Concept
Server (computing)
Related publications (150)
Genuinely Distributed Byzantine Machine Learning
Rachid Guerraoui, El Mahdi El Mhamdi, Le Nguyen Hoang, Sébastien Louis Alexandre Rouault, 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 Machinery2020Single-server Multi-message Private Information Retrieval with Side Information: the General Cases
Michael Christoph Gastpar, Su Li
The single-server multi-message private information retrieval with side information problem is studied for general cases. In this problem, K independent messages are stored at a single server. One user initially has M messages as side information and wants ...
IEEE2020Ensemble Distillation for Robust Model Fusion in Federated Learning
Martin Jaggi, Sebastian Urban Stich, Tao Lin, Lingjing Kong
Federated Learning (FL) is a machine learning setting where many devices collaboratively train a machine learning model while keeping the training data decentralized. In most of the current training schemes the central model is refined by averaging the par ...
2020Robust P2P Personalized Learning
Rachid Guerraoui, Yahya Benkaouz
Decentralized machine learning over peer-to-peer networks is very appealing for it enables to learn personalized models without sharing users data, nor relying on any central server. Peers can improve upon their locally trained model across a network graph ...
IEEE2020Drowsy-DC: Data center power management system
Willy Zwaenepoel, Baptiste Joseph Eustache Lepers, Mathieu Paul Fernand Bacou
In a modern data center (DC), a large majority of costs arise from energy consumption. The most popular technique used to mitigate this issue is virtualization and more precisely virtual machine (VM) consolidation. Although consolidation may increase serve ...
IEEE2019MAGNETIC: 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 ...
2019Mitigating Load Imbalance in Distributed Data Serving with Rack-Scale Memory Pooling
Babak Falsafi, Edouard Bugnion, Boris Robert Grot, Alexandros Daglis, Stanko Novakovic, Dmitrii Ustiugov
To provide low-latency and high-throughput guarantees, most large key-value stores keep the data in the memory of many servers. Despite the natural parallelism across lookups, the load imbalance, introduced by heavy skew in the popularity distribution of k ...
2019Functionality 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 ...
IEEE2019Hardware-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 ...
2019Traffic Locality as an Opportunity in the Data Center
In large scale data centers, network infrastructure is becoming a major cost component; as a result, operators are trying to reduce expenses, and in particular lower the amount of hardware needed to achieve their performance goals (or to improve the perfor ...
EPFL2019