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# Plan d'expé replay with feedback # Compare traditional replay and replay with feedback
- **Système observé** : modèle de rejeu de workload pour simulation de systèmes distribués This repository contains the necessary material to reproduce the experiments and the graphs presented in the article XXXXXX.
- **Type d'étude** : preuve de concept ?
- *c'est pas "hypothesis testing",*
- *on aimerait bien que ce soit une validation de modèle, mais on n'a pas de données réelles pour valider (TODO: regarder si on trouve de la littérature sur "workload prediction based on user behavior")*
Quels sont nos **input** ? TODO: finish writting README, explaining the content of the repo
- le comportement de soumission de l'utilisateur : \ No newline at end of file
- sessions (et horaires) de travail,
- réaction au feedback,
- tâches soumises (taille, durée, ...)
- la plateforme de calcul (machines, speed, bandwidth, etc.)
- le scheduler
**Output** du système :
- après analyse en session
- DAG de session : stats sur répartition des think time (*est-ce que ça fait sens d'avoir un think time de 195 jours ?*), nombre sessions, nombre de dépendences, ...
- après replay with feedback
- timestamps de soumission par rapport à la trace historique
- métriques de scheduling (waiting time, slowdown, jobs killed, ...)
- throughput
## Reproduire Zakay & Feitelson 2015
### Ce qu'ils ont fait
- 8 logs de PWA
- 4 user models (conventionnal, adjusted, distribution and fluid)
- 2 schedulers (FCFS et EASY)
- méthode de découpage *fixée* (arrival t=60)
En tout : $8*4*2 = 64$ expes, non randomisées
### Ce qu'il me manque pour reproduire ça
- 2 / 4 user models, en plus mon modèle "adjusted" n'est pas exactement comme le leur
- les fichiers de plateforme sur 7 / 8 logs
## Plan d'expe temporaire
### Comparer replay rigid / think time
Lancer `batmen` avec les inputs
- n workload: KTH-SP2, SDSC-SP2, X...[A COMPLETER]
- découpage: arrival t=0 (=> think time on jobs) et t=60
- comportements: ReplayRigid / FeedbackThinkTime
- scheduler FCFS
- infra "originale" / infra $* 0.5$ / infra $* 2$ (/!\ nb ressources)
- même étude sur la vitesse des machines (/!\ walltime)
$6n$ expes
Observer :
- job submissions sur une semaine mediane (observer la perte de saisonalité)
- dans le cas infra sur-dimensionné : on devrait avoir rigid = replay feedback
### Comparer EASY et FCFS
Lancer `batmen` avec les inputs
- n workload: KTH, X, X...[A COMPLETER]
- découpage: arrival t=0 (=> think time on jobs) et t=60
- comportements: FeedbackThinkTime
- scheduler EASY
$n * 1 * 1 * 2 = 2n$ expes ($n$ supplémentaires)
Observer :
- le throughput après un temps de chauffe et bien avant la fin de la log
### Modèle avec saisonnalité
- remplacer le think time par une probabilité de présence
- think time trop long = nouvel utilisateur
### Rejouer EuroPar'22
TODO
## Contribution de l'étude
- analyse fine des résultats
- comprendre ce qui est important
- (reproductible et open source)
\ No newline at end of file
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<?xml version='1.0'?>
<!DOCTYPE platform SYSTEM "http://simgrid.gforge.inria.fr/simgrid/simgrid.dtd">
<platform version="4.1">
<zone id="AS0" routing="Full">
<cluster id="cluster_MC" prefix="MC_" suffix="" radical="0-103"
core="16" bw="10Gbps" lat="0.0"
speed="11.77Gf, 1e-9Mf, 0.166666666666667f, 0.006666666666667f">
<prop id="wattage_per_state" value="100:100:217, 9.75:9.75:9.75, 100:100:100, 125:125:125" />
<prop id="wattage_off" value="10" />
<!-- sleep_ps:on_ps:off_ps -->
<prop id="sleep_pstates" value="1:2:3" />
</cluster>
