Network Service Embedding Across Multiple Resource Dimensions
| Autor: | Angelos Pentelas, George Papathanail, Panagiotis Papadimitriou, Ioakeim Fotoglou |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science Heuristic (computer science) Distributed computing Resource efficiency 020206 networking & telecommunications 02 engineering and technology Virtualization computer.software_genre Network service 0202 electrical engineering electronic engineering information engineering Cellular network Resource management Electrical and Electronic Engineering Heuristics Virtual network computer |
| Zdroj: | IEEE Transactions on Network and Service Management. 18:209-223 |
| ISSN: | 2373-7379 |
| DOI: | 10.1109/tnsm.2020.3044614 |
| Popis: | Network Function Virtualization (NFV) poses the need for efficient embeddings of network services, usually defined in the form of service graphs, associated with resource and bandwidth demands. As the scope of NFV has been expanded in order to meet the requirements of virtualized cellular networks and emerging 5G services, the diversity of resource demands across dimensions, such as CPU, memory, and storage, increased. This requirement exacerbates the already challenging problem of network service embedding (NSE), rendering most existing NSE methods inefficient, as they commonly account for a single resource dimension ( i.e. , typically, the CPU). In this context, we investigate methods for NSE optimization across multiple resource dimensions. To this end, we study a range of multi-dimensional mapping efficiency metrics and assess their suitability for heuristic and exact NSE methods. Utilizing the most suitable and efficient metrics, we propose two heuristics and a mixed integer linear program (MILP) for optimized multi-dimensional NSE. In addition, we devise a virtual network function (VNF) bundling scheme that generates (resource-wise) balanced VNF bundles in order to augment VNF placement. Our evaluation results indicate notable resource efficiency gains of the proposed heuristics compared to a single-dimensional counterpart, as well as a minor degree of sub-optimality in relation to our proposed MILP. We further demonstrate how the bundling scheme affects the embedding efficiency, when coupled with our most efficient heuristic. Our study also uncovers interesting insights and potential implications from the utilization of multi-dimensional metrics within NSE methods. |
| Databáze: | OpenAIRE |
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