Towards an efficient VNF placement in network function virtualization

Qi, Dandan; Su-bin, Shen; Wang, Guanghui

doi:10.1016/j.comcom.2019.03.005

Cited by 53 publications

(40 citation statements)

References 15 publications

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“…Two platforms are being used to solve the placement problems: MATLAB to implement our heuristic algorithms, and AMPL to solve the mathematical optimization model and the alternative heuristic proposed in [13]. AMPL is a modeling language designed to be used for solving optimization problems such as linear and non-linear programming problems [16].…”

Section: Modeling Environment and Resultsmentioning

confidence: 99%

“…Additionally, the subset of nodes could be chosen based on more sophisticated parameters to reach a near optimal solution faster. Another example is [13], where the proposed heuristic narrows the target search space of VNF placement by introducing a smaller accessible scope to which the possible locations of VNFs are confined. The requests are categorized based on their source and destination.…”

Section: Related Workmentioning

confidence: 99%

“…The size of each accessible scope for each set of requests is proportional to the total traffic volume of those requests. It is shown in [13] that the size of the accessible scope will impact the time efficiency and performance of the NF placement. Considering all nodes to be in the accessible scope will not reduce the execution time but will provide the acceptance ratio of the optimization model.…”

Section: Related Workmentioning

confidence: 99%

“…We therefore emphasize minimizing the BW consumption while placing as many requests as possible. We compare the performance of the proposed heuristics with the heuristic proposed in [13] and an optimization model proposed in [1] that formulates and solves an Integer Linear Programming (ILP) model for wireless multi-hop networks.…”

Section: Related Workmentioning

confidence: 99%

“…We also implemented one heuristic proposed in the literature that limits the location of VNFs to nodes on the shortest path(s). The accessible scope heuristic [13] reviewed earlier introduces the notion of accessible scope to which the possible locations of VNFs are confined. Based on a flow's source and destination, nodes with lowest sum of distance from source and destination are in the accessible scope of the request.…”

Section: Accessible Scope Heuristicmentioning

confidence: 99%

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The Value of Simple Heuristics for Virtualized Network Function Placement

Jahedi¹,

Kunz²

2020

Future Internet

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Network Function Virtualization (NFV) can lower the CAPEX and/or OPEX for service providers and allow for quick deployment of services. Along with the advantages come some challenges. The main challenge in the use of Virtualized Network Functions (VNF) is the VNFs’ placement in the network. There is a wide range of mathematical models proposed to place the Network Functions (NF) optimally. However, the critical problem of mathematical models is that they are NP-hard, and consequently not applicable to larger networks. In wireless networks, we are considering the scarcity of Bandwidth (BW) as another constraint that is due to the presence of interference. While there exist many efforts in designing a heuristic model that can provide solutions in a timely manner, the primary focus with such heuristics was almost always whether they provide results almost as good as optimal solution. Consequently, the heuristics themselves become quite non-trivial, and solving the placement problem for larger networks still takes a significant amount of time. In this paper, in contrast, we focus on designing a simple and scalable heuristic. We propose four heuristics, which are gradually becoming more complex. We compare their performance with each other, a related heuristic proposed in the literature, and a mathematical optimization model. Our results demonstrate that while more complex placement heuristics do not improve the performance of the algorithm in terms of the number of accepted placement requests, they take longer to solve and therefore are not applicable to larger networks.In contrast, a very simple heuristic can find near-optimal solutions much faster than the other more complicated heuristics while keeping the number of accepted requests close to the results achieved with an NP-hard optimization model.

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Section: Modeling Environment and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Accessible Scope Heuristicmentioning

confidence: 99%

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