Virtual Network Embedding Approximations: Leveraging Randomized Rounding [article]

Matthias Rost, Stefan Schmid
2018 arXiv   pre-print
The Virtual Network Embedding Problem (VNEP) captures the essence of many resource allocation problems of today's infrastructure providers, which offer their physical computation and networking resources to customers. Customers request resources in the form of Virtual Networks, i.e. as a directed graph which specifies computational requirements at the nodes and communication requirements on the edges. An embedding of a Virtual Network on the shared physical infrastructure is the joint mapping
more » ... (virtual) nodes to physical servers together with the mapping of (virtual) edges onto paths in the physical network connecting the respective servers. This work initiates the study of approximation algorithms for the VNEP. Concretely, we study the offline setting with admission control: given multiple request graphs the task is to embed the most profitable subset while not exceeding resource capacities. Our approximation is based on the randomized rounding of Linear Programming (LP) solutions. Interestingly, we uncover that the standard LP formulation for the VNEP exhibits an inherent structural deficit when considering general virtual network topologies: its solutions cannot be decomposed into valid embeddings. In turn, focusing on the class of cactus request graphs, we devise a novel LP formulation, whose solutions can be decomposed into convex combinations of valid embedding. Proving performance guarantees of our rounding scheme, we obtain the first approximation algorithm for the VNEP in the resource augmentation model. We propose two types of rounding heuristics and evaluate their performance in an extensive computational study. Our results indicate that randomized rounding can yield good solutions (even without augmentations). Specifically, heuristic rounding achieves 73.8% of the baseline's profit, while not exceeding capacities.
arXiv:1803.03622v2 fatcat:l7k2izg4sbf3fmfghknd7tsoai