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Concept

Server farm

Related publications (22)

Is the powersave governor really saving power?

David Atienza Alonso, Darong Huang, Luis Maria Costero Valero

A frequency scaling governor is critical for the performance management of cloud servers, as it enhances energy efficiency and helps to control operational temperatures, thereby ensuring system reliability. However, our in-depth analysis of the application ...

2024

Genuinely distributed Byzantine machine learning

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault, Le Nguyen Hoang, Arsany Hany Abdelmessih Guirguis

Machine learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

2022

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

David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Darong Huang, Luis Maria Costero Valero

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

LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads

Martin Jaggi, Sebastian Urban Stich

In distributed optimization, parameter updates from the gradient computing node devices have to be aggregated in every iteration on the orchestrating server. When these updates are sent over an arbitrary commodity network, bandwidth and latency can be limi ...

MICROTOME PUBLISHING2021

Genuinely Distributed Byzantine Machine Learning

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault, Le Nguyen Hoang, Arsany Hany Abdelmessih Guirguis

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

Association for Computing Machinery2020

Functionality Enhanced Memories for Edge-AI Embedded Systems

David Atienza Alonso, Alexandre Sébastien Julien Levisse, Pierre-Emmanuel Julien Marc Gaillardon, Marco Antonio Rios, William Andrew Simon

With the surge in complexity of edge workloads, it appeared in the scientific community that such workloads cannot be anymore overflown to the cloud due to the huge edge device to server communication energy cost and the high energy consumption induced in ...

IEEE2019

Hardware-conscious Query Processing in GPU-accelerated Analytical Engines

Anastasia Ailamaki, Periklis Chrysogelos, Panagiotis Sioulas

In order to improve their power efficiency and computational capacity, modern servers are adopting hardware accelerators, especially GPUs. Modern analytical DMBS engines have been highly optimized for multi-core multi-CPU query execution, but lack the nece ...

2019

Network-Compute Co-Design for Distributed In-Memory Computing

Alexandros Daglis

The booming popularity of online services is rapidly raising the demands for modern datacenters. In order to cope with data deluge, growing user bases, and tight quality of service constraints, service providers deploy massive datacenters with tens to hund ...

EPFL2018

The mean-field behavior of processor sharing systems with general job lengths under the SQ(d) policy

Arpan Mukhopadhyay

This paper addresses the mean-field behavior of large-scale systems of parallel servers with a processor sharing service discipline when arrivals are Poisson and jobs have general service time distributions when an SQ(d) routing policy is used. Under this ...

ELSEVIER SCIENCE BV2018

Increasing Availability in Distributed Storage Systems via Clustering

Michael Christoph Gastpar, Saeid Sahraei

We introduce the Fixed Cluster Repair System (FCRS) as a novel architecture for Distributed Storage Systems (DSS) that achieves a small repair bandwidth while guaranteeing a high availability. Specifically, we partition the set of servers in a DSS into s c ...

2018

Rack-Scale Memory Pooling for Datacenters

Stanko Novakovic

The rise of web-scale services has led to a staggering growth in user data on the Internet. To transform such a vast raw data into valuable information for the user and provide quality assurances, it is important to minimize access latency and enable in-me ...

EPFL2017

Towards Near-Threshold Server Processors

David Atienza Alonso, Luca Benini, Babak Falsafi, Marina Zapater Sancho, Pablo Garcia del Valle, Javier Picorel Obando, Ali Pahlevan, Arash Pourhabibi Zarandi

The popularity of cloud computing has led to a dramatic increase in the number of data centers in the world. The ever-increasing computational demands along with the slowdown in technology scaling has ushered an era of power-limited servers. Techniques suc ...

IEEE/ACM2016

Approximation methods for large-scale spatial queueing systems

Nikolaos Geroliminis, Burak Boyaci

Different than the conventional queueing systems, in spatial queueing systems (SQS) the service rate for each customer-server pairs differs and the server that intervenes for a specific customer is not known a priori, depending on the availability of serve ...

Pergamon-Elsevier Science Ltd2015

Dynamic Fine-Grained Scheduling for Energy-Efficient Main-Memory Queries

Anastasia Ailamaki, Danica Porobic, Pinar Tözün, Iraklis Psaroudakis, Erietta Liarou

Power and cooling costs are some of the highest costs in data centers today, which make improvement in energy efficiency crucial. Energy efficiency is also a major design point for chips that power whole ranges of computing devices. One important goal in t ...

2014

BuMP: Bulk Memory Access Prediction and Streaming

Babak Falsafi, Boris Robert Grot, Javier Picorel Obando, Stavros Volos

With the end of Dennard scaling, server power has emerged as the limiting factor in the quest for more capable datacenters. Without the benefit of supply voltage scaling, it is essential to lower the energy per operation to improve server efficiency. As th ...

2014

A Case for Specialized Processors for Scale-Out Workloads

Anastasia Ailamaki, Babak Falsafi, Michael Ferdman, Yusuf Onur Koçberber, Stavros Volos, Dorde Jevdic, Ilknur Cansu Kaynak, Adrian Daniel Popescu, Al Mutaz Adileh, Mohammad Alisafaee

Emerging scale-out workloads need extensive amounts of computational resources. However, datacenters using modern server hardware face physical constraints in space and power, limiting further expansion and requiring improvements in the computational densi ...

Ieee Computer Soc2014

Scale-Out Processors

Pejman Lotfi Kamran

Global-scale online services, such as Google’s Web search and Facebook’s social networking, run in large-scale datacenters. Due to their massive scale, these services are designed to scale out (or distribute) their respective loads and datasets across thou ...

EPFL2013

Clearing the Clouds: A Study of Emerging Scale-out Workloads on Modern Hardware

Anastasia Ailamaki, Babak Falsafi, Michael Ferdman, Yusuf Onur Koçberber, Adrian Daniel Popescu, Stavros Volos, Dorde Jevdic, Ilknur Cansu Kaynak, Al Mutaz Adileh, Mohammad Alisafaee

Emerging scale-out workloads require extensive amounts of computational resources. However, data centers using modern server hardware face physical constraints in space and power, limiting further expansion and calling for improvements in the computational ...

2012

Quantifying the Mismatch between Emerging Scale-Out Applications and Modern Processors

Anastasia Ailamaki, Babak Falsafi, Yusuf Onur Koçberber, Stavros Volos, Dorde Jevdic, Ilknur Cansu Kaynak, Michael Ferdman, Adrian Daniel Popescu, Al Mutaz Adileh, Mohammad Alisafaee

Association for Computing Machinery2012

Scale-Out Processors

Babak Falsafi, Boris Robert Grot, Javier Picorel Obando, Yusuf Onur Koçberber, Stavros Volos, Dorde Jevdic, Pejman Lotfi Kamran, Michael Ferdman, Al Mutaz Adileh

Scale-out datacenters mandate high per-server throughput to get the maximum benefit from the large TCO investment. Emerging applications (e.g., data serving and web search) that run in these datacenters operate on vast datasets that are not accommodated by ...

Ieee2012