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Publications associées (28)

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CALYPSO: Private Data Management for Decentralized Ledgers

Bryan Alexander Ford, Linus Gasser, Eleftherios Kokoris Kogias, Philipp Svetolik Jovanovic, Enis Ceyhun Alp

Distributed ledgers provide high availability and integrity, making them a key enabler for practical and secure computation of distributed workloads among mutually distrustful parties. Many practical applications also require strong confidentiality, howeve ...

2021

Scalable Robust Graph Embedding with Spark

Karl Aberer, Quoc Viet Hung Nguyen, Chi Thang Duong, Trung-Dung Hoang

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...

ASSOC COMPUTING MACHINERY2021

The Cost of Scaling a Reliable Interconnection Topology

Rachid Guerraoui, Alexandre David Olivier Maurer

In distributed computing, many papers try to evaluate the message complexity of a distributed system as a function of the number of nodes n. But what about the cost of building the distributed system itself? Assuming that we want to reliably connect n node ...

2020

Optimus Prime: Accelerating Data Transformation in Servers

Babak Falsafi, Christoph Koch, Siddharth Gupta, Mario Paulo Drumond Lages De Oliveira, Mark Johnathon Sutherland, Arash Pourhabibi Zarandi, Zilu Tian, Hussein Kassir

Modern online services are shifting away from monolithic applications to loosely-coupled microservices because of their improved scalability, reliability, programmability and development velocity. Microservices communicating over the datacenter network req ...

ACM2020

Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion

Nicolas Aspert, Volodymyr Miz, Benjamin Ricaud

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatic ...

2020

Plug-and-Play Fault Detection and Isolation for Large-Scale Nonlinear Systems With Stochastic Uncertainties

Giancarlo Ferrari Trecate

This paper proposes a novel scalable model-based fault detection and isolation approach for the monitoring of nonlinear large-scale systems, consisting of a network of interconnected subsystems. The fault diagnosis architecture is designed to automatically ...

2019

Efficient Workload Colocation in Modern Data Centers

Calin Iorgulescu

Despite the high costs of acquisition and maintenance of modern data centers, machine resource utilization is often low. Servers running online interactive services are over-provisioned to support peak load (which only occurs for a fraction of the time), d ...

EPFL2019

Scaling Byzantine Fault Tolerance

Matej Pavlovic

Online services are becoming more and more ubiquitous and keep growing in scale. At the same time, they are required to be highly available, secure, energy-efficient, and to achieve high performance. To ensure these (and many other) properties, replication ...

EPFL2019

PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication

Willy Zwaenepoel, Diego Didona, Kristina Spirovska

Geo-replicated data platforms are the backbone of several large-scale online services. Transactional Causal Consistency (TCC) is an attractive consistency level for building such platforms. TCC avoids many anomalies of eventual consistency, eschews the syn ...

IEEE COMPUTER SOC2019

Asynchronous Branch-Paralle Simulation of Detailed Neuron Models

Felix Schürmann, Michael Lee Hines

Simulations of electrical activity of networks of morphologically detailed neuron models allow for a better understanding of the brain. State-of-the-art simulations describe the dynamics of ionic currents and biochemical processes within branching topologi ...

2019

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