Classical and Quantum Solvers for Joint Network/Servers Power Optimization

TL;DR

This paper develops models for joint network and server power optimization in virtual data centers, comparing classical and quantum solvers to address complex resource management challenges in telecommunications.

Contribution

It introduces ILP, binary, and QUBO models for energy-efficient resource consolidation, and analyzes the computational complexity of classical versus quantum optimization methods.

Findings

QUBO model suitable for quantum algorithms

Theoretical comparison of classical and quantum solver complexities

Framework for energy-efficient virtual data center management

Abstract

The digital transformation that Telecommunications and ICT domains are crossing today, is posing several new challenges to Telecom Operators. These challenges require solving complex problems such as: dimensioning and scheduling of virtual/real resources in data centers; automating real-time management/control and orchestration of networks processes; optimizing energy consumption; and overall, ensuring networks and services stability. These problems are usually tackled with methods and algorithms that find suboptimal solutions, for computational efficiency reasons. In this work, we consider a Virtual Data Center scenario where virtual machine consolidation must be performed with joint minimization of network/servers power consumption. For this scenario, we provide an ILP model, the equivalent binary model and the steps towards the equivalent Quadratic Unconstrained Binary Optimization (QUBO) model that is suitable for being solved by means of quantum optimization algorithms. Finally, we compare the computational complexity of classical and quantum solvers from a theoretical perspective.