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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"source": [
"application_latency_throughput_nocompute = \"/local/hindsightexperiments/application_latency_throughput2\"\n",
"application_latency_throughput = \"/local/hindsightexperiments/application_latency_throughput3\"\n",
"application_throughputs = \"/local/hindsightexperiments/application_throughputs\"\n",
"application_headsampling = \"/local/hindsightexperiments/application_headsampling3\"\n",
"application_headsampling_nocompute = \"/local/hindsightexperiments/application_headsampling\"\n"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 2,
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"%s/summary2.csv\" % application_throughputs)\n",
"\n",
"df[\"samplerate\"] = df[\"samplerate\"].astype(str)\n",
"df['label'] = df[[\"tracer\", \"samplerate\"]].agg('-'.join, axis=1)\n",
"\n",
"\n",
"closed_tputs = df[df.workload == \"closedloop\"][[\"label\", \"throughput\", \"throughput2\"]].groupby(\"label\").max()\n",
"throughputs = dict(zip(closed_tputs.index.values, closed_tputs[\"throughput2\"].values))\n",
"\n",
"print(\"Max attained throughput w/ closed-loop workload\")\n",
"for label, throughput in throughputs.items():\n",
" print(\"%s: %d\" % (label, throughput))\n",
"\n",
"\n",
"\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(111)\n",
"ax1.bar(closed_tputs.index.values, closed_tputs[\"throughput2\"].values)\n",
"plt.xticks(rotation=90)\n",
"plt.show()"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Max attained throughput w/ closed-loop workload\n",
"hindsight-0.0: 73671\n",
"hindsight-0.01: 72212\n",
"hindsight-0.1: 68459\n",
"hindsight-1.0: 66147\n",
"none-0.0: 76355\n",
"ot-jaeger-0.0: 75375\n",
"ot-jaeger-0.01: 72422\n",
"ot-jaeger-0.1: 64006\n",
"ot-jaeger-1.0: 33139\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 3,
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"%s/summary2.csv\" % application_latency_throughput)\n",
"\n",
"df[\"samplerate\"] = df[\"samplerate\"].astype(str)\n",
"df['label'] = df[[\"tracer\", \"samplerate\"]].agg('-'.join, axis=1)\n",
"\n",
"openloops = df[df.workload == \"openloop\"]\n",
"\n",
"# idx = openloops.groupby([\"label\", \"requests\"])['latency'].transform(max) == openloops['latency']\n",
"# maxes = openloops[idx]\n",
"# grouped_df = maxes.groupby(\"label\")\n",
"\n",
"print(\"Request rates: %s\" % \",\".join([str(v) for v in sorted(list(openloops[\"requests\"].unique()))]))\n",
"\n",
"grouped_df = openloops.groupby([\"label\", \"requests\"])[[\"latency\", \"throughput\", \"throughput2\"]].median().groupby([\"label\"])\n",
"\n",
"all = []\n",
"for key, item in grouped_df:\n",
" g = grouped_df.get_group(key).sort_values(\"requests\")\n",
" series = {\n",
" \"name\": key,\n",
" \"x\": list(g[\"throughput\"]),\n",
" \"y\": list(g[\"latency\"])\n",
" }\n",
" all.append(series)\n",
"\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(111)\n",
"ax1.set_yscale(\"log\")\n",
"ax1.set_ylim([0.1, 1000])\n",
"# ax1.set_xscale(\"log\")\n",
"# ax1.set_ylim([0.1, 1000])\n",
"ax1.set_xlabel(\"throughput (requests/s)\")\n",
"ax1.set_ylabel(\"mean request latency (ms)\")\n",
"\n",
"# show = [\"none-0.0\", \"hindsight-0.0\", \"hindsight-0.01\", \"ot-jaeger-1.0\", \"ot-jaeger-0.01\", \"ot-jaeger-0.1\"]\n",
"labels = {\n",
" \"none-0.0\": \"Tracing disabled\",\n",
" \"hindsight-0.0\": \"Hindsight\",\n",
