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Network Service Embedding Across Multiple Resource Dimensions
IEEE Transactions on Network and Service Management ( IF 5.3 ) Pub Date : 2020-12-15 , DOI: 10.1109/tnsm.2020.3044614
Angelos Pentelas , George Papathanail , Ioakeim Fotoglou , Panagiotis Papadimitriou

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.

中文翻译:

跨多个资源维度嵌入网络服务

网络功能虚拟化(NFV)带来了对网络服务的有效嵌入的需求,通常以与资源和带宽需求相关的服务图的形式定义网络服务。随着NFV范围的扩大,以满足虚拟化蜂窝网络和新兴的5G服务的需求,跨维度(如CPU,内存和存储)的资源需求的多样性也在增加。这项要求加剧了本已充满挑战的网络服务嵌入(NSE)问题,使大多数现有的NSE方法效率低下,因为它们通常会占用一个资源维度( IE ,通常是CPU)。在这种情况下,我们研究了跨多个资源维度进行NSE优化的方法。为此,我们研究了一系列多维制图效率指标,并评估了它们对启发式和精确NSE方法的适用性。利用最合适,最有效的指标,我们提出了两种启发式方法和混合整数线性程序(MILP),以优化多维NSE。此外,我们设计了一种虚拟网络功能(VNF)捆绑方案,该方案会生成(在资源方面)平衡的VNF捆绑包,以增加VNF的位置。我们的评估结果表明,与一维方法相比,所提出的启发式方法具有显着的资源效率提高,并且相对于我们所提出的MILP,次优程度较小。当结合我们最有效的启发式方法时,我们进一步演示了捆绑方案如何影响嵌入效率。我们的研究还发现了有趣的见解和NSE方法中多维指标的利用所带来的潜在影响。
更新日期:2020-12-15
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