It's Good to Relax: Fast Profit Approximation for Virtual Networks with Latency Constraints

by   Robin Münk, et al.

This paper proposes a new approximation algorithm for the offline Virtual Network Embedding Problem (VNEP) with latency constraints. Given is a set of virtual networks with computational demands on nodes and bandwidth demands together with latency bounds on the edges. The VNEP's task is to feasibly embed a subset of virtual networks on a shared physical infrastructure, e.g., a data center, while maximizing the attained profit. In contrast to existing works, our approximation algorithm AFlex allows for (slight) violations of the latency constraints in order to greatly lower the runtime. To obtain this result, we use a reduction to the Restricted Shortest Path Problem (RSP) and leverage a classic result by Goel et al. We complement our formal analysis with an extensive simulation study demonstrating the computational benefits of our approach empirically. Notably, our results generalize to any other additive edge metric besides latency, including loss probability.



There are no comments yet.


page 7


Virtual Network Embedding Approximations: Leveraging Randomized Rounding

The Virtual Network Embedding Problem (VNEP) captures the essence of man...

On the Approximability and Hardness of the Minimum Connected Dominating Set with Routing Cost Constraint

In the problem of minimum connected dominating set with routing cost con...

New Approximation Algorithms for Maximum Asymmetric Traveling Salesman and Shortest Superstring

In the maximum asymmetric traveling salesman problem (Max ATSP) we are g...

Reducing Path TSP to TSP

We present a black-box reduction from the path version of the Traveling ...

Multi-Robot Routing for Persistent Monitoring with Latency Constraints

In this paper we study a multi-robot path planning problem for persisten...

Privacy preserving clustering with constraints

The k-center problem is a classical combinatorial optimization problem w...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.