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.
