Framework for the comparative analysis of networking software libraries and its application to C++ networking solutions

The objective evaluation of networking libraries remains a critical challenge in software engineering, as inconsistent methodologies and application-specific code often obscure meaningful performance and usability differences. The aim of this study was to conduct a comprehensive comparison of three C++ networking libraries (Boost.Asio, Boost.Beast, and Poco.Net) based on the proposed universal framework. The research methodology included the development of a universal comparative framework, experimental implementation of software prototypes, load testing to collect quantitative indicators, and a structured qualitative assessment based on a number of defined criteria. The framework encompassed both quantitative metrics (throughput, latency and resource consumption) and qualitative criteria (Application Programming Interface ergonomics, feature completeness, stability, cross platform compatibility and development complexity), with all libraries assessed under identical software conditions. Using each library, two C++ client-server prototypes were implemented: the RESTful service ResourceMonitor and the real time streaming application GameOfLifeStreaming. The project structure was unified to eliminate variability in application logic and to focus analysis exclusively on library behaviour. Boost.Asio demonstrated advantages in latency sensitive scenarios and scenarios requiring fine grained control. Boost.Beast offered effective HTTP support with minimal performance overhead but limited protocol coverage. Poco.Net achieved the lowest memory footprint and maintained stable performance at high loads while supporting the widest range of protocols, albeit at the expense of higher latency and lower raw throughput, and requiring more complex configuration. The practical contribution lies in the reusable framework for evaluating other networking libraries, the integration of created components into diverse development workflows, and the provided guidelines for choosing the most appropriate library

Authors

Publication Details

Journal
Вісник Черкаського державного технологічного університету
Published
2025-08-10
DOI
https://doi.org/10.62660/bcstu/3.2025.10
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Framework for the comparative analysis of networking software libraries and its application to C++ networking solutions

Kostiantyn Zhereb, Yehor Hrushevyy
Вісник Черкаського державного технологічного університету
Distributed and Parallel Computing Systems
article

Framework for the comparative analysis of networking software libraries and its application to C++ networking solutions

Kostiantyn Zhereb, Yehor Hrushevyy
article en

Abstract

The objective evaluation of networking libraries remains a critical challenge in software engineering, as inconsistent methodologies and application-specific code often obscure meaningful performance and usability differences. The aim of this study was to conduct a comprehensive comparison of three C++ networking libraries (Boost.Asio, Boost.Beast, and Poco.Net) based on the proposed universal framework. The research methodology included the development of a universal comparative framework, experimental implementation of software prototypes, load testing to collect quantitative indicators, and a structured qualitative assessment based on a number of defined criteria. The framework encompassed both quantitative metrics (throughput, latency and resource consumption) and qualitative criteria (Application Programming Interface ergonomics, feature completeness, stability, cross platform compatibility and development complexity), with all libraries assessed under identical software conditions. Using each library, two C++ client-server prototypes were implemented: the RESTful service ResourceMonitor and the real time streaming application GameOfLifeStreaming. The project structure was unified to eliminate variability in application logic and to focus analysis exclusively on library behaviour. Boost.Asio demonstrated advantages in latency sensitive scenarios and scenarios requiring fine grained control. Boost.Beast offered effective HTTP support with minimal performance overhead but limited protocol coverage. Poco.Net achieved the lowest memory footprint and maintained stable performance at high loads while supporting the widest range of protocols, albeit at the expense of higher latency and lower raw throughput, and requiring more complex configuration. The practical contribution lies in the reusable framework for evaluating other networking libraries, the integration of created components into diverse development workflows, and the provided guidelines for choosing the most appropriate library

Вісник Черкаського державного технологічного університету
Openalex Percentile: Top 68%
Distributed and Parallel Computing Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.