<cluster id="cluster_master" prefix="master_host" suffix="" radical="0-0"
bw="125MBps" lat="0.0" speed="100.0Mf">
<prop id="wattage_per_state" value="100:100:200" />
<prop id="wattage_off" value="10" />
<prop id="role" value="master" />
</cluster>
<link id="backbone" bandwidth="10Gbps" latency="0.0" />
<zoneRoute src="cluster_MC" dst="cluster_master" gw_src="MC_cluster_MC_router" gw_dst="master_hostcluster_master_router">
<link_ctn id="backbone" />
</zoneRoute>
</zone>
</platform>
# To save the nix cache in a file, for future use
# (usefull when working in Grid'5000)
nix-store --query --references $(nix-instantiate default.nix) | \
xargs nix-store --realise | \
xargs nix-store --query --requisites | \
xargs nix-store --export > cache_nix
#nix-store --export $(nix-store -qR paths) > cache_nix
#!/usr/bin/env bash
cd ~/expe_g5k
sudo-g5k
mkdir -m 0755 -p /nix/var/nix/{profiles,gcroots}/per-user/$USER
# sudo su root --command "echo 1 > /proc/sys/kernel/unprivileged_userns_clone"
# curl https://nixos.org/releases/nix/nix-2.13.3/install | sh
sh <(curl -L https://nixos.org/nix/install) --no-daemon --yes
. ~/.nix-profile/etc/profile.d/nix.sh
# nix-env -iA nixpkgs.bat nixpkgs.tree
# Uncomment the line below if you want to import the nix cache from a file
# (useful when working in Grid'5000)
# nix-store --import < cache_nix
#!/usr/bin/env python3
from util import *
import json
def run_batmen(expe_name, user_category, wl_folder, pf, clean_log):
"""Run batmen with given behavior, workload and platform"""
EXPE_DIR = f"{ROOT_DIR}/out/{expe_name}"
create_dir_rec_if_needed(EXPE_DIR)
create_dir_rec_if_needed(f"{EXPE_DIR}/cmd")
EXPE_FILE = f"{EXPE_DIR}/cmd/robinfile.yaml"
wl = f"{WL_DIR}/empty_workload.json"
uf = f"{EXPE_DIR}/cmd/user_description_file.json"
# User description file
def user_description(user):
return {
"name": user,
"category": user_category,
"param": {"input_json": f"{wl_folder}/{user}.SABjson"}
}
user_names = [user_file.split('.')[0] for user_file in os.listdir(wl_folder)]
data = {}
data["log_folder"] = EXPE_DIR
data["users"] = [user_description(user) for user in user_names]
with open(uf, 'w') as user_description_file:
json.dump(data, user_description_file)
# Generate and run robin instance
socket_batsim = f"tcp://localhost:28000"
socket_batsched = f"tcp://*:28000"
batcmd = gen_batsim_cmd(
pf, wl, EXPE_DIR, f"--socket-endpoint={socket_batsim} --energy --enable-compute-sharing --enable-dynamic-jobs --acknowledge-dynamic-jobs --enable-profile-reuse")
schedcmd = f"batmen --socket-endpoint={socket_batsched} -v fcfs --variant_options_filepath={uf}"
instance = RobinInstance(output_dir=EXPE_DIR,
batcmd=batcmd,
schedcmd=schedcmd,
simulation_timeout=604800, ready_timeout=10,
success_timeout=3600, failure_timeout=5
)
instance.to_file(EXPE_FILE)
print(f"Run robin {EXPE_FILE}")
ret = run_robin(EXPE_FILE)
print(f"Robin {EXPE_FILE} finished with return code {ret}")
def main():
expe_name = "expeKTH"
wl_folder = f"{WL_DIR}/KTH_arrival_t0"
pf = f"{PF_DIR}/KTH_platform.xml"
user_category = "fb_user_think_time_only"
run_batmen(expe_name, user_category, wl_folder, pf, clean_log=False)
if __name__ == "__main__":
main()
\ No newline at end of file
#! /bin/bash
cd ~/expe_g5k
source scripts/install_nix.sh
# nix-shell --pure -A exp_env --run "python3 campaign2.py"
#!/usr/bin/env python3
import os
import os.path
import subprocess
# import numpy as np
# import pandas
# from matplotlib import figure, pyplot as plt
# from evalys.jobset import JobSet
# from evalys.metrics import compute_load
# import evalys.visu.legacy as vleg
############
# USEFUL VAR
############
ROOT_DIR = os.path.realpath(os.path.join(os.path.dirname(__file__), '..'))
