Distributed computing model for channel bandwidth allocation and optimization using machine learning techniques

A distributed computing model for channel bandwidth allocation and optimization can involve multiple components working together to efficiently allocate and optimize the available bandwidth in a distributed system. The efficient allocation of channel bandwidth in the distributed computing model is crucial for optimizing resource utilization and improving system performance. This paper, proposed the Imperialist Competitive Spline Interpolation (ICSI) scheme, which combines computational intelligence and deep learning techniques to address the challenge of channel bandwidth allocation. The ICSI scheme optimizes resource allocation by considering user requirements and resource availability, utilizing polynomial equations and spline interpolation. The Imperialist Competitive Optimization model evaluates and optimizes the available resources in the distributed environment. With the optimized resources spline interpolation is implemented for the computation of the available resources. Extensive simulations and performance analysis demonstrate the effectiveness of the ICSI scheme in terms of resource utilization, throughput, latency, fairness index, and energy efficiency. The ICSI model achieves the minimal waiting time of 3 ms and minimal latency of 6.4 m. Comparative analysis of the Round Robin scheme further confirms the superiority of the ICSI scheme in terms of task scheduling efficiency. The findings of this paper contribute to the advancement of distributed computing models for channel bandwidth allocation, offering a promising solution for optimizing resource allocation and improving system performance in modern computing environments.