" \"hindsight-0.01\": \"Hindsight 1% Trigger\",\n",
" # \"hindsight-0.1\": \"Hindsight 10% Trigger\",\n",
" # \"hindsight-1.0\": \"Hindsight 100% Trigger\",\n",
" \"ot-jaeger-1.0\": \"OT Jaeger Tail-Sampling\",\n",
" \"ot-jaeger-0.01\": \"OT Jaeger 1% Head-Sampling\",\n",
" \"ot-jaeger-0.1\": \"OT Jaeger 10% Head-Sampling\"\n",
"}\n",
"\n",
"for series in all:\n",
" if series[\"name\"] in labels:\n",
" ax1.plot(series[\"x\"], series[\"y\"],marker = 'o', label=labels[series[\"name\"]])\n",
"plt.legend(loc='upper left')\n",
"plt.show()\n",
"\n",
"# 1000, 2000, 4000, 8000, 12000\n",
"# 32,128,320,1280\n",
"# 32,64,128,256,"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Request rates: 1000,2000,3000,4000,5000,6000,7000,8000,9000,10000,11000,12000,13000,14000,15000,16000,17000,18000,19000,20000,21000,22000,23000,24000,25000,26000,27000,28000\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 4,
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"%s/summary2.csv\" % application_latency_throughput_nocompute)\n",
"\n",
"df[\"samplerate\"] = df[\"samplerate\"].astype(str)\n",
"df['label'] = df[[\"tracer\", \"samplerate\"]].agg('-'.join, axis=1)\n",
"\n",
"openloops = df[df.workload == \"openloop\"]\n",
"\n",
"# idx = openloops.groupby([\"label\", \"requests\"])['latency'].transform(max) == openloops['latency']\n",
"# maxes = openloops[idx]\n",
"# grouped_df = maxes.groupby(\"label\")\n",
"\n",
"print(\"Request rates: %s\" % \",\".join([str(v) for v in sorted(list(openloops[\"requests\"].unique()))]))\n",
"\n",
"grouped_df = openloops.groupby([\"label\", \"requests\"])[[\"latency\", \"throughput\", \"throughput2\"]].median().groupby([\"label\"])\n",
"\n",
"all = []\n",
"for key, item in grouped_df:\n",
" g = grouped_df.get_group(key).sort_values(\"requests\")\n",
" series = {\n",
" \"name\": key,\n",
" \"x\": list(g[\"throughput\"]),\n",
" \"y\": list(g[\"latency\"])\n",
" }\n",
" all.append(series)\n",
"\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(111)\n",
"ax1.set_yscale(\"log\")\n",
"ax1.set_ylim([0.1, 1000])\n",
"# ax1.set_xscale(\"log\")\n",
"# ax1.set_ylim([0.1, 1000])\n",
"ax1.set_xlabel(\"throughput (requests/s)\")\n",
"ax1.set_ylabel(\"mean request latency (ms)\")\n",
"\n",
"# show = [\"none-0.0\", \"hindsight-0.0\", \"hindsight-0.01\", \"ot-jaeger-1.0\", \"ot-jaeger-0.01\", \"ot-jaeger-0.1\"]\n",
"labels = {\n",
" \"none-0.0\": \"Tracing disabled\",\n",
" \"hindsight-0.0\": \"Hindsight\",\n",
" \"hindsight-0.01\": \"Hindsight 1% Trigger\",\n",
" # \"hindsight-0.1\": \"Hindsight 10% Trigger\",\n",
" # \"hindsight-1.0\": \"Hindsight 100% Trigger\",\n",
" \"ot-jaeger-1.0\": \"OT Jaeger Tail-Sampling\",\n",
" \"ot-jaeger-0.01\": \"OT Jaeger 1% Head-Sampling\",\n",
" \"ot-jaeger-0.1\": \"OT Jaeger 10% Head-Sampling\"\n",
"}\n",
"\n",
"for series in all:\n",
" if series[\"name\"] in labels:\n",
" ax1.plot(series[\"x\"], series[\"y\"],marker = 'o', label=labels[series[\"name\"]])\n",
"plt.legend(loc='upper left')\n",
"plt.show()\n",
"\n",
"# 1000, 2000, 4000, 8000, 12000\n",
"# 32,128,320,1280\n",
"# 32,64,128,256,"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Request rates: 2000,4000,6000,8000,10000,12000,14000,16000,18000,20000,22000,24000,26000,28000,30000,32000,34000,36000,38000,40000,42000,44000,46000,48000,50000,52000,54000,56000,58000,60000,62000,64000,66000,68000,70000,72000,74000,76000,78000,80000\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 13,
