AI-Assisted Optimization of Software Energy Consumption
Khan, Md (2025-07-10)
AI-Assisted Optimization of Software Energy Consumption
(10.07.2025)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
avoin
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2025073080099
https://urn.fi/URN:NBN:fi-fe2025073080099
Tiivistelmä
The escalating energy footprint of software systems demands urgent intervention,
particularly in widely deployed platforms like WordPress, which powers 43.5% of
global websites. This thesis develops an AI-assisted methodology to optimize energy consumption in WordPress’s PHP backend, emphasizing rigorous empirical
validation. A controlled lab environment (Odroid H3+ rig with PowerGoblin instrumentation) enabled 100 Hz sample-rate energy profiling of computational inefficiencies, while a Random Forest classifier prioritized optimization targets based
on execution time, energy, and power metrics. Heuristic-guided refactoring generated context-specific optimizations, such as replacing nested loops with hash-map
logic and implementing transient caching. Experimental results demonstrate 99.44%
energy reduction for algorithmic bottlenecks (e.g. loop patterns) and statistically significant improvements in memory-intensive operations. The methodology validates
AI-driven prioritization as a robust framework for identifying and mitigating energy hotspots in PHP-based systems, advancing sustainable software engineering
practices.
particularly in widely deployed platforms like WordPress, which powers 43.5% of
global websites. This thesis develops an AI-assisted methodology to optimize energy consumption in WordPress’s PHP backend, emphasizing rigorous empirical
validation. A controlled lab environment (Odroid H3+ rig with PowerGoblin instrumentation) enabled 100 Hz sample-rate energy profiling of computational inefficiencies, while a Random Forest classifier prioritized optimization targets based
on execution time, energy, and power metrics. Heuristic-guided refactoring generated context-specific optimizations, such as replacing nested loops with hash-map
logic and implementing transient caching. Experimental results demonstrate 99.44%
energy reduction for algorithmic bottlenecks (e.g. loop patterns) and statistically significant improvements in memory-intensive operations. The methodology validates
AI-driven prioritization as a robust framework for identifying and mitigating energy hotspots in PHP-based systems, advancing sustainable software engineering
practices.