Disk Power Consumption Modeling in a Distributed Storage Infrastructure
About Scality:
Scality is one of the most prominent FrenchTech startups, recognized throughout the industry for its technical leadership and its open-source contributions. Selected for the French Tech 120 #FT120, Scality is a worldwide leader in the space of software-defined storage.
Scality has over 300 customers in more than 30 countries, including some of the largest telecom operators and banks, several TV stations, and over 30 hospitals. For its team members, Scality wants to be an exceptional employer with many benefits such as continuous education and sports budgets, donation matching, paternity leave worldwide, and many other benefits that go well beyond market standards.
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Context & Motivation
Modern distributed storage infrastructures are made up of thousands of hard disk drives (HDDs) operating continuously. While compute and network energy costs are increasingly well understood, HDD/SSD power consumption under real-world workloads remains difficult to measure and predict accurately. Understanding and modeling this consumption is critical for:
Reducing the environmental footprint of large-scale storage systems
Improving capacity planning and thermal management
Enabling proactive power optimization in production environments
To date, no robust, interpretable multi-parameter model exists that can accurately estimate disk energy consumption from observable parameters in a production environment. This internship aims to fill that gap.
Your Mission
As part of our R&D team, you will design, build, and validate a multi-parameter model for estimating HDD/SSD power consumption. Your work will include:
1. State of the Art
Review existing literature and open-source projects related to HDD/SSD power modeling, thermal behavior, and energy measurement in storage systems (including tools such as PowerAPI).
2. Physical Modeling
Develop a physics-based multi-parameter model of temperature and power draw, capturing relationships between:
Drive temperature
Fan speed and airflow
Read/write throughput and IOPS
Idle vs. active state transitions
3. Interpretable Machine Learning Model
Design a multi-parameter ML model (e.g., gradient boosting, linear regression with feature engineering) that is both accurate and interpretable — enabling engineers to understand which parameters drive consumption under different workload profiles.
4. Measurement & Validation
Instrument real drives in a controlled laboratory environment and under
production-representative workloads to collect ground-truth measurements.
Use this data to:
Calibrate and validate the model
Quantify model accuracy across workload types
Identify parameters with the greatest predictive value
Expected Outcomes & Valorization
Depending on results, the work may be valorized through:
A scientific publication or technical white paper
Integration into the open-source PowerAPI project
Direct integration into Scality's internal monitoring and capacity planning tooling
Technical Stack
Python (primary language for data collection, modeling, and analysis)
scikit-learn (ML modeling and evaluation)
PowerAPI ecosystem (https://powerapi.org/)
Linux system tooling for hardware instrumentation (smartctl, lm-sensors, etc.)
Jupyter Notebooks for exploratory analysis and result visualization
Candidate Profile
Final-year student in a Master's program or Engineering school (Bac+5)
Strong interest in physical modeling and/or applied machine learning
Comfortable working with real hardware and experimental data
Autonomous, curious, and rigorous in your approach to problem-solving
Able to communicate results clearly in written and spoken English
Work Environment
You will join a multicultural R&D team with colleagues across France, the US, and Asia. English is our primary working language. You will benefit from:
Mentorship from senior R&D engineers with expertise in distributed systems and performance engineering
Access to real production-grade storage hardware and laboratory infrastructure
A high-trust environment where your findings will directly influence engineering decision
Why Join Scality?
Work on a concrete research problem with real-world industrial impact
Contribute to open-source energy-efficiency tooling used beyond Scality
Be part of a team building infrastructure trusted by Fortune 500 companies
Potential to publish research or continue as a full-time engineer after the internship