Mathematical Model and Statistical Assessment of Non Linear Scheduling Optimization in Cloud Environment using Improved Cuckoo Search Algorithm

Abstract

The scheduling optimization subject is indeed one of the most effective solutions in cloud system area to ensure the high systems performance and resource usage. The novelty presented in this study is a mathematical model that draws on an Improved Cuckoo Search Algorithm as a scheduling non-linear optimizer for the cloud computing environment. The model under consideration takes into account the underlying complications as well as the varying nature of the use of cloud resources, primarily addressing the latency issue with priority given to the increase of the throughput. Improved Cuckoo Search (ICS) Algorithm, developed on 'cuckoo search' algorithm, uses adaptive step size and probabilistic switching, which, in turn, ensure that the solution space is both being properly examined and exploited. The applications’ utilization of the scheduling mathematical model is based on an elaborately designed theoretical background representing the task as a non-linear optimization problem that meets all the specific constraints and objectives important in cloud computing. Statistical reassessment involves the comparison of the proposed model with standard benchmarks. The proposed model superiority is evident in terms of convergence speed, solution quality and robustness against diverse workloads, as shown in varied tests. The findings of this research contributes to the field by deriving a specialized tool commonly used by the cloud service provider in designing resource scheduling, enhancing the service delivery and operational efficiency.