Révision 308a0754
| b/papers/2014/reservation/experiments/src/python/lib/kadeploy.py | ||
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| 7 | 7 |
import numpy as np |
| 8 | 8 |
from matplotlib import rc |
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from pylab import * |
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from scipy.stats import gaussian_kde |
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| 10 |
from scipy.stats import gaussian_kde, lognorm, rayleigh, norm |
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import scipy.stats as stats |
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rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
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rc('text', usetex=True)
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plt.rcParams.update({'font.size': 14})
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| ... | ... | |
| 20 | 21 |
def __init__(self, host, start, step1_dur, step2_dur, step3_dur, success): |
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self._host = host |
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self._start = start |
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self._step1_dur = step1_dur
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self._step2_dur = step2_dur
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self._step3_dur = step3_dur
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self._step1_dur = int(step1_dur)
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self._step2_dur = int(step2_dur)
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self._step3_dur = int(step3_dur)
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self._success = success |
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self._retry_1 = 0 |
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self._retry_2 = 0 |
| ... | ... | |
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return self._step3_dur |
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@property |
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def deployment_duration(self): |
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return self.step_1_duration + self.step_2_duration + self.step_3_duration |
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@property |
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def success(self): |
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return self._success |
| 54 | 59 |
|
| ... | ... | |
| 133 | 138 |
values = [] |
| 134 | 139 |
for d in deployments: |
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if d.success and not d.has_retries: |
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values.append(int(d.step_1_duration + d.step_2_duration + d.step_3_duration)) |
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value = d.deployment_duration |
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if d.step_1_duration > 5 and d.step_2_duration > 10 and d.step_3_duration > 10: |
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values.append(value) |
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# values.append(value) |
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# values.append(int(d.step_3_duration)) |
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# values.append(int(d.step_1_duration)) |
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title = 'Deployment on Cluster %s (%d)' % (cluster, year) |
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# title = 'Deployment on Cluster %s' % (cluster) |
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out_file = out_dir + ("/deployment_%s_%d.pdf" % (cluster, year))
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# out_file = out_dir + ("/deployment_%s.pdf" % (cluster))
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hist_graph(values, title, 'Deployment Time (seconds)', '', out_file) |
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| 144 | 155 |
|
| ... | ... | |
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ar1 = np.array(values) |
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fig, ax = plt.subplots() |
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num_bins = 20
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num_bins = 50
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n, bins, patches = plt.hist(ar1, bins=num_bins, histtype='bar', |
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normed=True, facecolor='#708090', alpha=0.5, rwidth=0.8) |
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plt.subplots_adjust(left=0.15) |
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max = round(bins[len(bins) - 1]) |
| 155 | 166 |
density = gaussian_kde(ar1) |
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xs = np.linspace(0,max,300)
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xs = np.linspace(0, max, 300)
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density.covariance_factor = lambda : .25 |
| 158 | 169 |
density._compute_covariance() |
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plt.plot(xs,density(xs), 'r--')
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# plt.plot(xs, density(xs), 'r--')
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ax.set_xlabel(x_label) |
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ax.set_ylabel(y_label) |
| ... | ... | |
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ax.get_xaxis().tick_bottom() |
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ax.get_yaxis().tick_left() |
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# # original distribution |
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# pdf = rayleigh.pdf(xs, loc=120, scale=230) |
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# plt.plot(xs, pdf, 'r-') |
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# |
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# param = rayleigh.fit(ar1) |
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# pdf_fitted = rayleigh.pdf(xs, loc=param[0], scale=param[1]) |
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# plt.plot(xs, pdf_fitted, 'b') |
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# original distribution |
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# pdf = lognorm.pdf(xs, 0.4, loc=10, scale=370) |
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# plt.plot(xs, pdf, 'r-') |
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dist_names = ['lognorm', 'beta', 'betaprime', 'gamma', 'rayleigh'] |
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for dist_name in dist_names: |
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dist = getattr(stats, dist_name) |
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param = dist.fit(ar1) |
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pdf_fitted = dist.pdf(xs, *param[:-2], loc=param[-2], scale=param[-1]) |
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plt.plot(xs, pdf_fitted, label=dist_name) |
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plt.xlim(0, max) |
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plt.legend(loc='upper right') |
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# param = lognorm.fit(ar1) |
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# pdf_fitted = lognorm.pdf(xs, param[0], loc=param[1], scale=param[2]) |
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# plt.plot(xs, pdf_fitted, 'b') |
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# param = norm.fit(ar1) |
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# pdf_fitted = norm.pdf(xs, loc=param[0], scale=param[1]) |
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# plt.plot(xs, pdf_fitted, 'b') |
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#print param |
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plt.show() |
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# plt.savefig(out_file) |
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def plot_graphs(): |
| 172 | 218 |
opts = parse_kdeploy_opt() |
| 173 | 219 |
deployments = parse_log(opts.input, opts.cluster) |
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