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Mathematics

Quantum Game Theory-Based Cloud Resource Allocation: A Novel Approach

Anjan Bandyopadhyay, Utkarsh Choudhary, Vaibhav Tiwari, Kaushtab Mukherjee, Sapthak Mohajon Turjya, Naim Ahmad, Abid Haleem, Saurav Mallik

Published Apr 24, 2025• 9 citations
Publisher

Abstract

This paper explores the application of quantum game theory to optimize cloud resource allocation. By leveraging the principles of quantum mechanics, the proposed framework aims to enhance efficiency, reduce costs, and improve scalability in cloud computing environments. The study introduces a quantum-based game-theoretic model and compares its performance with classical approaches. The results demonstrate significant improvements in resource utilization and decision-making efficiency. While prior works have explored classical game theory and auction-based methods, this study is among the first to implement quantum game theory in a practical cloud computing context, propose a resource allocation mechanism that incorporates both fairness and efficiency while leveraging the computational advantages of quantum systems, and highlight the strategic benefits of quantum entanglement in fostering collaboration between competing entities in cloud environments. This work not only addresses the current limitations of resource allocation but also redefines the possibilities for optimization in complex systems, making a substantial contribution to both quantum computing and cloud resource management domains.

DOI
10.3390/math13091392

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This paper explores the application of quantum game theory to optimize cloud resource allocation. By leveraging the principles of quantum mechanics, the proposed framework aims to enhance efficiency, reduce costs, and improve scalability in cloud computing environments. The study introduces a quantum-based game-theoretic model and compares its performance with classical approaches. The results demonstrate significant improvements in resource utilization and decision-making efficiency. While prior works have explored classical game theory and auction-based methods, this study is among the first to implement quantum game theory in a practical cloud computing context, propose a resource allocation mechanism that incorporates both fairness and efficiency while leveraging the computational advantages of quantum systems, and highlight the strategic benefits of quantum entanglement in fostering collaboration between competing entities in cloud environments. This work not only addresses the current limitations of resource allocation but also redefines the possibilities for optimization in complex systems, making a substantial contribution to both quantum computing and cloud resource management domains.
Resource allocation in cloud quantum computing environments is a challenging problem due to the combinatorial nature of quantum resources and the strategic behavior of users. This paper presents a novel Game Theoretic Quantum Dynamic Combinatorial Auction (GTQBDCA) framework for efficient and fair allocation of quantum resources in cloud environments. By integrating game theory and dynamic combinatorial auctions, our approach addresses the unique challenges of quantum resource allocation, ensuring optimal resource utilization and user satisfaction. We demonstrate the effectiveness of our framework through theoretical analysis and simulation results.
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Effective and efficient resource allocation procedures are required for the proper integration of quantum resources in the field of cloud computing. This paper focuses on the application of the second price auction and Vickrey-Clarke-Groves (VCG) mechanism to allocate quantum resources within a cloud computing environment. Using this auction mechanism, the proposed model tackles the difficulty caused by the scarcity and complexity of the quantum resources while optimizing resource utilization and cost-effectiveness. Through a detailed mathematical formulation and using a system model, we focus the bidding, allocation, and payment processes under the second price auction using VCG mechanisms. We try to run simulations in order to evaluate its effectiveness with respect to the traditional resource allocation methods. The results we got from this auction demonstrate the efficiency of the model and user satisfaction. We gain knowledge of quantum cloud computing, and it gives us information about the implementation of the auction-based resource allocation mechanism in an actual quantum cloud environment.
Cloud computing has transformed modern digital infrastructure by enabling on-demand access to scalable computational resources. However, the increasing complexity of dynamic workloads and the need for efficient resource allocation present persistent challenges in maintaining performance, cost efficiency, and energy sustainability. This paper introduces a Quantum-Driven Optimization (QDO) framework, a hybrid quantum-classical approach designed to enhance cloud resource allocation by integrating quantum computing techniques with classical optimization methods. The proposed framework leverages Quantum Annealing (QA) and the Variational Quantum Eigensolver (VQE) to optimize resource distribution, minimizing energy consumption and operational costs while maximizing throughput and utilization efficiency. Experimental evaluations demonstrate that the QDO framework achieves a 27% improvement in resource utilization, a 34% reduction in operational costs, and a 21% enhancement in task completion time compared to traditional heuristic-based approaches. Additionally, the hybrid model reduces Service Level Agreement (SLA) violations by 18%, ensuring robust Quality of Service (QoS) for cloud users. The framework employs classical algorithms for preprocessing and decision-making while delegating complex optimization tasks to quantum solvers, ensuring scalability across diverse cloud environments. This study highlights the transformative potential of hybrid quantum-classical computing in addressing cloud resource allocation challenges. The results indicate significant improvements in energy efficiency, cost-effectiveness, and system responsiveness, making the QDO framework a viable solution for next-generation cloud infrastructures. Future research directions include extending the framework to multi-cloud architectures and investigating advanced quantum algorithms for further optimization gains.
Quantum cloud computing will play a vital role in achieving quantum supremacy by providing flexible allocation of resources for applications that demand extensive computational power. Such a platform allows various clients to run their quantum jobs (quantum circuits) without managing the quantum hardware and to pay based on resource usage. Therefore, defining optimal quantum resource allocation is essential to prevent overcharging clients and to help quantum cloud providers maximize resource utilization. Our approach involves examining the problem from a game theory perspective. We introduce a quantum circuit partitioning resource allocation game model (QC-PRAGM) designed to minimize client costs and inter-node communication. This is achieved by solving a convex optimization problem that finds the system's optimal cost while maximizing the number of local gates within a partition through the selection of the best qubit combinations. We demonstrate analytically that clients are charged fairly. Additionally, our simulations show that our solutions outperform traditional methods.
Cloud computing has fundamentally transformed digital infrastructure by providing on-demand access to scalable computational resources. Yet the inherent unpredictability of workloads alongside surging demands for efficient resource distribution continues to present substantial operational hurdles. This investigation introduces a Quantum-Driven Optimization framework that synergistically combines quantum and classical computational paradigms to improve resource allocation efficacy within cloud ecosystems. The proposed methodology employs Quantum Annealing alongside Variational Quantum Eigensolver algorithms to optimize resource distribution through simultaneous minimization of energy expenditure and operational overhead while maximizing throughput and utilization rates. Empirical validation demonstrates that our framework achieves measurable improvements over conventional heuristic methodologies, specifically showing enhanced resource utilization, reduced operational costs, and accelerated task completion times. The hybrid architecture effectively distributes computational burden by delegating preprocessing and decision-making functions to classical systems while quantum processors tackle inherently complex optimization problems. The solution demonstrates scalability and adaptability across diverse cloud deployment scenarios while exhibiting reduced Service Level Agreement violations, thereby ensuring superior Quality of Service for end-users. This research contributes meaningful insights regarding the practical implementation of hybrid quantum-classical approaches for addressing contemporary resource allocation challenges within cloud computing infrastructures.