This research line focuses on multi-objective resource management of heterogeneous High Performance Computing (HPC) servers and datacenters through machine learning-based approaches.
Our research leverages system-level resource management techniques, such as Dynamic Voltage and Frequency Scaling (DVFS), task scheduling and allocation, and thread migration, to simultaneously satisfy different design- and run-time objectives and constraints including power/energy consumption, temperature, performance, and Quality-of-Service.
|Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers|
|Huang, Darong; Pahlevan, Ali; Costero, Luis; Zapater Sancho, Marina; Atienza Alonso, David|
|2022-03-07||IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems|
|Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning|
|Costero, Luis; Iranfar, Arman; Zapater Sancho, Marina; D. Igual, Francisco; Olcoz, Katzalin; Atienza Alonso, David|
|2020||IEEE Transactions on Parallel and Distributed Systems|
|A Machine Learning-Based Framework for Throughput Estimation of Time-Varying Applications in Multi-Core Servers|
|Iranfar, Arman; Silva De Souza, Wellington; Zapater Sancho, Marina; Olcoz, Katzalin; Xavier de Souza, Samuel; Atienza Alonso, David|
|A Machine Learning-Based Strategy for Efficient Resource Management of Video Encoding on Heterogeneous MPSoCs|
|Iranfar, Arman; Simon, William Andrew; Zapater Sancho, Marina; Atienza Alonso, David|
|Machine Learning-Based Quality-Aware Power and Thermal Management of Multistream HEVC Encoding on Multicore Servers|
|Iranfar, Arman; Zapater Sancho, Marina; Atienza Alonso, David|
|2018||Journal of IEEE Transactions on Parallel and Distributed Systems (TPDS)|