def prepare_paper(path='logs/nsdi19'):
path = Path(path)
with plt.style.context(['seaborn-paper', 'mypaper', 'line12']):
# also use color
# fifo = ju.load_trace(path/'card266'/'salus'/'trace.csv')
fifo = load_data(path / 'card272' / 'case0' / 'fifo', 'case0.output')
srtf = load_data(path / 'card266' / 'salus', 'card266.output')
srtf_refine = ju.load_refine(path / 'card266' / 'salus')
fair = load_data(path / 'card272' / 'case2' / 'salus', 'case2.output')
pack = load_data(path / 'card272' / 'case1' / 'salus', 'case1.output')
fig, ax = plt.subplots()
fig.set_size_inches(3.25, 1.5, forward=True)
jcts = pd.DataFrame({
'FIFO': fifo.JCT,
'SRTF': srtf.JCT,
'PACK': pack.JCT,
'FAIR': fair.JCT
})
plot_jcts(jcts, ax=ax, markevery=0.1, markersize=4, linewidth=1)
fig.tight_layout()
fig.savefig('/tmp/workspace/card272-jct.pdf', dpi=300)
plt.close()
return fifo, srtf, srtf_refine, fair, pack
def do_srtf2(path):
srtf = load_data(path / 'card266' / 'salus', 'card266.output')
srtf_refine = ju.load_refine(path / 'card266' / 'salus')
offset_local = srtf.Queued.min()
#srtf = ju.update_start_time_using_refine(srtf, srtf_refine, offset=offset_local)
jnos = [34, 80, 82, 86, 93, 94]
new_jnos = {n: i for i, n in enumerate(jnos)}
st_sec = 2651
ed_sec = 2910
srtf = srtf[srtf.No.isin(jnos)]
#colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2']
with plt.style.context(['seaborn-paper', 'mypaper', 'color3']):
# first do a refine plot to update 'Started' in srtf
ax = plt.gca()
ju.plot_refine(ax,
srtf,
srtf_refine,
offset=offset_local,
new_jnos=new_jnos,
plot_offset=-st_sec)
plt.close()
fig, ax = plt.subplots()
monochrome = cycler('color', ['0.0'])
ax.set_prop_cycle(monochrome)
ju.plot_timeline(srtf.drop(['LaneId'], axis=1),
ax=ax,
linewidth=2.5,
offset=offset_local,
new_jnos=new_jnos,
plot_offset=-st_sec)
ju.plot_refine(ax,
srtf,
srtf_refine,
offset=offset_local,
new_jnos=new_jnos,
plot_offset=-st_sec)
ax.set_xlim([0, ed_sec - st_sec])
ax.set_ylabel('Job #')
ax.yaxis.set_ticks([0, 1, 2, 3, 4, 5])
fig.set_size_inches(4.875, 2, forward=True)
fig.savefig('/tmp/workspace/card274-srtf-compute.pdf',
dpi=300,
bbox_inches='tight',
pad_inches=.015)
plt.close()
def do_timelines(path):
path = Path(path)
# fifo = ju.load_trace(path/'card266'/'salus'/'trace.csv')
fifo = load_data(path / 'card272' / 'case0' / 'fifo', 'case0.output')
srtf = load_data(path / 'card266' / 'salus', 'card266.output')
srtf_refine = ju.load_refine(path / 'card266' / 'salus')
fair = load_data(path / 'card272' / 'case2' / 'salus', 'case2.output')
pack = load_data(path / 'card272' / 'case1' / 'salus', 'case1.output')
with plt.style.context(['seaborn-paper', 'mypaper', 'color4']):
fig, axs = plt.subplots(nrows=4, sharex=True)
fig.set_size_inches(3.25, 5.35, forward=True)
_, colors = ju.plot_timeline(fifo, ax=axs[0], linewidth=2.5)
axs[0].set_ylabel('FIFO')
axs[0].legend().remove()
axs[0].set_xlabel('')
ju.plot_timeline(srtf.drop(['LaneId'], axis=1),
ax=axs[1],
linewidth=2.5,
colors=colors)
ju.plot_refine(axs[1], srtf, srtf_refine)