WL_DIR = f'{ROOT_DIR}/workload'
PF_DIR = f'{ROOT_DIR}/platform'
############
# TO RUN EXPES WITH ROBIN
############
class RobinInstance(object):
def __init__(self, output_dir, batcmd, schedcmd, simulation_timeout, ready_timeout, success_timeout, failure_timeout):
self.output_dir = output_dir
self.batcmd = batcmd
self.schedcmd = schedcmd
self.simulation_timeout = simulation_timeout
self.ready_timeout = ready_timeout
self.success_timeout = success_timeout
self.failure_timeout = failure_timeout
def to_yaml(self):
# Manual dump to avoid dependencies
return f'''output-dir: '{self.output_dir}'
batcmd: "{self.batcmd}"
schedcmd: "{self.schedcmd}"
simulation-timeout: {self.simulation_timeout}
ready-timeout: {self.ready_timeout}
success-timeout: {self.success_timeout}
failure-timeout: {self.failure_timeout}
'''
def to_file(self, filename):
create_dir_rec_if_needed(os.path.dirname(filename))
write_file(filename, self.to_yaml())
def gen_batsim_cmd(platform, workload, output_dir, more_flags):
return f"batsim -p '{platform}' -w '{workload}' -e '{output_dir}/' {more_flags}"
def write_file(filename, content):
file = open(filename, "w")
file.write(content)
file.close()
def create_dir_rec_if_needed(dirname):
if not os.path.exists(dirname):
os.makedirs(dirname)
def run_robin(filename):
return subprocess.run(['robin', filename])
# ############
# # FOR DATA VIZ
# ############
# class JobSetMulticore(JobSet):
# """Custom version of jobset to change the way 'utilisation' is computed."""
# def __init__(self, df, resource_bounds=None, float_precision=6):
# JobSet.__init__(self, df, resource_bounds, float_precision)
# self.MaxProcs = len(self.res_bounds) * 16 # because my machines have 16 cores
# @property # override
# def utilisation(self):
# if self._utilisation is not None:
# return self._utilisation
# self._utilisation = compute_load(self.df, col_begin='starting_time', col_end='finish_time',
# col_cumsum='requested_number_of_resources') # original: proc_alloc
# return self._utilisation
# def plot_load_and_details(expe_file):
# begin, end = 24 * 3600, 48 * 3600
# js = JobSetMulticore.from_csv(expe_file + "/_jobs.csv")
# #js.df = js.df[(js.df.submission_time >= begin) & (js.df.submission_time < end)]
# fig, axe = plt.subplots(nrows=2, sharex=True, figsize=(16, 8), tight_layout=True)
# fig.suptitle(expe_file, fontsize=16)
# vleg.plot_load(js.utilisation, js.MaxProcs, time_scale=False, ax=axe[0])
# vleg.plot_job_details(js.df, js.MaxProcs, time_scale=False, ax=axe[1])
# for ax in axe:
# ax.xaxis.set_ticks(np.arange(begin, end, 2*3600))
# ax.xaxis.set_ticklabels(np.arange(24, 48, 2))
# plt.xlim(begin, end)
# fig.savefig(expe_file + '_viz.png')
# plt.show()
# plt.close(fig)
# def energy_consumed_in(window, OUT_DIR):
# """Return the energy consumed during the time window (in kWh)."""
# # The problem is that batsim time series don't always have the specific
# # timestamp, we need to extrapolate
# data = pandas.read_csv(OUT_DIR + "/_consumed_energy.csv")
# energy = [0, 0]
# for i in range(2):
# first_greater = data[data['time'].ge(window[i])].index[0]
# df_entry = data.iloc[first_greater]
# energy[i] = df_entry['energy']
# delta_energy = (df_entry['time'] - window[i]) * df_entry['epower']
# energy[i] -= delta_energy
# return (energy[1] - energy[0]) / 3600 / 1000
# def scheduling_metrics_in(window, OUT_DIR):
# """Return the usual scheduling metrics for the subpart of jobs that have their submission time within the time window."""
# [inf, sup] = window
# data = pandas.read_csv(OUT_DIR + "/_jobs.csv")
# data_in_window = data[(data.submission_time
# >= inf) & (data.submission_time <= sup)]
# out = {}
# out["makespan"] = data_in_window["finish_time"].max() - inf
# out["#jobs"] = len(data_in_window.index)
# out["mean_waiting_time"] = data_in_window["waiting_time"].mean()
# out["max_waiting_time"] = data_in_window["waiting_time"].max()
# out["mean_slowdown"] = data_in_window["stretch"].mean()
# out["max_slowdown"] = data_in_window["stretch"].max()
# return out
\ No newline at end of file
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