Virtual Network Functions Scheduling Under Delay-Weighted Pricing

Abstract

We consider a cost minimization problem for High Volume Servers (HVS) equipped with Virtual Machines (VMs) to serve Virtual Network Functions (VNF) demands for resources. Given a scheduling period, a central scheduler decides which VM to use for each VNF demand. Each VM can be activated or disabled with different costs. Each VNF has a delay-weighted pricing function to indicate its completion time tolerance. We prove the NP-completeness of the problem and develop an algorithm based on Lagrangian relaxation and subgradient optimization to deal with this computational complexity. Finally, our numerical results demonstrate our algorithm’s effectiveness compared to two benchmarks.