Measuring Software Energy Consumption
Group 0: Title of the Template blog_By Student1 first and last name, Student2, Student3_ .
abstract Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate ve...
Group 1: LLM Energy Usage Comparison_By Nicholas Wu, Levi Ari Pronk, Francisco Duque de Morais Amaro, Miroslav Atanasov_ .
This project investigates and compares the energy consumption of two lightweight large language models executed locally via Ollama. We evaluate Qwen2.5:0.5B and Qwen3:0.6B under controlled conditions on a GPU-enabled Linux system to measure their GPU energy usage during inference across a diverse...
Group 2: Measuring the Energy Cost of Prompt Engineering for Software Engineering Tasks_By Roham Koohestani, Zofia Rogacka-Trojak, Antonio-Florin Lupu, Pranav Pisupati_ .
Prompt engineering often improves answer quality, but it can also increase token usage, latency, and the number of model calls. This project proposes a reproducible experiment design to quantify the energy/performance trade-offs of common prompting strategies (e.g., step-by-step, politeness, and ...
Group 3: Comparing Local LLM Inference Energy Consumption_By Maciej Bober, Jeroen Chu, Bill Vi, Joost Weerheim_ .
Large language models (LLMs) are increasingly used with supplementary context such as lecture notes, documentation, or retrieved passages to improve output quality. However, the energy cost of processing larger context windows during local inference is poorly understood. This project investigates...
Group 4: Comparing Energy Consumption in Computer Vision Across Different Model Architectures_By Anhar Al Haydar, Tom Clark, Moniek Tummers, Andriana Tzanidou_ .
This study investigates how different computer vision AI architectures affect energy consumption. It compares two open-source object detection models of similar size, RF-DETR medium and YOLOv8 medium, to explore how transformer-based versus CNN-based designs impact energy usage. The results show ...
Group 5: Cross-Platform Browser Energy Benchmarking_By Brewen Couaran, Valantis Andreas, Arnav Biswas, Alex Hautelman_ .
This project examines how operating systems impact browser energy consumption by comparing Google Chrome and Firefox on macOS, Windows, and Linux using a standardized BrowserBench workload measured with EnergiBridge.
Group 6: FaceTime vs Whatsapp energy consumption_By Maciej Wierzbicki, Yi Wu, Figen Ulusal, Zhiyong Zhu_ .
We will measure the energy consumption between FaceTime and WhatsApp 10 minute video call. We will use EnergiBridge to check this.
Group 7: Measuring the True Energy Cost of Correct LLM Inference_By Ceylin Ece, Georgios Markozanis, Kunal Narwani, Amy van der Meijden_ .
A model that uses less energy per token is not necessarily cheaper per correct answer. This project challenges the common practice of measuring LLM inference energy in isolation by introducing correctness as a first-class metric. We benchmark three recent ~3B-parameter LLMs from distinct model fa...
Group 8: Energy Implications of Language and Runtime Choices in Data Ingestion Pipelines_By Yuvraj Singh Pathania, Viktor Seršiḱ, Emils Dzintars, Madhav Chawla_ .
Compare the energy consumption of Python, Java, Go, and Rust when performing a data ingestion task.
Group 9: Sorting Out Energy: Comparing Merge Sort, Quick Sort, and Heap Sort in Python vs. JavaScript_By Norah Elisabeth Milanesi, Mohammed Nassiri, Jimmy Oei, Gonenc Turanlı_ .
This study investigates the energy consumption of Mergesort, Quicksort, and Heapsort in Python and JavaScript (Node.js). A total of 1,080 experiments with varying dataset sizes were conducted using Energibridge. Results show that JavaScript consumed on average 7.2× less energy than Python (3.75 J...
Group 10: The 'Daemon' Tax: An Energy Analysis of Docker and Podman across RESTful and Computational Workloads._By Atharva Dagaonkar, Kasper van Maasdam, Ignas Vasiliauskas, Andreas Tsatsanis_ .
This project compares the energy efficiency of Docker's daemon-based architecture against Podman's daemonless model. Using EnergiBridge to monitor full-system energy consumption, the study deploys a realistic RESTful multi-service web application under both runtimes and measures energy across 30 ...
Group 11: A Study on the Energy Efficiency of Minecraft Shaders_By Konstantinos Syrros, Samuel van den Houten, Sydney Kho, Alessandro Valmori_ .
Minecraft is a popular sandbox video game that allows players to build and explore virtual worlds. Shaders are modifications that enhance the graphics and visual effects of the game, but they can also increase energy consumption. In this project, we will analyze the energy efficiency of Minecraft...
Group 12: Comparing the Energy Consumption of Single-Threaded and Multi-Threaded Programs_By Maksym Ziemlewski, Frederik van der Els, Piotr Kranendonk_ .
Many tasks lend themselves for parallel programming, which can speed up the execution time of the program. Common ways to achieve this is by using multiple threads, and splitting the task into smaller tasks that can be executed in parallel on the different threads. While this indeed can speed up...
