Person
Ali Pahlevan
This person is no longer with EPFL
Related publications (13)
CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds
David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang, Ali Pahlevan
Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...
2024Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers
David Atienza Alonso, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang, Ali Pahlevan
Computing servers play a key role in the development and process of emerging compute-intensive applications in recent years. However, they need to operate efficiently from an energy perspective viewpoint, while maximizing the performance and lifetime of th ...
2022ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets
David Atienza Alonso, Marina Zapater Sancho, Ayse Kivilcim Coskun, Ali Pahlevan
Modern datacenters need to tackle efficiently the increasing demand for computing resources while minimizing energy usage and monetary costs. Power market operators have recently introduced emerging demand-response programs, in which electricity consumers ...
2020MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers
David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Kosar Haghshenas
Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource m ...
2019Multi-Objective System-Level Management of Modern Green Data Centers
In our modern society, the average citizen has turned into a daily cloud user. Despite being virtually transparent for the user, internet services require data centers behind the scenes. Data centers burn several megawatts of power and their electricity bi ...
EPFL2019Enhancing 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 Press2019Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers
David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Xiaoyu Qu
Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM ...
2018Energy Proportionality in Near-Threshold Computing Servers and Cloud Data Centers: Consolidating or Not?
David Atienza Alonso, Marina Zapater Sancho, Luca Benini, Ali Pahlevan, Yasir Mahmood Qureshi
Cloud Computing aims to efficiently tackle the increasing demand of computing resources, and its popularity has led to a dramatic increase in the number of computing servers and data centers worldwide. However, as effect of post-Dennard scaling, computing ...
2018Online Efficient Bio-Medical Video Transcoding on MPSoCs Through Content-Aware Workload Allocation
David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Ali Pahlevan
Bio-medical image processing in the field of telemedicine, and in particular the definition of systems that allow medical diagnostics in a collaborative and distributed way is experiencing an undeniable growth. Due to the high quality of bio-medical videos ...
IEEE and ACM Press2018Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers
David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Xiaoyu Qu
Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM ...
2017