Publications related to Server (computing)

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

Practical Byzantine-resilient Stochastic Gradient Descent

Sébastien Louis Alexandre Rouault

Algorithms are everywhere.The recipe for the frangipane cake is an algorithm.If all the listed ingredients are available and the cook is sufficiently deft, after a finite number of small, well-defined steps a delicious dessert will exit the oven.Now, what ...

EPFL2022

When to Hedge in Interactive Services

Edouard Bugnion, Mia Primorac

In online data-intensive (OLDI) services, each client request typically executes on multiple servers in parallel; as a result, “system hiccups”, although rare within a single server, can interfere with many client requests and cause violations of service-l ...

USENIX2021

GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

Rachid Guerraoui, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

We present GARFIELD, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine–resilient. GARFIELD relies on a novel object–oriented design, reducing t ...

IEEE2021

Virtual Capacitor Concept for Computationally Efficient and Flexible Real-Time MMC Model

Drazen Dujic, Stefan Milovanovic, Min Luo

This paper demonstrates the splitting of the modular multilevel converter real-time simulation model into several independent parts through the use of the virtual capacitor concept. As a result, the number of state-space matrices the real-time solver needs ...

2021

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

Martin Jaggi, Sebastian Urban Stich, Hossein Shokri Ghadikolaei

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

Hybrid Two-Phase Cooling Technology For Next-Generation Servers: Thermal Performance Analysis

John Richard Thome, Jackson Braz Marcinichen, Raffaele Luca Amalfi, Filippo Cataldo

This paper advances the work presented at ITHERM 2020 where a novel passive two-phase technology was introduced for more efficient cooling of datacenters compared to air-based cooling solutions (e.g. In-Room, In-Row, etc.). The proposed hybrid two-phase co ...

IEEE2021

Advances and Open Problems in Federated Learning

Martin Jaggi, Sebastian Urban Stich, Lie He, Yang Liu, Ayfer Özgür Aydin, Florian Tramèr, Qiang Yang, Ananda Theertha Suresh, Badih Ghazi

Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decen ...

NOW PUBLISHERS INC2021

Performance Characterization of HTAP Workloads

Anastasia Ailamaki, Sandhya Dwarkadas, Utku Sirin

Hybrid Transactional and Analytical Processing (HTAP) systems have become popular in the past decade. HTAP systems allow running transactional and analytical processing workloads on the same data and hardware. As a result, they suffer from workload interfe ...

IEEE COMPUTER SOC2021

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Server (computing)

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

Hardware and Software Support for RPC-Centric Server Architecture

Practical Byzantine-resilient Stochastic Gradient Descent

When to Hedge in Interactive Services

GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

Virtual Capacitor Concept for Computationally Efficient and Flexible Real-Time MMC Model

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

Hybrid Two-Phase Cooling Technology For Next-Generation Servers: Thermal Performance Analysis

Advances and Open Problems in Federated Learning

Performance Characterization of HTAP Workloads

Graph Chatbot

Chat with Graph Search

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

GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

Hardware and Software Support for RPC-Centric Server Architecture

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

Practical Byzantine-resilient Stochastic Gradient Descent

Hybrid Two-Phase Cooling Technology For Next-Generation Servers: Thermal Performance Analysis

When to Hedge in Interactive Services

Performance Characterization of HTAP Workloads

Virtual Capacitor Concept for Computationally Efficient and Flexible Real-Time MMC Model

Advances and Open Problems in Federated Learning