CoShare: An Efficient Approach for Redundancy Allocation in NFV

An appealing feature of Network Function Virtualization (NFV) is that in an NFV-based network, a network function (NF) instance may be placed at any node. On the one hand this offers great flexibility in allocation of redundant instances, but on the other hand it makes the allocation a unique and difficult challenge. One particular concern is that there is inherent correlation among nodes due to the structure of the network, thus requiring special care in this allocation. To this aim, our novel approach, called CoShare, is proposed. Firstly, its design takes into consideration the effect of network structural dependency, which might result in the unavailability of nodes of a network after failure of a node. Secondly, to efficiently make use of resources, CoShare proposes the idea of shared reservation, where multiple flows may be allowed to share the same reserved backup capacity at an NF instance. Furthermore, CoShare factors in the heterogeneity in nodes, NF instances and availability requirements of flows in the design. The results from a number of experiments conducted using realistic network topologies show that the integration of structural dependency allows meeting availability requirements for more flows compared to a baseline approach. Specifically, CoShare is able to meet diverse availability requirements in a resource-efficient manner, requiring, e.g., up to 85% in some studied cases, less resource overbuild than the baseline approach that uses the idea of dedicated reservation commonly adopted for redundancy allocation in NFV

Measures for Network Structural Dependency Analysis

A set of new measures for network structural dependency analysis is introduced. These measures are based on geodesic distance, which is the number of links in a shortest path. They capture the structural dependency effect at the path level, the node level, and the overall network level, and hence can be used to index such dependencies. Unlike the related literature measures, a novel aspect of the proposed measures is that the impact of network fragmentation caused by a node failure is taken into explicit consideration in deciding the structural dependency effect. As a result, when applied to critical node identification in a network, the proposed measures give results that are more in line with intuition