Automatic Analysis, Decomposition and Parallel Optimization of Large Homogeneous Networks

TL;DR

This paper introduces a novel meta-algorithm for analyzing, decomposing, and parallel optimizing large homogeneous networks, significantly improving efficiency in processing and optimizing complex systems like communication networks.

Contribution

The paper presents a new meta-algorithm that decomposes large networks into subnets and applies parallel optimization, adaptable to various network types including neural and biological systems.

Findings

Increased speed of parallel optimization demonstrated on wireless networks

Effective decomposition into loosely connected subnets

Applicable to diverse network systems

Abstract

The life of the modern world essentially depends on the work of the large artificial homogeneous networks, such as wired and wireless communication systems, networks of roads and pipelines. The support of their effective continuous functioning requires automatic screening and permanent optimization with processing of the huge amount of data by high-performance distributed systems. We propose new meta-algorithm of large homogeneous network analysis, its decomposition into alternative sets of loosely connected subnets, and parallel optimization of the most independent elements. This algorithm is based on a network-specific correlation function, Simulated Annealing technique, and is adapted to work in the computer cluster. On the example of large wireless network, we show that proposed algorithm essentially increases speed of parallel optimization. The elaborated general approach can be used for analysis and optimization of the wide range of networks, including such specific types as artificial neural networks or organized in networks physiological systems of living organisms.