ReProbe: An Architecture for Reconfigurable and Adaptive Probes
March 19, 2024 Β· Declared Dead Β· π 2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C)
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Federico Alessi, Alessandro Tundo, Marco Mobilio, Oliviero Riganelli, Leonardo Mariani
arXiv ID
2403.12703
Category
cs.SE: Software Engineering
Citations
0
Venue
2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C)
Last Checked
4 months ago
Abstract
Modern distributed systems are highly dynamic and scalable, requiring monitoring solutions that can adapt to rapid changes. Monitoring systems that rely on external probes can only achieve adaptation through expensive operations such as deployment, undeployment, and reconfiguration. This poster paper introduces ReProbes, a class of adaptive monitoring probes that can handle rapid changes in data collection strategies. ReProbe offers controllable and configurable self-adaptive capabilities for data transmission, collection, and analysis methods. The resulting architecture can effectively enhance probe adaptability when qualitatively compared to state-of-the-art monitoring solutions.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
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