Publications related to File server

uBFT: Microsecond-Scale BFT using Disaggregated Memory

Rachid Guerraoui, Antoine Murat, Mihail Igor Zablotchi, Athanasios Xygkis, Naama Ben David

We propose uBFT, the first State Machine Replication (SMR) system to achieve microsecond-scale latency in data centers, while using only 2f+1 replicas to tolerate f Byzantine failures. The Byzantine Fault Tolerance (BFT) provided by uBFT is essential as pu ...

2023

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

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

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

Ensemble 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 ...

2020

Hardware-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 ...

2019

Dictionary Compression in Point Cloud Data Management

Anastasia Ailamaki, Mirjana Pavlovic

Nowadays, massive amounts of point cloud data can be collected thanks to advances in data acquisition and processing technologies such as dense image matching and airborne LiDAR scanning. With the increase in volume and precision, point cloud data offers a ...

ASSOC COMPUTING MACHINERY2019

Taster: Self-Tuning, Elastic and Online Approximate Query Processing

Anastasia Ailamaki, Raja Appuswamy, Matthaios Alexandros Olma, Odysseas Papapetrou

Current Approximate Query Processing (AQP) engines are far from silver-bullet solutions, as they adopt several static design decisions that target specific workloads and deployment scenarios. Offline AQP engines target deployments with large storage budget ...

IEEE2019

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

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

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File server

uBFT: Microsecond-Scale BFT using Disaggregated Memory

Privatized graph federated learning

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

When to Hedge in Interactive Services

Ensemble Distillation for Robust Model Fusion in Federated Learning

Hardware-conscious Query Processing in GPU-accelerated Analytical Engines

Dictionary Compression in Point Cloud Data Management

Taster: Self-Tuning, Elastic and Online Approximate Query Processing

Secure Storage with Replication and Transparent Deduplication

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

Graph Chatbot

Chat with Graph Search

When to Hedge in Interactive Services

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

Privatized graph federated learning

uBFT: Microsecond-Scale BFT using Disaggregated Memory

Hardware-conscious Query Processing in GPU-accelerated Analytical Engines

Ensemble Distillation for Robust Model Fusion in Federated Learning

Secure Storage with Replication and Transparent Deduplication

Dictionary Compression in Point Cloud Data Management

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

Taster: Self-Tuning, Elastic and Online Approximate Query Processing