Group 13: Comparison of Different Browsers in Terms of Power Efficiency for Streaming Video_By Radu Chiriac, Adomas Bagdonas, Ibrahim Badr, Deon Saji_ .
This study compares the energy efficiency of three major web browsers (Chrome, Firefox, and Brave) during video streaming under controlled experimental conditions. Using EnergiBridge for power measurements and statistical analysis techniques, we evaluate whether browser choice significantly affec...
Group 14: Measuring the Energy Consumption of Python Package Managers: pip, uv, and poetry_By Calin Georgescu, Elia Jabbour, Wojciech Mundała, Daniel Rachev_ .
This article investigates the energy consumption of three Python package managers: pip, uv, and poetry. We run controlled experiments across common dependency management tasks and compare their energy usage, execution time, and consistency. The goal is to identify which tool is the most energy-ef...
Group 15: Zip Wars: Which Compression Tool is Burning Through Your Energy?_By Dragos Erhan, Tae yong Kwon, Priyansh Rajusth, Vincent Ruijgrok_ .
Zipping your files has become as ubiquitous as Googling for information or, by now, asking Chat (known as ChatGPT'ing). Zip archives are the standard in consumer and business data distribution. But there are multiple providers that can compress your folders. We will investigate if there's a diffe...
Group 16: The Energy Cost of the 'Free' Internet: Do Adblockers Save Power?_By Arda Duyum, Caio Miranda Haschelevici, Ion Tulei, Radu Andrei Vasile_ .
Online advertisements are ubiquitous, funding the "free" internet but degrading user experience. Group 16 investigates the hidden energy cost of rendering these ads, and whether using an AdBlocker can significantly reduce the power consumption and carbon footprint of web browsing.
Group 17: Energy Consumption for Online Podcast Playback_By Preethika Ajaykumar, Yuting Dong, Jayran Duggins, Riya Gupta_ .
This project investigates how energy consumption varies during podcast playback in web browsers under controlled conditions. We compare different browsers and playback speeds while streaming identical podcast content, ensuring consistent brightness, duration and system settings across all runs. E...
Group 18: Can a Student Be Greener Than the Teacher? Measuring the Sustainability of Distilled LLMs_By Medon Abraham, Konstantina Anastasiadou, Wilhelm Marcu, Mihai Radu Serban_ .
This study investigates whether the highly efficient, sparse activation patterns of DeepSeek can be successfully transferred to other model architectures (Llama, Qwen). By comparing DeepSeek-distilled models against their standard counterparts, we explore if energy efficiency is a learnable behav...
Group 19: Energy Consumption of Sorting Algorithms: Impact of Algorithmic Complexity and Memory Behavior_By Conall Lynch, Arjun Rajesh Nair, Viktor Shapchev, Coen Werre_ .
In this project, we propose to measure and compare the energy consumption of different sorting algorithms under varying input sizes and data distributions. Sorting is a fundamental building block in modern software systems, used in databases, analytics pipelines, and backend processing tasks. We ...
Group 20: Is Google Meet or Microsoft Teams Greener? Measuring the Energy Cost of Video Calls_By Ocean Wang, Erkin Başol, Yasar Kocdas, Alexia Neatu_ .
We compare the energy consumption, CPU usage, and data transfer of Google Meet and Microsoft Teams in a browser-based environment under stabilized conditions. Our results show that Microsoft Teams consumes significantly more energy and is therefore less sustainable than Google Meet for video conf...
Group 21: Youtube Energy Consumption on Different Browsers with or without Hardware Acceleration_By Leonidas Hadjiyiannis, Noky Soekarman, Yuchen Sun_ .
In this project, we analyze the power consumption of YouTube video playback across three browsers (Chrome, Edge, and Firefox) on Windows 11 using an AMD Ryzen 7 4800H, NVidia GeForce GTX 1650ti system. We compare configurations with hardware acceleration enabled and disabled, using average total ...
Group 22: Energy Consumption of File Compression Across Programming Languages_By Rebecca Andrei, Boris Annink, Paul Anton, Kevin Ji Shan_ .
This project attempts to quantify the effect of different programming languages on energy consumption through the use of a case study. We compare `gzip` compression and decompression across Python, Java, C++, and Go under controlled experimental conditions, using deterministic inputs. We further ...
Group 23: Comparing the Energy Efficiency of Standard JSON and orjson_By Cristian Benghe, Alexandru Mititelu, Antoni Nowakowski, Andrei Paduraru_ .
This project compares the energy consumption of data serialization in Python, namely how the standard json library compares to the orjson alternative. We investigate whether the performance gains from using a Rust-powered JSON library translate into meaningful energy savings through the "Race to ...
Group 24: Streaming in the Browser: Measuring Energy Use of Spotify Web Player vs Apple Music Web vs YouTube Music_By Sneha Prashanth, Gabriel Leite Savegnago, Diana-Otilia Sutac, Yanzhi Chen_ .
