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International Conference on Computational Intelligence and Communication Technologies (CCICT)

Quantum Resource Allocation through Second-Price Auctions: A Hybrid Classical-Quantum Approach

Bodhdipta Roy, Koushiki Das, Biswarupayan Dutta, Harish Mittal, Anjan Bandyopadhyay, Neeraj Sharma

Published Apr 11, 2025• 0 citations
Publisher

Abstract

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.

DOI
10.1109/ccict65753.2025.00053

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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.
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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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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.
Abstract Integrating quantum network optimization with traditional cloud computing presents a groundbreaking approach to enhancing computational efficiency and resource management. This study investigates the quantifiable impact of quantum-classical hybrid systems in cloud environments, focusing on resource utilization enhancement and task scheduling optimization. Using a MATLAB-based simulation environment interfaced with Variational Quantum Eigensolver (VQE) simulations, this research implements a three-layer architecture across 50 nodes, combining quantum optimization algorithms with classical scheduling techniques through a GPS queue model. The hybrid quantum-classical framework achieves remarkable improvements, including a 180% increase in task throughput, 70% reduction in response time, 41.5% improvement in resource utilization, and 27.1% enhancement in energy efficiency while maintaining 85% efficiency under peak load conditions and demonstrating 99.5% success rate for short-term tasks and 97.8% for long-running operations. Despite hardware limitations of existing quantum processors and scalability constraints, the system establishes a foundation for future developments, including advanced error correction mechanisms, expanded system scalability beyond 50 nodes, and enhanced resource utilization. These findings establish quantum-enhanced cloud computing as a viable solution for next-generation cloud infrastructure, particularly in managing complex workloads and optimizing resource allocation.
This paper presents a novel quantum-inspired algorithm designed for optimal resource allocation within cloud computing environments. Recognizing the potential of quantum superposition to explore multiple solutions concurrently, the proposed algorithm simulates this effect through a weighted sum approach. The core of the method involves evaluating various resource allocation scenarios simultaneously, assigning weights reflecting the desirability of each outcome. This parallel exploration significantly reduces the computational time required to find the optimal allocation compared to traditional, sequential methods. The algorithm's effectiveness is demonstrated through a theoretical framework and a conceptual implementation, highlighting its potential for substantial efficiency gains in dynamic cloud resource management. The algorithm aims to capture the essence of quantum exploration without the need for actual quantum hardware, offering a pragmatic solution for complex optimization problems.