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Publications associées (29)

Privatized graph federated learning

Ali H. Sayed, Stefan Vlaski, Elsa Rizk

Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-m ...

SPRINGER2023

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

When to Hedge in Interactive Services

Aikaterini Argyraki, 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 ...

USENIX ASSOC2021

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

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

David Atienza Alonso, Ali Pahlevan, Marina Zapater Sancho, 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 ...

2022

Mitigating Load Imbalance in Distributed Data Serving with Rack-Scale Memory Pooling

Babak Falsafi, Stanko Novakovic, Boris Robert Grot, Alexandros Daglis, Edouard Bugnion, 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 ...

2019

Drowsy-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 ...

IEEE2019

Successive Refinement to Caching for Dynamic Content

Michael Christoph Gastpar, Pinar Sen

To reduce the network load during peak hours, servers deliver partial data to users during the off-peak time of the network before the actual requests are known, which is known as caching. This paper studies a single user caching problem in which the file ...

IEEE2019

Systems and method for anonymous, low-latencey, tracking-resistant communications in a networked environment

Jean-Pierre Hubaux, Ludovic Barman, Italo Ivan Dacosta Petrocelli, Bryan Alexander Ford

Exemplary embodiments of the present disclosure provide cryptographically-strong tracking protection with unnoticeable "single-hop" network latencies by leveraging a client-relay- server architecture, in which the clients, relays (trusted or untrusted), an ...

2018

Load Balancing Video Clients in Enterprise Wireless Networks

Sergio Mena, Lorenzo Granai, Dan-Cristian Tomozei, Aswin Suresh

Given the increasing popularity of real-time video communication over enterprise wireless networks, ensuring good quality of experience is becoming critical. A common problem in such networks is that the clients typically adopt the strategy of associating ...

IEEE2018

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

Causal Consistency and Latency Optimality: Friend or Foe? [Extended Version]

Rachid Guerraoui, Willy Zwaenepoel, Junxiong Wang, Diego Didona

Causal consistency is an attractive consistency model for geo-replicated data stores. It is provably the strongest model that tolerates network partitions. It avoids the long latencies associated with strong consistency, and, especially when using read-onl ...

2018

Causal Consistency and Latency Optimality: Friend or Foe?

Rachid Guerraoui, Willy Zwaenepoel, Junxiong Wang, Diego Didona

ASSOC COMPUTING MACHINERY2018

Flow control for Latency-Critical RPCs

Edouard Bugnion, Evangelos Marios Kogias

In today’s modern datacenters, the waiting time spent within a server’s queue is a major contributor of the end-to-end tail latency of μs-scale remote procedure calls. In traditional TCP, congestion control handles in-network congestion, while flow control ...

2018

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

David Atienza Alonso, Luca Benini, Ali Pahlevan, Marina Zapater Sancho, 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

Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

Willy Zwaenepoel, Kristina Spirovska, Diego Didona

In this paper we present a new approach to implementing causal consistency in geo-replicated data stores, which we call Optimistic Causal Consistency (OCC). The optimism in our approach lies in that updates from a remote data center are immediately made vi ...

Ieee Computer Soc2017

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

A Gaussian Source Coding Perspective on Caching and Total Correlation

Guillaume Jean Op 't Veld

Communication technology has advanced up to a point where children are getting unfamiliar with themost iconic symbol in IT: the loading icon. We no longer wait for something to come on TV, nor for a download to complete. All the content we desire is availa ...

EPFL2017

PriFi: A Low-Latency and Tracking-Resistant Protocol for Local-Area Anonymous Communication

Jean-Pierre Hubaux, Ludovic Barman, Italo Ivan Dacosta Petrocelli, Bryan Alexander Ford, Mahdi Zamani

Popular anonymity mechanisms such as Tor provide low communication latency but are vulnerable to traffic analysis attacks that can de-anonymize users. Moreover, known traffic-analysis-resistant techniques such as Dissent are impractical for use in latency- ...

ACM2016

The Case for RackOut: Scalable Data Serving Using Rack-Scale Systems

Babak Falsafi, Stanko Novakovic, Boris Robert Grot, Alexandros Daglis, Edouard Bugnion

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 ...

2016

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