Communication channel prioritization in a publish-subscribe architecture
April 08, 2015 Β· Declared Dead Β· π Workshop on Software Engineering and Architectures for Realtime Interactive Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ali Paikan, Daniele Domenichelli, Lorenzo Natale
arXiv ID
1504.02128
Category
cs.SE: Software Engineering
Citations
5
Venue
Workshop on Software Engineering and Architectures for Realtime Interactive Systems
Last Checked
4 months ago
Abstract
Real-Time communication are important in all those distributed applications where timing constraints on data proccessing and task executation play a fundamental role. Standards-base software engineering does not yet specify how real-time properties should be integrated into a publish/subscribe middleware. This article describes an approach for integration of priority quality of service in a publish/subscribe middleware. The approach simply leverages the operating system functionalities to provide a framework where specific communication channels can be prioritized at run-time. The quality of service is implemented in YARP (Yet Another Robot Platform) framework and the primarily results of performance tests are presented.
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