Task scheduling is one of the crucial agendas in cloud computing technology. The system consists of various tasks namely termed cloudlets which need to be fed among various processing units for its execution. However, the algorithm that has been used to schedule tasks among various processing units should provide the optimum solution to a given problem. This research realizes the power of quantum computation specifically quantum annealing to provide the optimum solution to schedule the tasks among various processing units under the given constraints. Quantum Annealing is used for combinatorial optimization problems and as task scheduling is also one of such problems, it can be done in the quantum environment for optimal results. The Hybrid Solver provided by the D-Wave Quantum Computer has been used to solve the given problem.Leveraging the D-Wave Quantum Annealer, we analyze various scheduling scenarios and evaluate the performance in terms of makespan and throughput. Our findings reveal substantial improvements in scheduling efficiency compared to Partical Swarm Optimization scheduling. Specifically, we observed a notable reduction in makespan by up to 70% across diverse workload scenarios. Moreover, there was not much difference in throughput provided by both algorithms. The task scheduling among various processing units using quantum annealing has provided significant differences in makespan. Thus, using an annealing technique to schedule the tasks in cloud computing would significantly enhance in load balancing and efficient use of resources.
Task scheduling is one of the crucial agendas in cloud computing technology. The system consists of various tasks namely termed cloudlets which need to be fed among various processing units for its execution. However, the algorithm that has been used to schedule tasks among various processing units should provide the optimum solution to a given problem. This research realizes the power of quantum computation specifically quantum annealing to provide the optimum solution to schedule the tasks among various processing units under the given constraints. Quantum Annealing is used for combinatorial optimization problems and as task scheduling is also one of such problems, it can be done in the quantum environment for optimal results. The Hybrid Solver provided by the D-Wave Quantum Computer has been used to solve the given problem.Leveraging the D-Wave Quantum Annealer, we analyze various scheduling scenarios and evaluate the performance in terms of makespan and throughput. Our findings reveal substantial improvements in scheduling efficiency compared to Partical Swarm Optimization scheduling. Specifically, we observed a notable reduction in makespan by up to 70% across diverse workload scenarios. Moreover, there was not much difference in throughput provided by both algorithms. The task scheduling among various processing units using quantum annealing has provided significant differences in makespan. Thus, using an annealing technique to schedule the tasks in cloud computing would significantly enhance in load balancing and efficient use of resources.
Test automation plays a crucial role in modern software development, ensuring faster releases and higher software quality. However, optimizing parallel test execution remains a challenge due to resource constraints and scheduling inefficiencies. This research explores the potential of quantum computing, specifically quantum annealing using D-Wave systems, to enhance test execution efficiency. By formulating test suite scheduling as a combinatorial optimization problem, we leverage quantum annealing to achieve optimal test distribution across available resources. Our proposed approach significantly reduces execution time and improves resource utilization compared to classical optimization techniques. Experimental results demonstrate the effectiveness of quantum-enhanced scheduling, highlighting the potential of quantum computing in revolutionizing test automation.
Task scheduling with the cloud computing domain constitutes a significant challenge, especially when the objective is to improve energy efficiency without detracting from performance metrics. Traditional optimization methodologies, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), frequently encounter difficulties related to scalability and convergence velocity in expansive, dynamic cloud ecosystems. This manuscript introduces a quantum-assisted hybrid paradigm that amalgamates the Quantum Approximate Optimization Algorithm (QAOA) with the Variational Quantum Eigensolver (VQE) to facilitate energyefficient task scheduling within cloud computing platforms. The QAOA is utilized to investigate near-optimal mappings of tasks to resources by addressing combinatorial scheduling dilemmas, whereas the VQE refines the energy consumption model via variational circuit optimization techniques. Compared to classical metaheuristic approaches, the proposed hybrid framework exhibits enhanced efficiency in minimizing energy consumption by 23.6 %, decreasing the makespan by 17.4 %, and increasing resource utilization by 17.7 %. Empirical findings derived from a quantum simulator also indicate a 36.6 % acceleration in convergence relative to GA and a 10.3% improvement in solution accuracy when juxtaposed with PSO. These findings substantiate the premise that quantum-assisted hybrid optimization presents a viable path towards the realization of sustainable and highperformance cloud computing infrastructures.
This study investigates the application of quantum annealing in solving logistics and scheduling problems, areas traditionally challenged by combinatorial complexity. Using a case study approach, quantum annealing was applied to optimize delivery routing and job-shop scheduling problems. The methodology incorporated D-Wave's quantum processing unit (QPU), and performance was benchmarked against classical simulated annealing. Results indicate a significant improvement in computational efficiency and solution optimality, particularly for mid-sized problem instances. The research identifies key parameters influencing performance and provides insights into hybrid quantum-classical integration. These findings contribute to the growing field of quantum computing applications in operations research. Keywords: Quantum annealing, logistics optimization, scheduling algorithms, quantum computing, operations research
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.
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.
Task scheduling is a critical problem within the modern underlying computing infrastructure, where resource overload causes a direct impact on the cost and time performance metrics, leading to increased latency, excessive financial cost, and overall poor throughput. Traditional scheduling algorithms are normally fixed and lack the strong exploration and exploitation processes, hence limiting their effectiveness when faced with large problems in scheduling, which are NP-hard. The current work investigates Quantum Computing (QC)-algorithms including Grover, the Variational Quantum Eigensolver (VQE) and Quantum Annealing (QA). This research work includes an extensive experiment conducted in the WorkflowSim simulator with heterogeneous task-scheduling performed with diverse Virtual Machine (VM) configurations. The performance-metrics considered for their comparison include cost and time, along with Relative Performance Index (RPI). The experimental results indicate that QC-algorithm QA achieves higher cost-efficiency, with an average cost reduction of 4 % compared to Grover and 25% compared to VQE, with a corresponding average reduction in time of about 14 % compared to Grover. VQE provides competitive behavior with particular VM configurations but provides poor stability behavior with a wider range of parameters. Statistical evaluations using five regression provides the Grover algorithm to have a superior predictive stability and stronger goodness-of-fit concerning cost and time compared to QA and VQE. Future research will be directed towards the design of hybrid quantum-classical scheduling systems and inclusion of additional performance indicators (including energy efficiency and throughput) and testing the mentioned systems on real quantum equipment to evaluate scalability and real-world feasibility in computational environments.