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**/.ipynb_checkpoints/
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task_slowdown/?_state_changes.json.gz
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**/*.gz
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#!/usr/bin/env python3
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# coding: utf-8
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import json
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import pandas
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import findspark
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findspark.init()
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import pyspark
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import pyspark.sql
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import sys
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import gzip
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from pyspark import AccumulatorParam
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from pyspark.sql.functions import lit
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from pyspark.sql import Window
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from pyspark.sql.types import ByteType
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if len(sys.argv) is not 4:
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print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
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sys.exit()
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cluster=sys.argv[1]
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spark = pyspark.sql.SparkSession.builder \
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.appName("task_slowdown") \
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.config("spark.driver.maxResultSize", "128g") \
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.config("spark.local.dir", sys.argv[2]) \
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.config("spark.driver.memory", sys.argv[3]) \
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.getOrCreate()
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sc = spark.sparkContext
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df = spark.read.json("/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_instance_events*.json.gz")
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#df = spark.read.json("/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_test.json")
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try:
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df["collection_type"] = df["collection_type"].cast(ByteType())
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except:
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df = df.withColumn("collection_type", lit(None).cast(ByteType()))
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class NonPriorityAcc(pyspark.AccumulatorParam):
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def zero(self, value):
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return {}
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def addInPlace(self, v1, v2):
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for key in v2:
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if key in v1:
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v1[key] += v2[key]
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else:
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v1[key] = v2[key]
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return v1
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non = sc.accumulator({}, NonPriorityAcc())
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MICROS = 1000000
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def sumrow(l, p, t, c):
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t = t // (MICROS * 60)
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if t < 1:
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t = "<1"
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elif t < 2:
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t = "1-2"
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elif t < 4:
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t = "2-4"
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elif t < 10:
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t = "4-10"
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elif t < 60:
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t = "10-60"
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elif t < 60 * 24:
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t = "60-1d"
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else:
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t = ">=1d"
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return (l, p, t, c)
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def sumid(sr):
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return (sr[0], sr[1], sr[2])
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def for_each_task(ts):
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global non
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ts = sorted(ts, key=lambda x: x["time"])
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in_exec = False
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exec_start = None
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exec_tot = 0
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priority = 0
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l = len(ts)
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last_term = -1
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for i,t in enumerate(ts):
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if t["priority"] is not -1 and priority is -1:
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priority = t["priority"]
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if t["type"] >= 4 and t["type"] <= 8:
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last_term = t["type"]
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if in_exec and (t["type"] == 1 or (t["type"] >= 4 and t["type"] <= 8)):
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exec_tot += t["time"] - exec_start
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in_exec = False
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if (not in_exec) and (t["type"] == 3):
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exec_start = t["time"]
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in_exec = True
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return sumrow(last_term, priority, exec_tot, l)
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def cleanup(x):
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return {
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"time": int(x.time),
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"type": 0 if x.type is None else int(x.type),
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"id": x.collection_id + "-" + x.instance_index,
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"priority": -1 if x.priority is None else int(x.priority)
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}
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def sum_rows(xs):
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csum = 0
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for x in xs:
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csum += x[3]
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return csum
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df2 = df.rdd \
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.filter(lambda x: x.collection_type is None or x.collection_type == 0) \
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.filter(lambda x: x.time is not None and x.instance_index is not None and x.collection_id is not None) \
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.map(cleanup) \
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.groupBy(lambda x: x["id"]) \
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.mapValues(for_each_task) \
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.map(lambda x: x[1]) \
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.groupBy(lambda x: sumid(x)) \
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.mapValues(sum_rows) \
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.map(lambda x: str(x[0][0]) + "," + str(x[0][1]) + "," + str(x[0][2]) + "," + str(x[1])) \
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.coalesce(1) \
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.saveAsTextFile(cluster + "_priority_exectime")
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# vim: set ts=4 sw=4 et tw=80:
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6,0,<1,5855111
