This repository contains several scripts to analyze and visualize data collected from SURF's Lisa cluster. The data can be found on Zenodo at https://zenodo.org/record/4459519.
- Download the dataset using the link mentioned above
- Clone this repository to some folder.
- Per script, modify the paths as required.
"/path/to/surfsara-jobdata/","path to machine metric dataset"and variants should point the the dataset downloaded in point 1../cacheshould be point to a location where some scratch data can be put. - Run the notebook on a machine that has 64GB or more RAM, as some analyses require some in-memory storage. For some scripts, a Spark cluster is required due to the sheer amount of data and processing required. If
Koalasis used in a script, you are most likely needing to setup a small spark cluster. 4-10 machines each having 64GB or more RAM will suffice. Incorrelation_plot_koalas.py, we use 5 machines (1 master, 4 workers) each having 64GB of RAM. - The figures will be output in the folder where the notebook resides, or where you point the paths Matplotlib/Seaborn should output to. Tables are printed in the notebook as a string.
Scripts The scripts have generally a self-describing name. Below we provide some more details per script.
| Script | Explanation |
|---|---|
| !LSTM V2.ipynb | Investigates the effect of different sampling intervals on predictions of metric values. |
| !Network data analysis.ipynb | Performs various analyses related to network IO. |
| !Z-Score.ipynb | A script that investigates if anomalies can be detected using z-scores. |
| !jobdata_analysis_new.ipynb | Performs various different analyses related to the executed jobs within Lisa. |
| Full_Cluster_bottleneck_analysis.ipynb | Creates a holistic normalized overview of the dataset by aligning job arrivals with various machine metrics. |
| Generic_outline_dataset.ipynb | Computes various generic properties of the dataset. The overview table below in the readme is constructed using this script. |
| correlation_single_rack_one_day.ipynb | Computes the Pearson, Spearman, and Kendall correlation coefficients for all pairs of metrics within the dataset on individual days. |
| analysis_coefficient_separate_days.ipynb | Visualizes in various ways the output of correlation_single_rack_one_day.ipynb. |
| correlation_plot_koalas.py | Computes a dense correlation plot of normalized histograms, scatterplots with linear regression lines per metric pair, and visualized the Pearson, Spearman, and Kendall correlation coefficients per metric pair. |
| koalas_correlation_plot_data_only.ipynb | Creates a better visualization of the plot of correlation_plot_koalas.py by creating a variant of Seaborn's pairgrid. |
| daily_weekly_trend_load.ipynb | Creates several weekly and diurnal trend visualizations. |
| file_sizes_different_granularities.ipynb | Computes the storage overhead for different sampling frequencies using a selection of metrics. |
| generate_barplots.py | Generates barplots of metric values in covid vs non-covid periods. |
| generate_boxplots.py | Generates boxplots of metric values in covid vs non-covid periods. |
| job_arrival_characterization.ipynb | Creates several visualizations and performs different kind of analyses based on job arrivals. |
| mean_memory_utilization_nodes.ipynb | Analyses different aspects of the node RAM usage and creates several different visualization. |
| power_consumption_analysis.ipynb | Performs several analyses on the rack and power consumption and creates several different visualizations. |
| rack_temp_noenc.py | Analyzes for various racks their node temperatures and creates visualizations for them. |
The dataset spans from 2019-12-29 to 2020-08-07.
| Element | Value |
|---|---|
| Sampling frequency | 15 seconds |
| Max. samples per metric per node | 1,258,646 |
| Number of metrics | 327 |
| Number of measurements | 66,541,895,243 |
Most tools in this repository were created and tested using the following libraries and their versions:
| Library | Version |
|---|---|
| Pandas | 1.2.0 |
| NumPy | 1.19.4 |
| SciPy | 1.5.3 |
| statsmodels | 0.12.1 |
| pytz | 2020.4 |
| SKlearn | 0.24.0 |
| Tensorflow | 2.3.1 |
| pyarrow | 3.0.0 |
| Dask | 2021.03.0 |
| Matplotlib | 3.4.1 |
| Seaborn | 0.11.1 |
| Koalas | 1.5.0 |
| Spark | 3.0.0 |
| Hadoop | 2.7.7 |
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