Publications by Ali Pahlevan | EPFL Graph Search

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 ...

2024

Reinforcement 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 ...

2022

ECOGreen: 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 ...

2020

MAGNETIC: 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 ...

2019

Enhancing 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 Press2019

Integrating 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 ...

2018

Energy 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 ...

2018

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Xiaoyu Qu

2017

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Ali Pahlevan

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

Multi-Objective System-Level Management of Modern Green Data Centers

Enhancing Two-Phase Cooling Efficiency through Thermal- Aware Workload Mapping for Power-Hungry Servers

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

Energy Proportionality in Near-Threshold Computing Servers and Cloud Data Centers: Consolidating or Not?

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

Graph Chatbot

Chat with Graph Search

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

Multi-Objective System-Level Management of Modern Green Data Centers

MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

Energy Proportionality in Near-Threshold Computing Servers and Cloud Data Centers: Consolidating or Not?

Reinforcement Learning-Based Joint Reliability and Performance Optimization for Hybrid-Cache Computing Servers

ECOGreen: Electricity Cost Optimization for Green Datacenters in Emerging Power Markets

Enhancing Two-Phase Cooling Efficiency through Thermal- Aware Workload Mapping for Power-Hungry Servers