Commit 59da88a9 authored by Daniel Seybold's avatar Daniel Seybold
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finished docs on data analytics

parent edb64da0
......@@ -16,13 +16,13 @@ The respective data analytics code can be found on [CodeOcean](https://doi.org/1
## Scalability
A supportive python script to visualize the throughput and latency of different evaluations scenarios and multiple evaluation runs of each scenario is provided in the [analytics](https://omi-gitlab.e-technik.uni-ulm.de/mowgli/evaluation-orchestrator/-/tree/master/analytics) folder of the evaluation orchestrator.
A supportive python script to visualize the throughput and latency of different evaluations scenarios and multiple evaluation runs of each scenario is provided in the [scalability analytics](https://omi-gitlab.e-technik.uni-ulm.de/mowgli/evaluation-orchestrator/-/tree/master/analytics) folder of the evaluation orchestrator.
#### Configuration
The target evaluation results folders need to be configured directly in the `pandasBarplot.py` script, see examples starting from `line 23`
#### Execution
#### Usage
```
./python3 pandasBarplot.py
......@@ -34,7 +34,19 @@ The target evaluation results folders need to be configured directly in the `pan
## Elasticity
A supportive python script to visualize the elasticity phases of different evaluations scenarios and multiple evaluation runs of each scenario is provided in the [elasticity analytics](https://omi-gitlab.e-technik.uni-ulm.de/mowgli/evaluation-orchestrator/-/tree/master/analytics) folder of the evaluation orchestrator.
#### Usage
1. copy the `transaction.txt` files from the target evaluation runs into a dedicated folder, e.g. `elasticity-analytics`
2. extract the throughput and latency data frames
```
./python3 extractDataFrame.py -i elasticity-analytics
```
3. the script will extract a latency and throughput data frame into the specified folder
4. execute the `elasticityPhases.py` script to generate the following plots. Check the configuration section within the script from **line 36**!
#### Example Results
......@@ -44,17 +56,24 @@ The target evaluation results folders need to be configured directly in the `pan
## Availability
A supportive python script to visualize the availability phases of different availability evaluations scenarios for each evaluation run is provided in the [availability analytics](https://omi-gitlab.e-technik.uni-ulm.de/mowgli/evaluation-orchestrator/-/tree/master/analytics) folder of the evaluation orchestrator.
The script provided advanced visualization of the different phases and also extract availability metrics and phase durations as data frame and Excel sheet for post processing.
adanved plots
data frames
#### Usage
```
python3 plotAvailabilityPhases.py -runFolder availability-ycsb-read\openstack\cassandra\nodes-3_rf-3_cc-one_threads-4\evaluation_run_2019_09_08-15_36\data -workloadPhase transaction
```
`workloadPhase` is required to determine the correct result file, for use `load` for write-only workloads and `transaction` for CRUD workloads that operate in previously loaded data
#### Example Result
![](../misc/availability_phases_analytics.png)
## Multi Criteria Decision Making
## Multi Criteria Decision Making (MCDM)
Advanced analytic scripts that apply MCDM for rating the combination of Cloud and DBMS configurations for objectives such as performance, costs or scalability is currently work-in-progress and will be added as soon as the results are published.
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