Publications related to Windows Server 2016

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 ...

2022

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

Resource Management for Power-Constrained HEVC Transcoding Using Reinforcement Learning

David Atienza Alonso, Marina Zapater Sancho, Arman Iranfar, Luis Maria Costero Valero

The advent of online video streaming services along with the users' demand for high-quality contents require High Efficiency Video Coding (HEVC), which provides higher quality and compression at the cost of increased complexity. On one hand, HEVC exposes a ...

2020

A system design for elastically scaling transaction processing engines in virtualized servers

Anastasia Ailamaki, Raja Appuswamy, Angelos Christos Anadiotis, Hillel Avni

Online Transaction Processing (OLTP) deployments are migrating from on-premise to cloud settings in order to exploit the elasticity of cloud infrastructure which allows them to adapt to workload variations. However, cloud adaptation comes at the cost of re ...

ASSOC COMPUTING MACHINERY2020

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 Machinery2020

Timed-Release Encryption With Master Time Bound Key

Serge Vaudenay, Gwangbae Choi

Timed-release encryption allows senders to send a message to a receiver which cannot decrypt until a server releases a time bound key at the release time. The release time usually supposed to be known to the receiver, the ciphertext therefore cannot be dec ...

2019

Caching and Distributed Storage : Models, Limits and Designs

Saeid Sahraei

A simple task of storing a database or transferring it to a different point via a communication channel turns far more complex as the size of the database grows large. Limited bandwidth available for transmission plays a central role in this predicament. I ...

EPFL2018

Scaling Up Concurrent Analytical Workloads on Multi-Core Servers

Iraklis Psaroudakis

Today, an ever-increasing number of researchers, businesses, and data scientists collect and analyze massive amounts of data in database systems. The database system needs to process the resulting highly concurrent analytical workloads by exploiting modern ...

EPFL2016

Thermal Characterization of Cloud Workloads on a Power-Efficient Server-on-Chip

Babak Falsafi, Pejman Lotfi Kamran, Dorde Jevdic, Andreas Panteli

We propose a power-efficient many-core server-on-chip system with 3D-stacked Wide I/O DRAM targeting cloud workloads in datacenters. The integration of 3D-stacked Wide I/O DRAM on top of a logic die increases available memory bandwidth by using dense and f ...

2012

Scale-Out Processors

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

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

Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles

Jean-Pierre Hubaux, Reza Shokri, Pedram Pedarsani, Georgios Theodorakopoulos

In recommender systems, usually, a central server needs to have access to users' profiles in order to generate useful recommendations. Having this access, however, undermines the users' privacy. The more information is revealed to the server on the user-it ...

ACM2009