Commit 6674aef1 by Stefan Reif

### RTN'18 dump

parent a96c866f
 ... ... @@ -29,7 +29,7 @@ def duration_to_string(d): return str(d['Start']) + "->" + str(d['Stop']) def analyse(df1, df2, config, export=False): for d in latency.get_durations(df1, config): for d in latency.get_durations_2([df1, df2], config): data1 = df1[d['Stop']] - df1[d['Start']] data2 = df2[d['Stop']] - df2[d['Start']] if samples_are_different(remove_outliers(data1), remove_outliers(data2)) and samples_are_different(data1, data2): ... ...
 import math import numpy as np from .util import cdf, extract_durations from scipy import stats import matplotlib.pyplot as plt import xlap.analyse.latency as latency # Taken from: http://composition.al/blog/2015/11/29/a-better-way-to-add-labels-to-bar-charts-with-matplotlib/ def autolabel(rects, ax, labels): # Get y-axis height to calculate label position from. (x_left, x_right) = ax.get_xlim() x_width = x_right - x_left for i, rect in enumerate(rects): width = rect.get_width() color = "black" align = "left" # Fraction of axis height taken up by this rectangle p_width = (width / x_width) # If we can fit the label above the column, do that; # otherwise, put it inside the column. if p_width > 0.50: # arbitrary; 95% looked good to me. label_position = width - (x_width * 0.01) color = "white" align = "right" else: label_position = width + (x_width * 0.01) mono = {'family': 'monospace'} ax.text(label_position, rect.get_y(), labels[i], ha=align, va='bottom', rotation=0, color=color, fontdict=mono) def _plot_regions(dataset): """ plot regions, sorted by latency criticality """ relevant = sorted([x for x in dataset if x['Slowdown'] > 0 and x['Slowdown'] <= 5], key=lambda x: x['Slowdown']) x = np.arange(len(relevant)) correlations = list(map(lambda x: x['Slowdown'], relevant)) ticks = list(map(lambda x: "<%s,%s>" % (x['Start'][:-2], x['End'][:-2]), relevant)) fig, ax = plt.subplots() rects = ax.barh(x, correlations, align="center", tick_label="") autolabel(rects, ax, ticks) plt.tight_layout() plt.savefig("normalized-slowdown.pdf") plt.close() def duration_to_string(d): return str(d['Start']) + "->" + str(d['Stop']) def analyse(df1, df2, config, export=False): data1 = df1['EndToEnd_D'] data2 = df2['EndToEnd_D'] print("#1 e2e: " + str(np.mean(data1)) + " +- " + str(np.std(data1))) print("#2 e2e: " + str(np.mean(data2)) + " +- " + str(np.std(data2))) normalize = np.mean(data1) / np.mean(data2) dataset = [] for d in latency.get_durations_2([df1, df2], config): local1 = df1[d['Stop']] - df1[d['Start']] local2 = df2[d['Stop']] - df2[d['Start']] # too short -> ignore if np.mean(local1) == 0 or np.mean(local2) == 0: continue slowdown = np.mean(local1) / np.mean(local2) #print(duration_to_string(d)+" "+str(lf) + " / "+str(frac)+ " = " + str(lf/frac)) #print("\"<"+d['Start']+","+d['Stop']+">\" "+str(slowdown/normalize)+" "+str(slowdown)+" "+str(normalize)) dataset += [{ 'Start': d['Start'], 'End': d['Stop'], 'Slowdown': slowdown }] _plot_regions(dataset)
 ... ... @@ -204,6 +204,10 @@ def get_durations(df, config): hdb += [{'Start': str(event1), 'Stop': str(event2), 'Source': 'cfa'}] return hdb def get_durations_2(dfs, config): df = dfs[0] + dfs[1] return get_durations(df, config) def analyse(df, config): hb = [] ... ...
 ... ... @@ -4,11 +4,13 @@ from xlap.parse import evaluate, parse_config import xlap.analyse.jitter as jitter import xlap.analyse.latency as latency import xlap.analyse.diff as difference import xlap.analyse.e2e as e2e tasks = { "jitter": None, "latency": None, "difference": None, "e2e": None, "capture": None } ... ... @@ -43,18 +45,29 @@ def main(): f.write("\n") else: print(output) elif command == "e2e": khz1 = 2000000 khz2 = 3000000 path="../publications/nsdi-18/eval/20180419_energy/" df_data1 = evaluate(path+"sender-"+str(khz1)+".csv", path+"receiver-"+str(khz1)+".csv", config=config, kind=0) df_data2 = evaluate(path+"sender-"+str(khz2)+".csv", path+"receiver-"+str(khz2)+".csv", config=config, kind=0) e2e.analyse(df_data1, df_data2, config) elif command == "latency": df_data = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0) a = latency.analyse(df_data, config) print(a.corr.sort_values(ascending=False)) elif command == "difference": df_data1 = evaluate("../prrt/out/s.csv", "../prrt/out/r.csv", config=config, kind=0) path="../publications/nsdi-18/eval/20180420_" khz = 3000000 df_data1 = evaluate(path+"base1/sender-"+str(khz)+".csv", path+"base1/receiver-"+str(khz)+".csv", config=config, kind=0) # sanity check: #df_data2 = evaluate("../prrt/out/s.csv", "../prrt/out/r.csv", config=config, kind=0) # same setup, different measurement run: #df_data2 = evaluate("../prrt/out/s+same.csv", "../prrt/out/r+same.csv", config=config, kind=0) # different setup: df_data2 = evaluate("../prrt/out/s+send.csv", "../prrt/out/r+send.csv", config=config, kind=0) #df_data2 = evaluate("../prrt/out/s+send.csv", "../prrt/out/r+send.csv", config=config, kind=0) df_data2 = evaluate(path+"base2/sender-"+str(khz)+".csv", path+"base2/receiver-"+str(khz)+".csv", config=config, kind=0) #df_data2 = evaluate(path+"changed/sender-"+str(khz)+".csv", path+"changed/receiver-"+str(khz)+".csv", config=config, kind=0) difference.analyse(df_data1, df_data2, config) else: df_data = evaluate(data_files["sender"], data_files["receiver"], config=config, kind=0) ... ...
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