Publications related to Client (computing)

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

SCAFFOLD Stochastic Controlled Averaging for Federated Learning

Sebastian Urban Stich, Sai Praneeth Reddy Karimireddy, Ananda Theertha Suresh

Federated Averaging (FEDAVG) has emerged as the algorithm of choice for federated learning due to its simplicity and low communication cost. However, in spite of recent research efforts, its performance is not fully understood. We obtain tight convergence ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2020

Pythia: Remote Oracles for the Masses

Mathias Josef Payer, Yuqing Zhang

Remote Direct Memory Access (RDMA) is a technology that allows direct access from the network to a machine's main memory without involving its CPU. RDMA offers low-latency, high-bandwidth performance and low CPU utilization. While RDMA provides massive per ...

USENIX ASSOC2019

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

Rachid Guerraoui, Willy Zwaenepoel, Diego Didona, Junxiong Wang

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, Diego Didona, Junxiong Wang

ASSOC COMPUTING MACHINERY2018

Load Balancing Video Clients in Enterprise Wireless Networks

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

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

Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

Willy Zwaenepoel, Diego Didona, Kristina Spirovska

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

Enabling the Automatic Generation of User Interfaces for Remote Laboratories

Denis Gillet, Christophe Salzmann, Wissam Halimi, Hagop Jamkojian

Remote laboratories are an important component of blended and dis- tance science and engineering education. By definition, they provide access to a physical lab in a distant location. Many architectures enabling remote laboratory systems exist, the most co ...

Springer2017

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Client (computing)

Groove: Flexible Metadata-Private Messaging

When to Hedge in Interactive Services

SCAFFOLD Stochastic Controlled Averaging for Federated Learning

Pythia: Remote Oracles for the Masses

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

Causal Consistency and Latency Optimality: Friend or Foe?

Load Balancing Video Clients in Enterprise Wireless Networks

Towards a Solution to the Red Wedding Problem

Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

Enabling the Automatic Generation of User Interfaces for Remote Laboratories

Graph Chatbot

Chat with Graph Search

When to Hedge in Interactive Services

Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

Towards a Solution to the Red Wedding Problem

Groove: Flexible Metadata-Private Messaging

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

Enabling the Automatic Generation of User Interfaces for Remote Laboratories

Pythia: Remote Oracles for the Masses

Load Balancing Video Clients in Enterprise Wireless Networks

Causal Consistency and Latency Optimality: Friend or Foe?

SCAFFOLD Stochastic Controlled Averaging for Federated Learning