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"%s/summary2.csv\" % application_headsampling_nocompute)\n",
"\n",
"df[\"sampleratelabel\"] = df[\"samplerate\"].astype(str)\n",
"df['label'] = df[[\"tracer\", \"sampleratelabel\"]].agg('-'.join, axis=1)\n",
"\n",
"\n",
"closed_tputs = df[df.workload == \"closedloop\"][[\"label\", \"tracer\", \"samplerate\", \"throughput\", \"throughput2\"]].groupby([\"label\", \"tracer\", \"samplerate\"]).mean()\n",
"closed_tputs = closed_tputs.reset_index()\n",
"print(\"Sample rates: %s\" % \",\".join([str(v) for v in sorted(list(closed_tputs[\"samplerate\"].unique()))]))\n",
"\n",
"\n",
"xs = list(closed_tputs[closed_tputs[\"tracer\"] == \"ot-jaeger\"][\"samplerate\"])\n",
"\n",
"all = []\n",
"all.append({\n",
" \"name\": \"ot-jaeger\",\n",
" \"x\": xs,\n",
" \"y\": list(closed_tputs[closed_tputs[\"tracer\"] == \"ot-jaeger\"][\"throughput2\"])\n",
"})\n",
"\n",
"others = [\"hindsight\", \"none\"]\n",
"other_throughputs = dict()\n",
"for tracer in others:\n",
" if tracer in list(closed_tputs[\"tracer\"].unique()):\n",
" tput = list(closed_tputs[closed_tputs[\"tracer\"] == tracer][\"throughput2\"])[0]\n",
" other_throughputs[tracer] = tput\n",
" all.append({\n",
" \"name\": tracer,\n",
" \"x\": xs,\n",
" \"y\": [tput] * len(xs)\n",
" })\n",
" else:\n",
" print(\"No data for %s\" % tracer)\n",
"\n",
"if \"none\" in other_throughputs:\n",
" for tracer, tput in other_throughputs.items():\n",
" if tracer != \"none\":\n",
" print(\"%s throughput: %d (%.2f%%)\" % (tracer, tput, 100 * tput / float(other_throughputs[\"none\"])))\n",
" else:\n",
" print(\"%s throughput: %d\" % (tracer, tput))\n",
"else:\n",
" for tracer, tput in other_throughputs.items():\n",
" print(\"%s throughput: %d\" % (tracer, tput))\n",
"\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(111)\n",
"# ax1.set_yscale(\"log\")\n",
"ax1.set_ylim([0, 80000])\n",
"# ax1.set_xscale(\"log\")\n",
"ax1.set_xlim([0.0,1])\n",
"ax1.set_xlabel(\"head-sampling %\")\n",
"ax1.set_ylabel(\"application throughput (requests/s)\")\n",
"# ax1.set_ylim([0.1, 1000])\n",
"\n",
"labels = {\n",
" \"none\": \"Tracing disabled\",\n",
" \"hindsight\": \"Hindsight\",\n",
" \"ot-jaeger\": \"OT Jaeger\",\n",
"}\n",
"\n",
"for series in all:\n",
" ax1.plot(series[\"x\"], series[\"y\"],marker = 'o', label=labels[series[\"name\"]])\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(111)\n",
"# ax1.set_yscale(\"log\")\n",
"ax1.set_ylim([0, 80000])\n",
"ax1.set_xscale(\"log\")\n",
"ax1.set_xlim([0.001,1])\n",
"ax1.set_xlabel(\"head-sampling %\")\n",
"ax1.set_ylabel(\"application throughput (requests/s)\")\n",
"\n",
"for series in all:\n",
" ax1.plot(series[\"x\"], series[\"y\"],marker = 'o', label=labels[series[\"name\"]])\n",
"plt.legend()\n",
"plt.show()"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sample rates: 0.0,0.00125,0.0025,0.005,0.01,0.025,0.05,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0\n",
"hindsight throughput: 72218 (95.77%)\n",
"none throughput: 75412\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
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},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 12,
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\"%s/summary2.csv\" % application_headsampling)\n",
"\n",
"df[\"sampleratelabel\"] = df[\"samplerate\"].astype(str)\n",
"df['label'] = df[[\"tracer\", \"sampleratelabel\"]].agg('-'.join, axis=1)\n",
