Personne
Arman Iranfar
Cette personne n’est plus à l’EPFL
Publications associées (12)
Multi-Objective Management of Multiprocessor Systems: From Heuristics to Reinforcement Learning
In my thesis, I reveal several already-existing and emerging challenges in multi-objective management of multiprocessor systems, and address them through novel solutions, from heuristics to RL, depending on the complexity of the problem. Conventional multi ...
EPFL2020Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning
David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Arman Iranfar
The advent of online video streaming services along with the users' demand for high-quality contents require High Efficiency Video Coding (HEVC), which provides higher quality and compression at the cost of increased complexity. On one hand, HEVC exposes a ...
2020Containergy-A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads
David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Wellington Silva De Souza
Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and ...
2020Dynamic Thermal Management with Proactive Fan Speed Control Through Reinforcement Learning
David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Federico Terraneo, Gabor Andras Csordas
Dynamic Thermal Management (DTM) has become a major challenge since it directly affects Multiprocessors Systems-on-chip (MPSoCs) performance, power consumption, and reliability. In this work, we propose a transient fan model, enabling adaptive fan speed co ...
IEEE2020MAMUT: Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-User Video Transcoding
David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Arman Iranfar
Real-time video transcoding has recently raised as a valid alternative to address the ever-increasing demand for video contents in servers' infrastructures in current multi-user environments. High Efficiency Video Coding (HEVC) makes efficient online trans ...
IEEE2019A QoS and Container-Based Approach for Energy Saving and Performance Profiling in Multi-Core Servers
David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Wellington Silva De Souza
In this work we present ContainEnergy, a new performance evaluation and profiling tool that uses software containers to perform application runtime assessment, providing energy and performance profiling data. It is focused on energy efficiency for next gen ...
IEEE2019Enhancing Two-Phase Cooling Efficiency through Thermal- Aware Workload Mapping for Power-Hungry Servers
David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Ali Pahlevan
The power density and, consequently, power hungriness of server processors is growing by the day. Traditional air cooling systems fail to cope with such high heat densities, whereas single-phase liquid-cooling still requires high mass flowrate, high pumpin ...
IEEE and ACM Press2019Design of a Two-Phase Gravity-Driven Micro-Scale Thermosyphon Cooling System for High-Performance Computing Data Centers
David Atienza Alonso, John Richard Thome, Marina Zapater Sancho, André Olivier Seuret, Arman Iranfar
Next-generation High-Performance Computing (HPC) systems need to provide outstanding performance with unprecedented energy efficiency while maintaining servers at safe thermal conditions. Air cooling presents important limitations when employed in HPC infr ...
IEEE2018A Machine Learning-Based Strategy for Efficient Resource Management of Video Encoding on Heterogeneous MPSoCs
David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, William Andrew Simon
The design of new streaming systems is becoming a major area of research to deploy services targeted in the Internet-of-Things (IoT) era. In this context, the new High Efficiency Video Coding (HEVC) standard provides high efficiency and scalability of qual ...
2018