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Lahore Garrison University Research Journal of Computer Science and Information Technology

A Quantum Optimization Model for Dynamic Resource Allocation in Cloud Computing

Tahir Alyas

Published Mar 31, 2017• 0 citations
Publisher

Abstract

Quantum Computing and Cloud Computing technologieshave potential capability to change the dynamic of futurecomputing. Similarly, both Complexities, time and Space are the basicconstraints which can determine the efficient cloud service performance.Quantum optimization for the cloud resources in dynamic environmentprovides a way to deal with the present classical cloud computationmodel’s challenges. By combining the fields of quantum computing andcloud computing, will result in evolutionary technology. Virtual resourceallocation is a major challenge facing cloud computing with dynamiccharacteristics, a single aspect for the evaluation of resource allocationstrategy cannot satisfy the real world demands in this case. QuantumOptimization resource allocation mechanism on the cloud computingenvironment based two-way factors, improving user satisfaction and bestuse of resource utilization of cloud computing systems.A dynamic resource allocation mechanism for cloud services, based onnegotiation by keeping the focus on preferences and pricing factor istherefore proposed.

DOI
10.54692/lgurjcsit.2017.01015

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Quantum Computing and Cloud Computing technologieshave potential capability to change the dynamic of futurecomputing. Similarly, both Complexities, time and Space are the basicconstraints which can determine the efficient cloud service performance.Quantum optimization for the cloud resources in dynamic environmentprovides a way to deal with the present classical cloud computationmodel’s challenges. By combining the fields of quantum computing andcloud computing, will result in evolutionary technology. Virtual resourceallocation is a major challenge facing cloud computing with dynamiccharacteristics, a single aspect for the evaluation of resource allocationstrategy cannot satisfy the real world demands in this case. QuantumOptimization resource allocation mechanism on the cloud computingenvironment based two-way factors, improving user satisfaction and bestuse of resource utilization of cloud computing systems.A dynamic resource allocation mechanism for cloud services, based onnegotiation by keeping the focus on preferences and pricing factor istherefore proposed.
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.
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 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.
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.
Quantum computing holds immense promise for solving complex problems intractable for classical computers. However, realizing this potential hinges critically on efficient resource allocation. This paper addresses the optimization of resource allocation in quantum computing, specifically focusing on problems like quantum circuit design and qubit allocation. We propose a methodology leveraging optimization techniques, primarily linear and integer programming, to determine optimal resource configurations. The core claim is the development of an effective method for this type of optimization. The mechanism involves formulating the resource allocation problem as a mathematical optimization problem and utilizing established techniques to find the optimal solution. This research contributes to the burgeoning field by providing a structured approach to tackling the fundamental challenge of resource allocation, aiming for efficiency and optimality in quantum computational tasks. The primary objective is to present a framework for systematically addressing resource allocation, a critical factor in the successful deployment of quantum computing technologies.
This paper presents a novel approach to resource allocation and scheduling within quantum computing environments. Recognizing the inherent scarcity and unique characteristics of quantum resources, such as qubits and quantum gates, we propose a dynamic algorithm designed to optimize resource utilization and consequently enhance computational efficiency. The core of the algorithm lies in a mechanism that intelligently balances the demands of diverse quantum computing tasks. We formalize this approach using mathematical notation, detailing the key components including task prioritization, qubit assignment, gate scheduling, and dynamic adjustment based on real-time system metrics. The proposed algorithm addresses limitations within existing static scheduling methods, offering improved performance in complex quantum workflows. The design incorporates a queuing theory-inspired model to manage task flow and a stochastic optimization technique for adaptive resource allocation. Simulation results (presented conceptually here) demonstrate the algorithm's potential to significantly improve quantum computing performance compared to traditional approaches. Further research will focus on formal verification and optimization for specific quantum computing architectures.