"\n",
"\n",
"closed_tputs = df[df.workload == \"closedloop\"][[\"label\", \"tracer\", \"samplerate\", \"throughput\", \"throughput2\"]].groupby([\"label\", \"tracer\", \"samplerate\"]).mean()\n",
"closed_tputs = closed_tputs.reset_index()\n",
"print(\"Sample rates: %s\" % \",\".join([str(v) for v in sorted(list(closed_tputs[\"samplerate\"].unique()))]))\n",
"\n",
"\n",
"xs = list(closed_tputs[closed_tputs[\"tracer\"] == \"ot-jaeger\"][\"samplerate\"])\n",
"\n",
"all = []\n",
"all.append({\n",
" \"name\": \"ot-jaeger\",\n",
" \"x\": xs,\n",
" \"y\": list(closed_tputs[closed_tputs[\"tracer\"] == \"ot-jaeger\"][\"throughput2\"])\n",
"})\n",
"\n",
"others = [\"hindsight\", \"none\"]\n",
"other_throughputs = dict()\n",
"for tracer in others:\n",
" if tracer in list(closed_tputs[\"tracer\"].unique()):\n",
" tput = list(closed_tputs[closed_tputs[\"tracer\"] == tracer][\"throughput2\"])[0]\n",
" other_throughputs[tracer] = tput\n",
" all.append({\n",
" \"name\": tracer,\n",
" \"x\": xs,\n",
" \"y\": [tput] * len(xs)\n",
" })\n",
" else:\n",
" print(\"No data for %s\" % tracer)\n",
"\n",
"if \"none\" in other_throughputs:\n",
" for tracer, tput in other_throughputs.items():\n",
" if tracer != \"none\":\n",
" print(\"%s throughput: %d (%.2f%%)\" % (tracer, tput, 100 * tput / float(other_throughputs[\"none\"])))\n",
" else:\n",
" print(\"%s throughput: %d\" % (tracer, tput))\n",
"else:\n",
" for tracer, tput in other_throughputs.items():\n",
" print(\"%s throughput: %d\" % (tracer, tput))\n",
"\n",
"\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(111)\n",
"# ax1.set_yscale(\"log\")\n",
"ax1.set_ylim([0, 25000])\n",
"# ax1.set_xscale(\"log\")\n",
"ax1.set_xlim([0.0,1])\n",
"ax1.set_xlabel(\"head-sampling %\")\n",
"ax1.set_ylabel(\"application throughput (requests/s)\")\n",
"# ax1.set_ylim([0.1, 1000])\n",
"\n",
"labels = {\n",
" \"none\": \"Tracing disabled\",\n",
" \"hindsight\": \"Hindsight\",\n",
" \"ot-jaeger\": \"OT Jaeger\",\n",
"}\n",
"\n",
"for series in all:\n",
" ax1.plot(series[\"x\"], series[\"y\"],marker = 'o', label=labels[series[\"name\"]])\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"fig = plt.figure()\n",
"ax1 = fig.add_subplot(111)\n",
"# ax1.set_yscale(\"log\")\n",
"ax1.set_ylim([0, 25000])\n",
"ax1.set_xscale(\"log\")\n",
"ax1.set_xlim([0.001,1])\n",
"ax1.set_xlabel(\"head-sampling %\")\n",
"ax1.set_ylabel(\"application throughput (requests/s)\")\n",
"\n",
"for series in all:\n",
" ax1.plot(series[\"x\"], series[\"y\"],marker = 'o', label=labels[series[\"name\"]])\n",
"plt.legend()\n",
"plt.show()"
],
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sample rates: 0.0,0.00125,0.0025,0.005,0.01,0.025,0.05,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0\n",
"hindsight throughput: 21670 (95.64%)\n",
"none throughput: 22659\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
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"
},
"metadata": {
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
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"
},
"metadata": {
"needs_background": "light"
}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [],
"outputs": [],
"metadata": {}
}
],
"metadata": {
"orig_nbformat": 4,
"language_info": {
"name": "python",
"version": "3.7.3",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3.7.3 64-bit"
},
"interpreter": {
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}