Evaluating the Efficiency of Zlib for Real-Time Communication: A Comprehensive Analysis


Authors : Prayrit Jain

Volume/Issue : Volume 9 - 2024, Issue 1 - January

Google Scholar : http://tinyurl.com/y6rcvsa9

Scribd : http://tinyurl.com/2umeynb2

DOI : https://doi.org/10.5281/zenodo.10499894

Abstract : The burgeoning realm of real-time communication (RTC) has revolutionized how we interact, blurring the lines between physical and virtual experiences. However, behind the seamless connection lies a complex dance of data transfer, where minimizing bandwidth usage and mitigating lag play critical roles in ensuring a smooth and enjoyable user experience. This is where data compression techniques like zlib step in, acting as silent heroes by reducing data size and optimizing data flow. Utilizing Python as a computational tool, this research delves deep into the efficiency of zlib, meticulously evaluating its performance across key metrics like compression and decompression times, throughput optimization, and limitations with handling large data chunks. Additionally, we explore alternative compression strategies that hold promise for addressing zlib's limitations and enhancing data transfer efficiency in the ever-evolving world of real-time communication. The findings illuminate the strengths and weaknesses of zlib, equipping developers with valuable insights for optimizing data transfer and paving the way for further exploration in the fascinating realms of theoretical computer science and machine learning.

Keywords : Zlib Compression, Real-Time Communication, Python Libraries, Compression Time, Compression Speed, Decompression Time, Decompression Speed, Throughput, Compression Ratio.

The burgeoning realm of real-time communication (RTC) has revolutionized how we interact, blurring the lines between physical and virtual experiences. However, behind the seamless connection lies a complex dance of data transfer, where minimizing bandwidth usage and mitigating lag play critical roles in ensuring a smooth and enjoyable user experience. This is where data compression techniques like zlib step in, acting as silent heroes by reducing data size and optimizing data flow. Utilizing Python as a computational tool, this research delves deep into the efficiency of zlib, meticulously evaluating its performance across key metrics like compression and decompression times, throughput optimization, and limitations with handling large data chunks. Additionally, we explore alternative compression strategies that hold promise for addressing zlib's limitations and enhancing data transfer efficiency in the ever-evolving world of real-time communication. The findings illuminate the strengths and weaknesses of zlib, equipping developers with valuable insights for optimizing data transfer and paving the way for further exploration in the fascinating realms of theoretical computer science and machine learning.

Keywords : Zlib Compression, Real-Time Communication, Python Libraries, Compression Time, Compression Speed, Decompression Time, Decompression Speed, Throughput, Compression Ratio.

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