Flexible Development of Dependability Services: An Experience Derived from Energy Automation Systems
October 01, 2019 Β· Declared Dead Β· π Proceedings Ninth Annual IEEE International Conference and Workshop on the Engineering of Computer-Based Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Vincenzo De Florio, Susanna Donatelli, Giovanna Dondossola
arXiv ID
1910.01483
Category
cs.DC: Distributed Computing
Citations
10
Venue
Proceedings Ninth Annual IEEE International Conference and Workshop on the Engineering of Computer-Based Systems
Last Checked
4 months ago
Abstract
This paper describes a novel approach for the flexible development of dependable automation services applied to a case study taken from requirements of energy automation systems. It shows first how the use of a custom compositional recovery language can be exploited to achieve a flexible and dependable functionality in software. Then it is shown how modeling techniques based on Petri nets can be used to assess the properties that different configurations of the addressed service can achieve.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Distributed Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Reproducing GW150914: the first observation of gravitational waves from a binary black hole merger
R.I.P.
π»
Ghosted
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
R.I.P.
π»
Ghosted
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
R.I.P.
π»
Ghosted
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
R.I.P.
π»
Ghosted
iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted