Virtual network embedding with multiple priority classes sharing substrate resources

Ogino, Nagao; Kitahara, Takeshi; Arakawa, Shin’ichi; Murata, Masayuki

doi:10.1016/j.comnet.2016.10.007

Cited by 31 publications

(12 citation statements)

References 8 publications

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“…Following [3], the work of [4] apply a similar way to dispose of the collision risk with a different substrate resource model. Besides, [5] put forward a sharing VNE solution supporting multi-priority classes and also divide the required resource amount into two parts of The set of substrate nodes.…”

Section: A Resource Sharing In Embedding Problemmentioning

confidence: 99%

“…However, this fixed resource reservation scheme goes against efficient resource utilization, and resource sharing is a popular way to improve resource utilization. There are already some works discussing the mapping problem with resource sharing [3][4][5]. We can see that the higher the sharing degree, the higher the resource utilization, but introduce the collision risk between network slices.…”

Section: Introductionmentioning

confidence: 99%

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NSE-RRS: Network Slicing Embedding With Risk-Controlled Resource Sharing

Liu

Huang

et al. 2021

IEEE Access

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Network slicing (NS) is a promising technology of 5G that provides end-to-end customized network service to multi-tenant. The network slicing embedding (NSE) problem is one of the essential issues in NS technology. In most NSE solutions, the resource conservation is dedicated and utilizationinefficient. Applying the resource sharing into NSE is the most direct way to improve resource utilization, but introduce the collision risk. This paper puts forward a novel NSE solution with risk-controlled resource sharing, namely NSE-RRS. We abstract the embedded slices' loads as the uncertainties and formulate the risk as chance constraints. Hence NSE-RRS is formulated as a chance-constrained program. We apply the distributionally robust approach in terms of conditional value at risk (CVaR) to give a deterministic and tractable form of NSE-RRS. Then, we use a two-steps heuristic algorithm to solve NSE-RRS. Finally, numerical evaluation verifies that NSE-RRS can effectively improve utilization with controlled risk.

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Section: A Resource Sharing In Embedding Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

NSE-RRS: Network Slicing Embedding With Risk-Controlled Resource Sharing

Liu

Huang

et al. 2021

IEEE Access

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“…Node Capability Model: The node capability of i is marked as c i , which can be calculated using (1).…”

Section: Eo-vne Modelmentioning

confidence: 99%

“…To decrease this cost, a good approach is to utilize the existing computing devices and enterprise network to compose the substrate networks that can accommodate and run data tasks. In this case, an inevitable problem is to efficiently share the resources, named virtual network embedding (VNE) [1]. VNE allows heterogeneous networks to coexist in the same substrate network, and the resources in VNE can be shared by different types of tasks [2].…”

Section: Introductionmentioning

confidence: 99%

Energy Optimized Virtual Network Embedding With Location Constraint in the Enterprise Network

Cong

Shuang

2020

IEEE Access

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To satisfy enterprise demands of analyzing and dealing with the large scale of data with lower costs, an effective method is to integrate the servers and computers and use virtualization technology to construct an enterprise network. Prior studies on network virtualization have mainly been executed in the cloud; however, these studies may not be appropriate for enterprise networks for two reasons: i) the goal of most of them is to generate more revenues for cloud providers, but focus less on saving costs; ii) the physical machines are relatively concentrated in the cloud platform but dispersed over different geographic locations in enterprise networks. In this paper, we solve the problem of energy-optimized virtual network embedding with location constraints (EO-VNE). First, the node and link capabilities in enterprise networks are defined in the form of complex number theory, unifying computers and virtual requests. Second, the normalized method of computing and storage capabilities are proposed to identify the node capability. Third, an energy model of the enterprise network is built, and using this model, EO-VNE is shown to be NP-complete. Finally, an energy-optimized virtual network embedding with a location constraint algorithm (EOLC) is proposed to minimize the energy consumption under the constraint of node position. The experiments show that EOLC consumes less energy compared with the algorithm of energy-aware virtual network embedding with dynamic demands (EAD). It also has better performance than the location constraint algorithm based on bisection (GLC).

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“…Well-known VNE approaches include Monte Carlo Tree Search (MCTS)-based algorithms employing Multi-Commodity Flow (MaVEn-M) and the shortest path (MaVEn-S) [19], Deterministic (D-ViNE) and Randomized (R-ViNE) algorithms [13], Global Resource Capacity (GRC) [14], GRC with Multi-Commodity Flow (GRC-M) [16]. Other approaches include: VNE based on deep reinforcement learning [37], [40], reinforcement learning algorithms based on historical VNRs data [39], Real-Time Dynamic Virtual Embedding (RT-VNE) algorithm for cloud networks [28], candidate-assisted optimal mapping algorithm [11], algorithms that allow substrate resource sharing when embedding requests with multiple priority classes [29], and algorithms that implement topology awareness [10].…”

Section: Introductionmentioning

confidence: 99%

Virtual Network Embedding for Switch-Centric Data Center Networks

Rios

Bekshentayeva

Singh

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Advances in software defined and data center networks have enabled network virtualization. Virtual network embedding increases resources utilization and reduces cost of network deployment. Its performance depends on embedding algorithms and data center network topologies. In this paper, we evaluate performance of virtual network embedding algorithms based on acceptance ratio, revenue to cost ratio, and node and link utilizations by simulating virtual network embeddings on Spine-Leaf, Three-Tier, and Collapsed Core data center network topologies.

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Virtual network embedding with multiple priority classes sharing substrate resources

Cited by 31 publications

References 8 publications

NSE-RRS: Network Slicing Embedding With Risk-Controlled Resource Sharing

NSE-RRS: Network Slicing Embedding With Risk-Controlled Resource Sharing

Energy Optimized Virtual Network Embedding With Location Constraint in the Enterprise Network

Virtual Network Embedding for Switch-Centric Data Center Networks

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