
Alexandre VeithNokia Bell Labs · Software and Data Systems (SDSR) Research Lab
Alexandre Veith
Ph.D.
About
34
Publications
12,571
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Introduction
I am a Research Software Engineer at Nokia Bell Labs (Belgium), conducting research on federated stream processing systems. Previously, I was a postdoctoral researcher at the University of Toronto, Canada. I hold a Ph.D. in Computer Science from the Ecole Normale Superieure (ENS) of Lyon and the University of Lyon, France. My wide-ranging areas of interest include distributed systems, ML/RL/FL, data stream processing, fog/edge computing, IoT, big data analytics, and optimization problems.
Additional affiliations
Education
November 2016 - September 2019
September 2012 - September 2014
August 2003 - December 2011
Publications
Publications (34)
Internet of Things (IoT) applications often require the processing of data streams generated by devices dispersed over a large geographical area. Traditionally, these data streams are forwarded to a distant cloud for processing, thus resulting in high application end-to-end latency. Recent work explores the combination of resources located in cloud...
The Internet of Things has enabled many application scenarios where a large number of connected devices generate unbounded streams of data, often processed by data stream processing frameworks deployed in the cloud. Edge computing enables offloading processing from the cloud and placing it close to where the data is generated, whereby reducing both...
The Internet of Things has enabled many application scenarios where a large number of connected devices generate unbounded streams of data, often processed by data stream processing frameworks deployed in the cloud. Edge computing enables offloading processing from the cloud and placing it close to where the data is generated, thereby reducing the...
The rapid growth of stream applications in financial markets, health care, education, social media, and sensor networks represents a remarkable milestone for data processing and analytic in recent years, leading to new challenges to handle Big Data in real-time. Traditionally, a single cloud infrastructure often holds the deployment of Stream Proce...
Data Stream Processing applications are increasingly used in new pervasive services that process enormous amounts of data in a seamless and near real-time fashion. Edge computing has emerged as a means to minimise the time to handle events by enabling processing (i.e., operators) to be offloaded from the Cloud to the edges of the Internet, where th...
There is an increasing demand for handling massive amounts of data in a timely manner via Distributed Stream Processing (DSP). A DSP application is often structured as a directed graph whose vertices are operators that perform transformations over the incoming data and edges representing the data streams between operators. DSP applications are trad...
There is an increasing demand for handling massive amounts of data in a timely manner via Distributed Stream Processing (DSP). A DSP application is often structured as a directed graph whose vertices are operators that perform transformations over the incoming data and edges representing the data streams between operators. DSP applications are trad...
There is increasing demand for handling massive amounts of data in a timely manner via Data Stream Processing (DSP). A DSP application is often structured as a directed graph whose vertices are operators that perform transformations over the incoming data and edges representing the data streams between operators.
DSP applications are traditionally...
There is increasing demand for handling massive amounts of data in a timely manner via Data Stream Processing. A DSP application is often structured as a directed graph whose vertices are operators that perform transformations over the incoming data and edges representing the data streams between operators.
DSP applications are traditionally deploy...
The number of Internet of Things applications is forecast to grow exponentially within the coming decade. Owners of such applications strive to make predictions from large streams of complex input in near real time. Cloud-based architectures often centralize storage and processing, generating high data movement overheads that penalize real-time app...
The number of Internet of Things applications is forecast to exponentially grow within the coming decade. Owners of such applications strive to make predictions from large streams
of complex input in near real time. Cloud-based architectures often centralize storage and processing, generating high data movement overheads that penalize real-time app...
The interest in processing data events under stringent time constraints as they arrive has led to the emergence of architecture and engines for data stream processing. Edge computing, initially designed to minimize the latency of content delivered to mobile devices, can be used for executing certain stream processing operations. Moving operators fr...
The interest in processing data events under stringent time constraints as they arrive has led to the emergence of architecture and engines for data stream processing. Edge computing, initially designed to minimize the latency of content delivered to mobile devices, can be used for executing certain stream processing operations. Moving operators fr...
Stream Processing Engines (SPEs) have to support high data ingestion to ensure the quality and efficiency for the end-user or a system administrator. The data flow processed by SPE fluctuates over time, and requires real-time or near real-time resource pool adjustments (network, memory, CPU and other). This scenario leads to the problem known as sk...
Much of the "big data" generated today is received in near real-time and requires quick analysis. In Internet of Things (IoT) [1, 9], for instance, continuous data streams produced by multiple sources must be handled under very short delays. As a result, several stream processing engines have been proposed. Under several engines, a stream processin...
Under several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous data streams must be processed under very short delays. Several solutions, including multiple software engines, have been developed for processing unbounded data streams in a...
Under several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous data streams must be processed under very short delays. Several solutions, including multiple software engines, have been developed for processing unbounded data streams in a...
Expectations regarding the future growth of Internet of Things (IoT)-related technologies are high. These expectations require the realization of a sustainable general purpose application framework that is capable to handle these kinds of environments with their complexity in terms of heterogeneity and volatility. The paradigm of the Lambda archite...
A substantial part of the " big data " generated today is received in near real time and must be promptly processed. Cloud-based architectures for data stream processing comprise multiple software modules or frameworks for data collection, message queueing, and stream processing itself. This modular approach allows each component to grow independen...
Expectations regarding the future growth of Internet of Things (IoT)-related technologies are high. These expectations require the realization of a sustainable general purpose application framework that is capable to handle these kind of environments with their complexity in terms of heterogeneity and volatility. The paradigm of the Lambda architec...
Demonstracao de frameworks e conceitos de Big Data Stream Processing.
Esse livro aborda a questão "Como explorar computação colaborativa em Grades P2P para execução de aplicações BSP com eficiência?". Para resposta dessa questão surgiu o modelo BSPonP2P, o qual cria um ambiente com abordagens baseadas nos modelos estruturado e não estruturado vindos da arquitetura P2P. A mescla dessas abordagens tem como objetivo agi...
Instituição de registro: INPI - Instituto Nacional da Propriedade Industrial
Today, BSP (Bulk-Synchronous Parallel) represents one of the most often used models for writing tightly-coupled parallel programs. As resource substrates, commonly clusters and eventually computational grids are used to run BSP applications. In this context, here we investigate the use of collaborative computing and idle resources to execute this k...
Questions
Questions (3)
I am looking for recommendations on how to synchronize multi-host clocks efficiently to get precise clock values. NTP provides an average error of 10 ms (maximum 100 ms), which is unacceptable to my experiments.
Frameworks such as Apache Storm, Flink, Heron and Spark were developed to run on clusters or cloud. These such kinds of infrastructures do not have memory, CPU and bandwidth limitations. In contrast, computing resources at the network edge are constrained regarding their capabilities. I am aware of the Apache Edgent and Nifi frameworks. However, they were conceived to run locally on a single computing resource. If you want to run them in a distributed infrastructure, you might create your own stack of components (broker + framework).
Dear All.
I am a CPLEX beginner. In this way, I am looking for help to build my solution. Currently, my objective function minimizes the response time for an event in a Real-time application.
Application: In each operator, I will notice the CPU and bandwidth requirements.
Nodes: the available CPU will be informed.
Links: Bandwidth and latency will be available.