Innovative quantum techniques for improving system performance in cloud computing

Abstract

Effective task scheduling is pivotal for optimizing the performance of cloud computing services, particularly to minimize execution time and enhance resource utilization. Traditional approaches often focus on single-objective metrics, such as task completion time, or fail to address the intricate interdependencies between multiple objectives. To overcome these limitations, we introduce QISPF, a novel multi-objective task scheduling algorithm that combines genetic algorithms with innovative quantum techniques. QISPF is designed to achieve an optimal task distribution by addressing key performance metrics makespan, scheduling length, throughput, resource utilization, energy consumption, and load balancing, through a unified measure known as system performance. QISPF leverages quantum techniques to enhance the traditional genetic algorithm framework by incorporating principles from quantum mechanics, such as probabilistic quantum encoding and superposition. The simulations were conducted for two cases. The first had 100 tasks and anything from 10 to 50 virtual machines. Furthermore, in the second case, there were a certain number of virtual machines (VMs), with the number of tasks ranging from 500 to 1000. The simulation results demonstrated the scheduling efficiency of QISPF compared to the G-MOTSA, ETVMC, TSACS, and ACO algorithms. QISPF offers a more powerful approach to exploring and exploiting the solution space. This novel method allows for a richer representation of potential solutions and improves the algorithm’s ability to find high-quality solutions in complex problem landscapes.