Publications associées à How to Allocate Tasks Asynchronously

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Coordinate-descent adaptation over networks

Ali H. Sayed, Bicheng Ying, Chengcheng Wang

This work examines the mean-square error performance of diffusion stochastic algorithms under a generalized coordinate-descent scheme. In this setting, the adaptation step by each agent is limited to a random subset of the coordinates of its stochastic gra ...

2017

Sequential Proximity: Towards Provably Scalable Concurrent Search Algorithms

Rachid Guerraoui, Vasileios Trigonakis, Karolos Antoniadis, Julien Michel Stainer

Establishing the scalability of a concurrent algorithm a priori, before implementing and evaluating it on a concrete multi-core platform, seems difficult, if not impossible. In the context of search data structures however, according to all practical work ...

Springer International Publishing2017

Quantization Design for Distributed Optimization

Colin Neil Jones, Ye Pu, Melanie Nicole Zeilinger

We consider the problem of solving a distributed optimization problem using a distributed computing platform, where the communication in the network is limited: each node can only communicate with its neighbors and the channel has a limited data-rate. A co ...

2017

Quantization Design for Distributed Optimization

Colin Neil Jones, Ye Pu, Melanie Nicole Zeilinger

We consider the problem of solving a distributed optimization problem using a distributed computing platform, where the communication in the network is limited: each node can only communicate with its neighbours and the channel has a limited data-rate. A c ...

2015

Multi-Objective Quality-Driven Service Selection-A Fully Polynomial Time Approximation Scheme

Boi Faltings, Immanuel Trummer

The goal of multi-objective quality-driven service selection (QDSS) is to find service selections for a workflow whose quality-of-service (QoS) values are Pareto-optimal. We consider multiple QoS attributes such as response time, cost, and reliability. A s ...

Institute of Electrical and Electronics Engineers2014

Tight Bounds for Asynchronous Renaming

Rachid Guerraoui, Seth Gilbert, Dan Alistarh

This article presents the first tight bounds on the time complexity of shared-memory renaming, a fundamental problem in distributed computing in which a set of processes need to pick distinct identifiers from a small namespace. We first prove an individual ...

Association for Computing Machinery2014

Near-Optimally Teaching the Crowd to Classify

Andreas Krause, Amin Karbasi, Ilija Bogunovic

How should we present training examples to learners to teach them classification rules? This is a natural problem when training workers for crowdsourcing labeling tasks, and is also moti- vated by challenges in data-driven online educa- tion. We propose a ...

2014

Asynchronous Gossip

Rachid Guerraoui, Seth Gilbert

We study the complexity of gossip in an asynchronous, message-passing fault-prone distributed system. We show that an adaptive adversary can significantly hamper the spreading of a rumor, while an oblivious adversary cannot. The algorithmic techniques prop ...

Association for Computing Machinery2013

How to Allocate Tasks Asynchronously

Graph Chatbot

Chattez avec Graph Search

Coordinate-descent adaptation over networks

Sequential Proximity: Towards Provably Scalable Concurrent Search Algorithms

Quantization Design for Distributed Optimization

Pattern formation with voluminous processes

Nematodynamics and random homogenization

Quantization Design for Distributed Optimization

Multi-Objective Quality-Driven Service Selection-A Fully Polynomial Time Approximation Scheme

Tight Bounds for Asynchronous Renaming

Near-Optimally Teaching the Crowd to Classify

Asynchronous Gossip

Sequential Proximity: Towards Provably Scalable Concurrent Search Algorithms

Asynchronous Gossip

Coordinate-descent adaptation over networks

Pattern formation with voluminous processes

Tight Bounds for Asynchronous Renaming

Quantization Design for Distributed Optimization

Multi-Objective Quality-Driven Service Selection-A Fully Polynomial Time Approximation Scheme

Near-Optimally Teaching the Crowd to Classify

Quantization Design for Distributed Optimization

Nematodynamics and random homogenization