https://github.com/radon-h2020/radon-decomposition-enhancement
https://github.com/radon-h2020/radon-decomposition-enhancement
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/radon-h2020/radon-decomposition-enhancement
- Owner: radon-h2020
- License: bsd-3-clause
- Created: 2021-06-24T20:25:04.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2021-09-07T13:32:56.000Z (over 3 years ago)
- Last Synced: 2025-01-12T11:37:29.837Z (4 months ago)
- Language: MATLAB
- Size: 29.3 KB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
| Items | Contents |
|----------|---------------|
| Description | The goal of the Radon-Decomposition-Enhancement tool is to provide resource demand estimations for AWS Lambda Functions, using external monitoring data captured with AWS X-Ray. |
| License | BSD-3-Clause License |
| Mainteners | Giuliano Casale [@gcasale](https://github.com/gcasale), Jose Perusquia [@JosePerusquia](https://github.com/JosePerusquia), and Runan Wang [@runanwang07](https://github.com/runanwang07) |
| Demo | Link: https://www.youtube.com/watch?v=vmnjp_nDqXU&list=PLJ3re6Ar-kEV5WAxbTiJJsBBzPp8-Bzs_&index=6 |
# Radon-Decomposition-Enhancement
The radon-decomposition-enhancement repository, contains the required functions to perform service demand estimation for AWS Lambda Functions.
# Functionality
The enhancement feature is integrated into RADON’S decomposition tool to obtain resource demand estimation based on monitoring data. The current estimation procedure supports a regression-based model on the mean response time as a linear function of the mean queue length at arrival.
The main procedure of the accuracy enhancement for RADON’S decomposition tool is as follows.
- First, users need to monitor the deployed Lambda functions and obtain the distributed traces with AWS X-Ray.
- The enhancement feature takes the log file and the original tosca model file containing the specifications of the pipeline as inputs.
- The full demand estimation is broken down into the following procedures.
- Parse the required timestamps and metadata of all the functions called in the log file.
- Receive the parsed traces and split them according to the different AWS Lambda functions.
- Extract the arrival, the departure, the response time (including queueing time) and the queue length for each trace.
- Estimate the service demand with a regression-based model.
- After obtaining the estimated demand, the original tosca model will be updated with the estimated values.
# Documentation
The extended description of the enhancement can be found in the D2.3 and D6.5, where we present the monitoring customisation and accuracy enhancement. A video presentation and a live demo for the enhancement feature are available at [RADON Webinar 5](https://www.youtube.com/watch?v=vmnjp_nDqXU&list=PLJ3re6Ar-kEV5WAxbTiJJsBBzPp8-Bzs_&index=6).