axs[1].set_ylabel('SRTF')
ju.plot_timeline(pack, ax=axs[2], linewidth=2.5, colors=colors)
axs[2].set_ylabel('PACK')
ju.plot_timeline(fair.drop(['LaneId'], axis=1),
ax=axs[3],
linewidth=2.5,
colors=colors)
axs[3].set_ylabel('FAIR')
#fig.subplots_adjust(bottom=0.35)
#axs[-1].legend(loc="upper center", frameon=False,
# bbox_to_anchor=[0.5, -0.8],
# #bbox_transform=fig.transFigure,
# fontsize='x-small',
# ncol=3
# #mode='expand'
# )
fig.tight_layout()
fig.savefig('/tmp/workspace/card272.pdf', dpi=300)
def do_srtf(path):
srtf = load_data(path / 'card272' / 'case5' / 'salus', 'case5.output')
srtf_refine = ju.load_refine(path / 'card272' / 'case5' / 'salus')
df = cm.load_mem(path / 'card274/srtf/alloc.output')
offset_local = srtf.Queued.min()
# timeline logs in US/Eastern, but server logs in UTC
# convert offset from US/Eastern to UTC
offset_server = offset_local.tz_localize('US/Eastern').tz_convert(
'UTC').tz_localize(None)
# select sess name after calc the offset
# use jno = [13, 14, 15]
jnos = [13, 14, 15]
srtf = srtf[srtf.No.isin(jnos)]
#srtf_refine = srtf_refine[srtf_refine.No.isin(jnos)]
# select data in range
st_sec = 400
ed_sec = 515
st = offset_server + pd.to_timedelta(st_sec, 's')
ed = offset_server + pd.to_timedelta(ed_sec, 's')
df = df[(df.timestamp >= st) & (df.timestamp <= ed)]
df = df[df.Sess.isin(srtf.Sess.unique())]
sess2Model = srtf.set_index('Sess').Model.str.split('.', expand=True)
sess2Model['Label'] = sess2Model.apply(
lambda x: '#{}: {}'.format(x[3], x[0]), axis=1)
df['Sess'] = df.Sess.map(sess2Model.Label)
#colors = ['#1f77b4', '#ff7f0e', '#2ca02c']
with plt.style.context(['seaborn-paper', 'mypaper', 'color3']):
fig, axs = plt.subplots(nrows=2,
sharex=True,
gridspec_kw={'height_ratios': [1, 4]})
_, _, sessProps = ju.plot_timeline(srtf.drop(['LaneId'], axis=1),
ax=axs[0],
linewidth=2.5,
returnSessProps=True,
offset=offset_local)
ju.plot_refine(axs[0], srtf, srtf_refine, offset=offset_local)
#axs[0].set_ylabel('Job No.')
# srtf is selected by simply set axis limit, because they are not easy to cut in data
axs[0].set_xlim([st_sec, ed_sec])
axs[0].set_ylim([12.45, 15.5])
#axs[0].set_ylabel('Job', rotation=0, labelpad=20)
sessProps = {
row.Label: sessProps[sess]
for sess, row in sess2Model.iterrows()
}
cm.plot_all(df, offset=offset_server, ax=axs[1], sessProps=sessProps)
axs[1].set_ylim(bottom=0)
#axs[1].legend().remove()
# Use axs[0]'s legend, but put it at axs[1]'s legend's place
fig.subplots_adjust(bottom=0.15, hspace=0.05)
axs[1].legend(
#*axs[0].get_legend_handles_labels(),
loc="lower center",
frameon=False,
bbox_to_anchor=[0.5, 1],
#bbox_transform=fig.transFigure,
fontsize='x-small',
ncol=4
#mode='expand'
)
axs[1].xaxis.set_major_locator(pu.MaxNLocator(nbins=5))
axs[1].minorticks_off()
pu.cleanup_axis_bytes(axs[1].yaxis, maxN=5)
axs[1].set_ylabel('Memory Usage')
axs[1].set_xlabel('Time (s)')
#fig.tight_layout()
fig.set_size_inches(6.5, 2, forward=True)
fig.savefig('/tmp/workspace/card274-srtf.pdf',
dpi=300,
bbox_inches='tight',
pad_inches=.015)