We compare the energy consumption of Spotify Web Player, Apple Music Web and YouTube Music Web for common listening sessions in the browser. Using a controlled, reproducible setup, we measure power and CPU activity across identical scenarios such as idle-in-tab, active playback, seeking/skipping,...
Group 25: Energy Compare Project - Doomscrolling Tiktok vs. Youtube Shorts_By Arnas Venskūnas, Dibyendu Gupta, Nick van Luijk, Sophie Schaaf_ .
Screen time is often discussed in terms of productivity and mental health, but what about energy usage? In this post, we compare the system energy usage for different frequency of scrolling while doomscrolling Tiktok versus YouTube Shorts.
Group 26: Browser Energy Usage: The Effect of Ad Blocking_By Maria Cristescu, Nicolas Hornea, Daniël Rugge, Alexandru Verhoveţchi_ .
This project measures the energy consumption of different web browsers with and without UBlock Origin ad blocking. Using LibreHardwareMonitor on Windows, we compare power usage across browsers (i.e. Chrome, Firefox and Edge) and websites (i.e. Youtube, Netflix, Reddit, Nu.nl) to determine whether...
Group 27: LLM Prompting: an energy consumption study_By Fedor Baryshnikov, Jari de Keijzer, Tobias Veselka, Stilyan Penchev_ .
This study will focus on analysing the energy consumption of two LLM-related tasks. Firstly, we will analyse how the length of a question input prompt affects the energy consumption of an LLM generating a response. Secondly, we will study three different models by three different developers and c...
Group 28: Energy Consumption of YouTube Short-Form Video Playback: Auto vs HD720 on Chrome and Firefox_By Uddhav Pisharody, Job Stouthart, Vasil Chirov, Georgi Dimitrov, Horia Zaharia_ .
We investigate the energy consumption of YouTube short-form video playback under controlled conditions. Specifically, we analyze how browser choice (Google Chrome vs Mozilla Firefox) and video quality settings (automatic quality selection vs forced 720p playback) affect energy usage. Using the sa...
Group 29: Comparing the energy consumption of running oxipng benchmarks using different Rust compiler versions_By Tess Hobbes, Nina Semjanová, Mikołaj Magiera, Kristian Hristov_ .
This study examines how energy consumption and execution time have evolved across various versions of the Rust compiler, spanning from early 2020 to early 2026. This analysis is significant from a sustainability perspective, as newer compiler versions often prioritize speed without necessarily op...
Group 30: Measuring the Energy Impact of Mixed Precision on Convolutional Neural Network Training_By Emre Çebi, Aadesh Ramai, Noah Tjoen, Andrea Vezzuto_ .
We measured the energy impact of mixed precision on the training process of a convolutional neural network. Our findings demonstrate that, compared to the default 32-bit floating point accuracy, mixed precision reduces energy consumption by more than 12%, while only sacrificing 1% accuracy in tes...
Group 31: Spotfiy energy consumption comparison_By Abdul Wahab Aiman, Bińkowska Maja, El Khal Miguel, Marin Alexandru_ .
Energy consumption comparison of Spotify during music playback across different client settings using EnergiBridge. We aim to determine which Spotify settings influence energy consumption the most and identify potential energy “pain points” during playback. This includes comparing energy consumpt...
Group 32: Adblocker Consumption: Measuring the Energy Usage of Adblockers_By Cem Gungor, Rodrigo Montero Gonzalez, Sahana Ganesh, Poyraz Temiz_ .
This study compares the energy consumption between two profiles, where one profile has an ad blocker extension and the other one does not. In both profiles we simulate a user reading and browsing content on different newsletters, through an automated script. Our findings aim to highlight the sign...
Group 33: Llama model quantization effect on GPU energy consumption_By Ana Mako, Anouck Heutinck, Robin Kruijf, Jeffrey Meerovici Goryn_ .
This study investigates the energy implications of running quantized Large Language Models in the cloud. By measuring energy consumption across different quantization levels for identical inference tasks, the study aims to quantify the sustainability benefits of model compression and establish an...
Group 34: Are YouTube’s Default Features Silently Wasting Energy? An Experimental Evaluation_By Cosmin Anton, Jan Kuhta, Ada Turgut, Thomas van der Boon_ .
This study investigates whether YouTube’s optional player features—Ambient Mode, Stable Volume, and Voice Boost—affect client-side energy consumption. In controlled experiments isolating each feature, we measure energy usage relative to a baseline with all features disabled. Our results show th...
Group 35: Comparing the difference in Power Consumption between Video Conference Applications Microsoft Teams and Zoom_By Ayush Khadka, Carolyn Alcaraz, Nicolas Loaiza Atehortua, Benas Pranauskas_ .
This project concerns comparing the energy usage of Microsoft Teams and Zoom. We compare the energy impact of enabling different variations of video-call features on both applications: camera on/off, blur on/off, and screen-share on/off. We run 30 iterations per feature pair, per application, and...
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