Autonomous Configuration of Network Parameters in Operating Systems using Evolutionary Algorithms

TL;DR

This paper presents a genetic algorithm-based system that dynamically tunes Linux network parameters to optimize high-speed network performance, achieving up to 65% throughput improvements.

Contribution

It introduces a novel method using genetic algorithms to automatically optimize Linux network parameters based on traffic history, adaptable to virtual environments.

Findings

Up to 65% increase in network throughput.

Effective tuning of multiple TCP/IP parameters.

Validation across different network scenarios.

Abstract

By default, the Linux network stack is not configured for highspeed large file transfer. The reason behind this is to save memory resources. It is possible to tune the Linux network stack by increasing the network buffers size for high-speed networks that connect server systems in order to handle more network packets. However, there are also several other TCP/IP parameters that can be tuned in an Operating System (OS). In this paper, we leverage Genetic Algorithms (GAs) to devise a system which learns from the history of the network traffic and uses this knowledge to optimize the current performance by adjusting the parameters. This can be done for a standard Linux kernel using sysctl or /proc. For a Virtual Machine (VM), virtually any type of OS can be installed and an image can swiftly be compiled and deployed. By being a sandboxed environment, risky configurations can be tested without the danger of harming the system. Different scenarios for network parameter configurations are thoroughly tested, and an increase of up to 65% throughput speed is achieved compared to the default Linux configuration.