Task Scheduler Performance Survey Results

Task scheduler performance survey

This dataset contains results of task graph scheduler performance survey.
The results are stored in the following files, which correspond to simulations performed on 
the `elementary`, `irw` and `pegasus` task graph datasets published at https://doi.org/10.5281/zenodo.2630384.

• elementary-result.zip
• irw-result.zip
• pegasus-result.zip

The files contain compressed pandas dataframes in CSV format, it can be read with the following Python code:
```python
import pandas as pd
frame = pd.read_csv("elementary-result.zip")
```

Each row in the frame corresponds to a single instance of a task graph that
was simulated with a specific configuration (network model, scheduler etc.).
The list below summarizes the meaning of the individual columns.

• graph_name - name of the benchmarked task graph
• graph_set - name of the task graph dataset from which the graph originates
• graph_id - unique ID of the graph
• cluster_name - type of cluster used in this instance the format is <number-of-workers>x<number-of-cores>; 32x16 means 32 workers, each with 16 cores
• bandwidth - network bandwidth [MiB]
• netmodel - network model (simple or maxmin)
• scheduler_name - name of the scheduler
• imode - information mode
• min_sched_interval - minimal scheduling delay [s]
• sched_time - duration of each scheduler invocation [s]
• time - simulated makespan of the task graph execution [s]
• execution_time - real duration of all scheduler invocations [s]
• total_transfer - amount of data transferred amongst workers [MiB]

The file `charts.zip` contains charts obtained by processing the datasets.
On the X axis there is always bandwidth in [MiB/s].
There are the following files:

• [DATASET]-schedulers-time - Absolute makespan produced by schedulers [seconds] 
• [DATASET]-schedulers-score - The same as above but normalized with respect to the best schedule (shortest makespan) for the given configuration.
• [DATASET]-schedulers-transfer - Sums of transfers between all workers for a given configuration [MiB]
• [DATASET]-[CLUSTER]-netmodel-time - Comparison of netmodels, absolute times [seconds]
• [DATASET]-[CLUSTER]-netmodel-score - Comparison of netmodels, normalized to the average of model "simple"
• [DATASET]-[CLUSTER]-netmodel-transfer - Comparison of netmodels, sum of transfered data between all workers [MiB]
• [DATASET]-[CLUSTER]-schedtime-time - Comparison of MSD, absolute times [seconds]
• [DATASET]-[CLUSTER]-schedtime-score - Comparison of MSD, normalized to the average of "MSD=0.0" case
• [DATASET]-[CLUSTER]-imode-time - Comparison of Imodes, absolute times [seconds]
• [DATASET]-[CLUSTER]-imode-score - Comparison of Imodes, normalized to the average of "exact" imode

Reproducing the results

1. Download and install Estee (https://github.com/It4innovations/estee)

$ git clone https://github.com/It4innovations/estee
$ cd estee
$ pip install .

2. Generate task graphs
You can either use the provided script `benchmarks/generate.py` to generate graphs
from three categories (elementary, irw and pegasus):

$ cd benchmarks
$ python generate.py elementary.zip elementary
$ python generate.py irw.zip irw
$ python generate.py pegasus.zip pegasus

or use our task graph dataset that is provided at https://doi.org/10.5281/zenodo.2630384.

3. Run benchmarks
To run a benchmark suite, you should prepare a JSON file describing the benchmark.
The file that was used to run experiments from the paper is provided in
`benchmark.json`. Then you can run the benchmark using this command:

$ python pbs.py compute benchmark.json

The benchmark script can be interrupted at any time (for example using Ctrl+C).
When interrupted, it will store the computed results to the result file and restore
the computation when launched again.

3. Visualizing results

$ python view.py --all <result-file>

The resulting plots will appear in a folder called `outputs`.