Virtual Network Mapping: A Graph Pattern Matching Approach

  • Yang Cao
  • Wenfei Fan
  • Shuai Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9147)


Virtual network mapping (\(\mathsf {VNM}\)) is to build a network on demand by deploying virtual machines in a substrate network, subject to constraints on capacity, bandwidth and latency. It is critical to data centers for coping with dynamic cloud workloads. This paper shows that \(\mathsf {VNM}\) can be approached by graph pattern matching, a well-studied database topic. (1) We propose to model a virtual network request as a graph pattern carrying various constraints, and treat a substrate network as a graph in which nodes and edges bear attributes specifying their capacity. (2) We show that a variety of mapping requirements can be expressed in this model, such as virtual machine placement, network embedding and priority mapping. (3) In this model, we formulate \(\mathsf {VNM}\) and its optimization problem with a mapping cost function. We establish complexity bounds of these problems for various mapping constraints, ranging from PTIME to NP-complete. For intractable optimization problems, we further show that these problems are approximation-hard, i.e., NPO-complete in general and APX-hard even for special cases.


Virtual Network Edge Mapping Virtual Node Graph Pattern Virtual Link 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Fan and Cao are supported in part by NSFC 61133002, 973 Program 2014CB340302, Shenzhen Peacock Program 1105100030834361, Guangdong Innovative Research Team Program 2011D005, EPSRC EP/J015377/1 and EP/M025268/1, and a Google Faculty Research Award. Ma is supported in part by 973 Program 2014CB340304, NSFC 61322207 and the Fundamental Research Funds for the Central Universities.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.RCBD and SKLSDE LabBeihang UniversityBeihangChina
  2. 2.University of EdinburghEdinburghUK

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