously map the virtual network onto substrate network through a Particle Swarm Optimization (PSO) based process. Simulation results show that due to the pre-pruning procedure the algorithm can achieve high revenue to cost ratio and more fairness than traditional VNE algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Virtual Network Embedding with Virtual Topology Pre-Pruning

Yang¹

2013

Advances in Intelligent Systems Research

View full text Add to dashboard Cite

show abstract

“…Both approaches differ from the work in this paper in that our approach does not require changing virtual network embeddings. The authors in [13] propose a solution which aims at minimising the number of congested substrate links by carrying out link migrations. But this is a reactive solution since it is carried out only when an embedding strategy cannot assign a VN request in the SN.…”

Section: Related Workmentioning

confidence: 99%

Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Mijumbi

Gorricho

Serrat

et al. 2014

2014 IEEE Network Operations and Management Symposium (NOMS)

View full text Add to dashboard Cite

Abstract-Network virtualisation is considerably gaining attention as a solution to ossification of the Internet. However, the success of network virtualisation will depend in part on how efficiently the virtual networks utilise substrate network resources. In this paper, we propose a machine learning-based approach to virtual network resource management. We propose to model the substrate network as a decentralised system and introduce a learning algorithm in each substrate node and substrate link, providing self-organization capabilities. We propose a multiagent learning algorithm that carries out the substrate network resource management in a coordinated and decentralised way. The task of these agents is to use evaluative feedback to learn an optimal policy so as to dynamically allocate network resources to virtual nodes and links. The agents ensure that while the virtual networks have the resources they need at any given time, only the required resources are reserved for this purpose. Simulations show that our dynamic approach significantly improves the virtual network acceptance ratio and the maximum number of accepted virtual network requests at any time while ensuring that virtual network quality of service requirements such as packet drop rate and virtual link delay are not affected.

show abstract

“…One of the first works that applied reconfigurations in online embedding algorithms is [7]. Every time the embedding of a service fails, a greedy reconfiguration algorithm tries to migrate one virtual node with its incident virtual edges to make room for the failed request.…”

Section: Related Workmentioning

confidence: 99%

Fast and efficient network service embedding method with adaptive offloading to the edge

Németh

Szalay

Dóka

et al. 2018

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

View full text Add to dashboard Cite

Abstract-Next generation networks and applications have recently drawn the attention of many researchers in both academia and industry. The plethora of service orchestration, embedding and scheduling solutions being developed indicates that efficient resource utilization in the cloud and edge/fog architecture is of crucial importance in order to exploit the great economic potential of this infrastructure. In this paper we propose a novel service orchestration approach that aims for speed and quality in terms of service provisioning: our embedding algorithm instantly deploys end-to-end delay-constrained service graphs while regularly offloads the most burdened parts of the infrastructure applying a cost-aware VNF migration strategy. In this sense we propose a hybrid orchestration approach, which unites the advantages of online heuristic and offline optimizing service orchestration methods, with the goal of obtaining a system design that provides fast service placement decisions and efficient long-term operation. By an exhaustive evaluation of our orchestration system on core, cloud and edge network topologies, we show its benefits in efficiency and the collateral cost of migrations.

show abstract

VNR Algorithm: A Greedy Approach for Virtual Networks Reconfigurations

Cited by 88 publications

References 14 publications

Virtual Network Embedding with Virtual Topology Pre-Pruning

Virtual Network Embedding with Virtual Topology Pre-Pruning

Design and evaluation of learning algorithms for dynamic resource management in virtual networks

Fast and efficient network service embedding method with adaptive offloading to the edge

Contact Info

Product

Resources

About