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7,0,60-1d,601542196
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7,0,10-60,95838227
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6,0,>=1d,530133143
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4,0,>=1d,36971224
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5,0,60-1d,1010045
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8,0,<1,3811
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8,0,>=1d,263540
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5,0,2-4,22902724
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5,0,4-10,6661506
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7,0,<1,12043755
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6,0,60-1d,10739044
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7,0,2-4,16985420
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4,0,1-2,2714
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7,0,1-2,6803910
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5,0,>=1d,4610298
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4,0,<1,1840
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7,0,4-10,36246298
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6,0,10-60,19503280
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7,0,>=1d,1599925741
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6,0,1-2,8607061
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5,0,<1,275553
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4,0,60-1d,6441519
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8,0,60-1d,1244
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4,0,2-4,8100
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4,0,10-60,141727
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5,0,10-60,2046864
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6,0,4-10,9704610
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5,0,1-2,23954
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6,0,2-4,10143359
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4,0,4-10,35493
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-1,0,<1,397201
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8,0,>=1d,5054
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-1,0,<1,954975
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7,0,<1,24484463
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6,0,60-1d,23056333
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4,0,2-4,1836
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4,0,4-10,8989
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8,0,<1,124
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7,0,2-4,23796068
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7,0,>=1d,1239402690
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5,0,4-10,1489254
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6,0,>=1d,78233744
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7,0,4-10,62455654
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6,0,4-10,11418060
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6,0,2-4,11039358
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4,0,10-60,40994
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7,0,60-1d,722926038
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5,0,>=1d,8729456
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7,0,1-2,4366627
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5,0,<1,189088
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5,0,60-1d,1497194
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8,0,60-1d,1922
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5,0,2-4,16174
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6,0,1-2,4843246
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4,0,<1,779
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6,0,10-60,19472894
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4,0,1-2,791
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7,0,10-60,138882516
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4,0,60-1d,953635
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4,0,>=1d,4032484
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6,0,<1,13791195
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5,0,1-2,3721
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5,0,10-60,325669
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#!/usr/bin/env python3
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# coding: utf-8
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|
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import os
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import json
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import pandas as pd
|
||||
import findspark
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findspark.init()
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import pyspark
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import pyspark.sql
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import sys
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import gzip
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from pyspark import AccumulatorParam
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from pyspark.sql.functions import lit
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from pyspark.sql import Window
|
||||
from pyspark.sql.types import *
|
||||
from decimal import *
|
||||
|
||||
TESTDATA = False
|
||||
|
||||
if len(sys.argv) is not 4:
|
||||
print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
|
||||
sys.exit()
|
||||
|
||||
cluster=sys.argv[1]
|
||||
|
||||
spark = pyspark.sql.SparkSession.builder \
|
||||
.appName("task_slowdown") \
|
||||
.config("spark.driver.maxResultSize", "128g") \
|
||||
.config("spark.local.dir", sys.argv[2]) \
|
||||
.config("spark.driver.memory", sys.argv[3]) \
|
||||
.getOrCreate()
|
||||
sc = spark.sparkContext
|
||||
|
||||
#
|
||||
# READING INSTANCE EVENTS DATA
|
||||
#
|
||||
dfepath = "/home/claudio/google_2019/instance_events/" + cluster
|
||||
dfepath += "/" + cluster + ("_instance_events00000000000?.json.gz" if TESTDATA else "_instance_events*.json.gz")
|
||||
dfe = spark.read.json(dfepath)
|
||||
#
|
||||
# READING INSTANCE USAGE DATA
|
||||
#
|
||||
dfupath = "/home/claudio/google_2019/instance_usage/" + cluster
|
||||
dfupath += "/" + cluster + ("_instance_usage00000000000?.csv.gz" if TESTDATA else "_instance_usage*.csv.gz")
|
||||
usage_schema = StructType() \
|
||||
.add("start_time", LongType(), True) \
|
||||
.add("end_time", LongType(), True) \
|
||||
.add("collection_id", StringType(), True) \
|
||||
.add("instance_index", StringType(), True) \
|
||||
.add("machine_id", StringType(), True) \
|
||||
.add("alloc_collection_id", LongType(), True) \
|
||||
.add("alloc_instance_index", StringType(), True) \
|
||||
.add("collection_type", ByteType(), True) \
|
||||
.add("average_usage_cpus", DoubleType(), True) \
|
||||
.add("average_usage_memory", DoubleType(), True) \
|
||||
.add("maximum_usage_cpus", DoubleType(), True) \
|
||||
.add("maximum_usage_memory", DoubleType(), True) \
|
||||
.add("random_sample_usage_cpus", DoubleType(), True) \
|
||||
.add("random_sample_usage_memory", DoubleType(), True) \
|
||||
.add("assigned_memory", DoubleType(), True) \
|
||||
.add("page_cache_memory", DoubleType(), True) \
|
||||
.add("cycles_per_instruction", DoubleType(), True) \
|
||||
.add("memory_accLesses_per_instruction", DoubleType(), True) \
|
||||
.add("sample_rate", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_00", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_10", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_20", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_30", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_40", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_50", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_60", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_70", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_80", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_90", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_91", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_92", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_93", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_94", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_95", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_96", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_97", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_98", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_99", DoubleType(), True)
|
||||
|
||||
dfu = spark.read.format("csv") \
|
||||
.option("header", False) \
|
||||
.schema(usage_schema) \
|
||||
.load(dfupath)
|
||||
|
||||
# READING MACHINE EVENTS DATA, sort them and save them as broadcast variable
|
||||
print("Starting to read machine events...")
