https://github.com/spirali/estee
Scheduler Simulator Environment
https://github.com/spirali/estee
Last synced: 11 months ago
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Scheduler Simulator Environment
- Host: GitHub
- URL: https://github.com/spirali/estee
- Owner: spirali
- License: mit
- Created: 2018-09-05T19:01:33.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-04-06T12:29:12.000Z (about 7 years ago)
- Last Synced: 2025-05-06T13:47:54.986Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 5.4 MB
- Stars: 9
- Watchers: 3
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Estee
Estee is a discrete event simulator for executing task graphs in distributed
environments. It is mainly designed for benchmarking and experimenting with
schedulers. Estee is created as an open-ended simulator; most of its
components can be extended or replaced by a custom implementation. It is
possible to experiment with different kinds of schedulers, workers, and network
models. Estee comes with battery-included and provides a standard
implementation for all its components.
## Achitecture
## Installation
```
python3 setup.py install
```
## Hello world example
The following example creates a simple task graph, instantiates a network model
and the simulator and executes the task with one of build-in schedulers.
```python
from estee.common import TaskGraph
from estee.schedulers import BlevelGtScheduler
from estee.simulator import Simulator, Worker, MaxMinFlowNetModel
# Create task graph containing 3 tasks
# (each task runs 1s and requires 1 CPU)
#
# t0
# | (50MB output)
# / \
# t1 t2
task_graph = TaskGraph()
t0 = task_graph.new_task(duration=1, cpus=1, output_size=50)
t1 = task_graph.new_task(duration=1, cpus=1)
t1.add_input(t0)
t2 = task_graph.new_task(duration=1, cpus=1)
t2.add_input(t0)
# Create B-level scheduler
scheduler = BlevelGtScheduler()
# Define cluster with 2 workers (1 CPU each)
workers = [Worker(cpus=1) for _ in range(2)]
# Define MaxMinFlow network model (100MB/s)
netmodel = MaxMinFlowNetModel(bandwidth=100)
# Create a simulator
simulator = Simulator(task_graph, workers, scheduler, netmodel, trace=True)
# Run simulation, returns the makespan in seconds
makespan = simulator.run()
# Print simulation time
print("Task graph execution makespan = {}s".format(makespan))
simulator.write_chrome_trace("trace.json")
```
### Displying trace.json in Chrome profiler
## Built-in implementations
### Built-in schedulers
* Blevel (HLFET)
* Tlevel
* DLS
* ETF
* LAST
* MCP
* Simple genetic algorithm based scheduler
* Simple work stealing scheduler
* Camp2
### Built-in network models
* MaxMin flow model (MaxMinFlowNetModel)
* All downloads runs at full speed (SimpleNetModel)
* Instant communication (InstantNetModel)