Virtual Network Embedding
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".
Deadline for manuscript submissions: closed (16 August 2020) | Viewed by 5677
Special Issue Editor
Interests: Network Virtualisation (SDN & NFV); Network Resilience; Experimental Algorithmics
Special Issue Information
Dear Colleagues,
With the rise of software-defined networking and network function virtualization, sophisticated optimization of complex communication networks is more important than ever. The field of virtual network embedding has emerged, discussing all aspects of the optimization of resource assignment in complex networks. Practical applications include federated cloud environments or mobile (5G) telecommunication networks.
The optimization itself often hits a performance bound, as the underlying resource assignment problem is found to be NP-complete. This requires elaborate heuristic algorithms. Solution approaches encompass all types of optimization algorithms, online and offline, with resource models ranging from simple numbers to elaborate node and link categories. Recently, advances in artificial intelligence and machine learning have also provided promising results this field. Further, the evaluation and in-depth comparison of related solution approaches has started to gain more attention.
In this Special Issue, we want to discuss recent research advances in this field. We invite novel contributions showing interesting and relevant results related to the keywords listed below.
Prof. Dr. Andreas Fischer
Guest Editor
Manuscript Submission Information
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Keywords
- Planning and optimization for complex networks
- Service function chain embedding
- Network embedding for cloud providers
- Dynamic service provisioning in operator networks
- Resource planning for software-defined networks
- Network resource allocation
- Nonlinear optimization algorithms
- Machine learning for network optimization
- Mixed integer linear programming for embedding
- Embedding heuristics and metaheuristics
- Algorithmic experimentation and evaluation
- Random problem generation for embedding algorithms
- Simulation environments
