Optimization of Resource Allocation using Quantum Genetic Algorithm for Cloud Data

The term "optimization" refers to the process of identifying the best solution for a given problem. Optimization techniques such as TSP, categorization, scan, and ERP systems are used to solve a variety of real-world problems. Scheduling is an important domain of the optimization technique, and it is one of their primary application fields. The primary goal of this paper is to investigate and optimize cloud computing-based resource scheduling techniques. As a result, this paper proposes a quantum genetics-based resource allocation optimization strategy. The technique starts with quantum physics for encoding and population generation, then moves on to a genetic algorithm for searching the generated population. Following a search, the genetic algorithm returns resource sequences that match the search criteria, as well as a task list. The CPU consumption cycles are efficiently minimized by using the resulting sequences. The CloudSim simulation tool is also used to test the efficacy of the proposed algorithm for resource scheduling. Finally, the system's output is determined by the algorithm's time complexity, space complexity, and CPU usage cycles. A comparison analysis of historically available resource allocation techniques is also carried out and compared to similar criteria. When compared to conventional resource allocation techniques, experimental findings indicate that the proposed quantum genetics concept provides superior efficiency.