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.
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.
Abstract: This study investigates a quantum-inspired optimisation framework for energy-aware virtual machine (VM) placement in cloud data centres based on a Quadratic Unconstrained Binary Optimisation (QUBO) formulation. The proposed approach encodes the VM–host assignment problem into a QUBO Hamiltonian that jointly minimises energy consumption and penalises service level agreement (SLA) violations and capacity constraint violations. Quantum-inspired solvers implemented using TensorFlow Quantum and Qiskit are employed to approximate low-energy configurations, which translate to near-optimal placement decisions. The framework is evaluated on realistic workload traces calibrated to empirical characteristics of production cloud environments and executed on a heterogeneous testbed of physical servers. Experimental results demonstrate that the quantum-inspired QUBO optimisation achieves up to 17.8% lower energy consumption compared with a power-aware best-fit decreasing heuristic, while simultaneously reducing SLA violations by 6.6%. The results further show that the QUBO-based approach provides more stable server utilisation profiles and a higher degree of VM consolidation without incurring excessive migration overhead. These findings indicate that quantum-inspired optimisation, even when executed on classical hardware through TensorFlow Quantum and Qiskit simulators, offers a promising direction for improving the sustainability and efficiency of large-scale cloud data centres. Keywords Quantum-inspired optimisation; QUBO; energy-aware VM placement; TensorFlow Quantum; Qiskit; cloud data centres.
Optimization is a crucial challenge across various domains, including finance, resource allocation, and mobility. Quantum computing has the potential to redefine the way we handle complex problems by reducing computational complexity and enhancing solution quality. Optimization, particularly of objective functions, stands to benefit significantly from quantum solvers, which leverage principles of quantum mechanics like superposition, entanglement, and tunneling. The Ising and Quadratic Unconstrained Binary Optimization (QUBO) models are the most suitable formulations for these solvers, involving binary variables and constraints treated as penalties within the overall objective function. To harness quantum approaches for optimization, two primary strategies are employed: exploiting quantum annealers—special-purpose optimization devices—and designing algorithms based on quantum circuits. This review provides a comprehensive overview of quantum optimization methods, examining their advantages, challenges, and limitations. It demonstrates their application to real-world scenarios and outlines the steps to convert generic optimization problems into quantum-compliant models. Lastly, it discusses available tools and frameworks that facilitate the exploration of quantum solutions for optimization tasks.
As systems become more complex and large-scale, there is a need for more effective scheduling algorithms to maximize resource usage and minimize job completion times. The older methods of scheduling like First-Come-First-Serve (FCFS) and Round Robin (RR), although easier to compute, tend to perform poorly in dynamic scenarios because they cannot manage intricate constraints and multi-objective optimization efficiently. As traditional methods approach their limit of performance, quantum computing presents a potentially fruitful avenue for conquering combinatorial challenges based on its ability to examine many states at once. This work proposes a quantum-aided scheduling framework that employs Quadratic Unconstrained Binary Optimization (QUBO) formulations and quantum solvers for the purpose of improving job scheduling in distributed environments. The original contribution is the marrying of quantum optimization (with IBM Qiskit's QAOA and D-Wave's hybrid solvers) with traditional distributed computing frameworks such as Apache Spark. Google Cluster Workload Traces are employed to mimic real-world situations, and jobs are described by CPU, memory requirements, and arrival times. Once the scheduling problem has been formulated into a QUBO model, quantum algorithms produce the optimized job-to-machine assignments. The outcomes are compared against baseline classical schedulers on performance metrics including make span, throughput, and resource utilization. The experiment demonstrates that quantum-assisted scheduling significantly improves makespan and enhances resource utilization, particularly under high workloads. It also provides evidence of the feasibility of integrating quantum solvers with existing big data infrastructures.
Optimization of electricity surplus is a crucial element for transmission power networks since it leads to reducing costs as well as increasing efficiency across the network as a whole. In this paper, we show how to optimize such network costs using a quantum annealing approach. First, we define the Quadratic Unconstrained Binary Optimization (QUBO) problem for network partitioning. To achieve this, we introduce a method to translate inequality constraints with real-valued coefficients into approximate penalty functions. Next, we test the implementation on purely quantum and quantum-classical hybrid architectures. We then solve the problem using the D-Wave hybrid Constrained Quadratic Model (CQM) solver, the D-Wave hybrid Binary Quadratic Model (BQM) solver, as well as classical solvers available on Azure Quantum Cloud. Finally, we find that the value of the objective function obtained with the quantum-classical hybrid solvers is always lower compared to the classical approaches across a range of different problem sizes. This demonstrates that the quantum-classical hybrid methods outperform the classical methods in terms of solution quality.
Data centers are deemed essential infrastructures for the digital economy and a wide variety of operational activities such as cloud computing & Artificial Intelligence, big data analytics, e-commerce and even more. But at the same time, they are also among the biggest electrical power consumers in the current active industries as their thousands of servers and networking tools demand much electricity consumption. Of these energy requirements, cooling systems are the most important because they account for one-third and one-half of the overall energy costs across most structures. This energy is used chiefly to regulate the ambient operating temperatures to guarantee that servers work correctly without experiencing a thermal malfunction. This need to reduce energy consumption in data centers is especially timely given current forecasts for the energy consumption of data centers globally to rise, alongside the expansion of the digital environment, becoming a significant menace to sustainable development where minds must devise new effective solutions to the problem. This paper reports on using quantum optimization to transform the cooling mechanism under data centers. Quantum computing, especially by quantum annealing, presents exceptional capabilities to address difficult OPs that are not feasibly solvable by any classical computing system. Following the principles of quantum mechanics, the present work suggests the quantum-classical model to achieve the best thermal distribution and improve the cooling systems. Reliant on quantum annealing for the recognition of topologies of cooling resources like opportunities for fresh airflow, location of server racks, and temperature management, other classical machine learning models offer real-time suggestions about what adjustments should be made. Playing out experiments proves quite effective, with fifty to thirty potential energy-saving percentages feasible for this strategy. This research focuses on the machining capability of quantum computing that could help develop efficient green technologies for data centers to meet the increasing global demands of the digital era.
Energy communities are collectives that manage their own production, distribution, and consumption of energy, usually from renewable sources. To increase efficiency and sustainability, the scheduling of the loads on the basis of the user needs, the electricity prices, the availability of local renewable energy and the battery storage capacity must be optimized. Quantum computing, leveraging principles like superposition and entanglement, offers new computational power for complex optimization tasks. This paper lays the basis for exploring the potential of quantum computing in optimizing the operation of energy communities by modeling the problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem.