{P2PPerf}: a framework for simulating and optimizing peer-to-peer distributed computing applications

International audiencePeer-to-Peer architectures are more and more studied by distributed computing community. Indeed, this type of architecture makes possible to inherit few properties, in particular the absence of centralized topology, fault-tolerance and dynamic reorganization of the network. However, the complexity of these networks is increased and the acceleration of the distributed applications is not ensured. That's why it is necessary to predict the performances as soon as possible in design and development phases, to bypass bottlenecks and correct program blocks which slow down the execution time. In this context, we propose P2PPerf: a simulation tool of which objective is to predict performances and execution times of a distributed application before its finalization. P2PPerf has been tested on JNGI: a P2P distributed computing application using the JXTA platfor

Evaluation of a Performance Prediction Tool for Peer-to-Peer Distributed Computing Applications

International audience--In the search of new architecture for distributed computing, peer-to-peer is studied as a new way forward. But, its intrinsic properties like the absence of centralized topology or the dynamic reorganization of the network make it difficult to reach high performances. Furthermore, it is not trivial to execute applications on an existing testbed which brings together a sufficient number of nodes. That is why it is necessary to use a simulation environment which will be used to bypass bottlenecks and to correct the parts of the applications which slow down the execution time. In this context, we have proposed P2PPerf: a simulation tool which aims at predicting performance and the execution time of a distributed application before its finalization. This article presents some experiments conducted with P2PPerf to evaluate its accuracy. The emphasis is put on the use of real execution platforms with the Grid'5000 platform and with the Wrekavoc tool

Using similarity groups to increase performance of P2P computing

Abstract. This article aims to demonstrate how to build new types of groups called similarity groups into the JNGI project. This is done in order to increase the relevance of task dispatching and therefore to increase the performance of JNGI.

Welding Seam Classification in the Automotive Industry using Deep Learning Algorithms

International audienceWelding seam inspection is key process in the automotive industry and should guarantee the quality required by the client. Visual inspection is achieved by the operator who checks each part manually, making the reliability highly improvable. That's why automating the visual inspection is needed in today's production process. Collecting data from inside the plant may not provide a balanced number of images between good welding seams and bad welding seams. In this article, we will compare a standard deep learning algorithm applied on raw data with data augmentation approaches. Our target is to reach an accuracy of 97 % on the defected reference parts. This target is reached on some welds, while it remains a challenge on other welds