Cédric Tedeschi's research while affiliated with French National Centre for Scientific Research and other places
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Publications (18)
Stream Processing (SP), i.e., the processing of data in motion, as soon as it becomes available, is a hot topic in cloud computing. Various SP stacks exist today, with applications ranging from IoT analytics to processing of payment transactions. The backbone of said stacks are Stream Processing Engines (SPEs), software packages offering a high-lev...
The constant growth of maritime traffic leads to the need of automatic anomaly detection, which has been attracting great research attention. Information provided by AIS (Automatic Identification System) data, together with recent outstanding progresses of deep learning, make vessel monitoring using neural networks (NNs) a very promising approach....
Stream Processing deals with the efficient, real-time processing of continuous streams of data. Stream Processing engines ease the development and deployment of such applications which are commonly pipelines of operators to be traversed by each data item. Due to the varying velocity of the streams, autoscaling is needed to dynamically adapt the num...
Collaborative web applications benefit from good responsiveness. This can be difficult to achieve with deployments on core data centers subject to high network latencies. Hybrid deployments using a mix of core and edge resources closer to end users are a promising alternative. Many challenges are associated with hybrid deployments of applications,...
We consider the problem of executing composite computing applications called workflows on top of unreliable computing infrastructures. Having in mind the situation of the electric delivery in the sub-saharan area, we propose BEDWE, a decentralized workflow engine able to dynamically assign portions of the workflow to currently live compute nodes. M...
The surveillance of the maritime traffic is a major issue for security and monitoring issues. Spaceborne technologies, especially satellite AIS ship tracking and high-resolution imaging, open new avenues to address these issues. Current operational systems cannot fully benefit from the available and upcoming multi-source data streams. In this conte...
In this paper, we are interested in the runtime complexity of programs based on multiset rewriting. The motivation behind this work is the study of the complexity of chemistry-inspired programming models, which recently regained momentum due to their adequacy to the programming of large autonomous systems. In these models, data are most of the time...
Stream processing engines have appeared as the next generation of data processing systems, facing the needs for low-delay processing. While these systems have been widely studied recently, their ability to adapt their processing logics at run time upon the detection of some events calling for adaptation is still an open issue.
Chemistry-inspired mo...
In this paper, we devise a chemistry-inspired programming model for the decentralised execution of scientific workflows, with the possibility of dynamically adapting its shape when its initial specification fails to reach the user's requirements or simply to run due to external conditions. We describe a decentralised architecture to support the mod...
The growth for scientific data has led to data analysis being a critical step in the scientific process. The next-generation scientific data analysis environment needs to address two challenges i) productivity of the end-user and ii) scalability of the workflows. The need to ensure both goals requires us to revisit the design and implementation of...
Citations
... This consensus occupied a space in CS by the process where already other processes with their transaction are available. Stream processing as another classified problem of synchronization, Belkhiria et al. [77] extended GME to scale stream processing pipelines to ensure higher concurrent occupancy by introducing Two Fixed Groups Mutual Exclusion (2-FGME) algorithm. Various restrictions have been made as a part of their work to limit the number of request messages to a set of certain nodes to reduce the traffic in the network. ...
... Since AIS was internationally adopted as a standard safety measure in 2002 [10], many publications have investigated using AIS information to detect dark ships [11,12]. The definition of dark ship that these publications use can vary from paper to paper, but the term typically describes ships that are failing to transmit AIS messages at a rate that falls within international maritime law compliance or are blatantly spoofing messages [13][14][15]. ...
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... After that, we devote our attention to different service placement methods. Battulga, Miorandi & Tedeschi (2020) introduce the FogGuru platform for fog computing implemented via a real-world testbed. Their representative fog landscape is built out of five Raspberry Pis united in a cluster cloud tier. ...
... The Grid'5000 testbed is a testbed distributed principally in France with 8 sites, 31 clusters and 828 nodes with a total of 12328 cores [181]. It allows researchers and developers to conduct high quality, reproducible, large-scale experiments as for example in [116], [167], [182] in the domain of data stream processing. ...
... Unfortunately, most cloud services and applications were neither designed following distributed approaches (e.g., e-commerce, web-services, stream processing, etc.) nor thinking about the edge, as we explained in Section 3.3.1 for OpenStack and Kubernetes. Moreover, modifications to such software stacks are tedious while not impossible [66,179]. ...
... The node asks in a semi random sequential pattern, neighboring nodes to take over the control of the sensor they are unable to handle, which would result in QoS degradation if they keep under their influence. In [31], authors propose a structured overlay network for efficient service localization with aim to minimize management traffic, while providing locality-aware routing. In [7], a Fog-2-Fog collaboration model is proposed to achieve load balancing to Fog Nodes inside a Fog Colony. ...
... A fog device can restore corrupted data by asking other fog device in the network. In the mentioned works, devices are organized in a P2P manner and are assumed to have partial view [25] or full view of the network (i.e., O(1) protocols). In contrast to the mentioned works, our approach provides a decentralized mechanism to organize edge devices in clusters. ...
... Another important maritime surveillance big data-oriented approach has been created within the SESAME platform to make novel solutions for the management, analysis, and visualization of multi-source Automatic Identification System (AIS) and satellite data streams from Earth Observation high-resolution Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imaging [15]. The platform combines big-data-oriented clusters such as Cassandra, Hadoop, Spark, and Fink for storage and batch processing. ...
... The GinFlow prototype is currently under development and testing. In parallel to this work, the GinFlow prototype is currently integrated to the TIGRES workflow tools [11]. ...
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