|
||||
dfm = pd.read_csv("~/google_2019/machine_events/" + cluster + "_machine_events.csv")
|
||||
print("Dropping remove events...")
|
||||
dfm = dfm[(dfm.type==1)|(dfm.type==3)]
|
||||
print("Dropping missing data events...")
|
||||
dfm = dfm[dfm.missing_data_reason.notnull()]
|
||||
print("Projecting on useful columns...")
|
||||
dfm = dfm[["time", "machine_id", "capacity.cpus", "capacity.memory"]]
|
||||
print("Sorting by time...")
|
||||
dfm = dfm.sort_values(by=["machine_id", "time"])
|
||||
print("Converting to broadcast variable...")
|
||||
dfm = sc.broadcast([tuple(r) for r in dfm.to_numpy()])
|
||||
print("Done with machine events.")
|
||||
|
||||
def tabid(x):
|
||||
return Decimal(x.collection_id) + Decimal(x.instance_index) / Decimal(2**64)
|
||||
|
||||
# interpolate machine data by extending each last machine report before a gap to cover it
|
||||
def clean_usage(x):
|
||||
return [tabid(x), Decimal(x.average_usage_cpus), Decimal(x.average_usage_memory), int(x.start_time),
|
||||
int(x.end_time), x.machine_id]
|
||||
|
||||
def interpolate_usage(ts):
|
||||
ts = sorted(ts, key=lambda x: x[3])
|
||||
l = len(ts)
|
||||
for i in range(1, l-1):
|
||||
if ts[i+1][3] > ts[i][4]:
|
||||
ts[i][4] = ts[i+1][3]
|
||||
return ts
|
||||
|
||||
dfu = dfu.rdd \
|
||||
.filter(lambda x: x.start_time is not None and x.end_time is not None and
|
||||
x.instance_index is not None and x.collection_id is not None and
|
||||
x.machine_id is not None) \
|
||||
.map(clean_usage).groupBy(lambda x: x[0]) \
|
||||
.flatMap(lambda x: interpolate_usage(x[1])).toDF(["id", "acpu", "aram", "start", "end", "mid"])
|
||||
|
||||
try:
|
||||
dfe["collection_type"] = dfe["collection_type"].cast(ByteType())
|
||||
except:
|
||||
dfe = dfe.withColumn("collection_type", lit(None).cast(ByteType()))
|
||||
|
||||
dfe = dfe.rdd \
|
||||
.filter(lambda x: x.time is not None and x.type is not None and x.machine_id is not None and
|
||||
x.instance_index is not None and x.collection_id is not None and x.resource_request is not None and
|
||||
x.resource_request.cpus is not None and x.resource_request.memory is not None) \
|
||||
.map(lambda x: [tabid(x), int(x.time), int(x.type),
|
||||
Decimal(x.resource_request.cpus), Decimal(x.resource_request.memory), x.machine_id]) \
|
||||
.toDF(["id", "time", "type", "rcpu", "rram", "mid"])
|
||||
|
||||
df = dfe.alias("dfe").join(dfu.alias("dfu"), [dfe.id == dfu.id, dfe.mid == dfu.mid, dfe.time > dfu.start, dfe.time <= dfu.end]) \
|
||||
.selectExpr("dfe.id as id", "dfe.mid as mid", "dfe.time as time", "dfe.type as type", "dfe.rcpu as rcpu",
|
||||
"dfe.rram as rram", "dfu.acpu as acpu", "dfu.aram as aram")
|
||||
|
||||
df.write.parquet("/home/claudio/raid0/figure-8-join-" + cluster + ".parquet")
|
||||
|
||||
# vim: set ts=4 sw=4 et tw=120:
|
|
@ -0,0 +1,195 @@
|
|||
#!/usr/bin/env python3
|
||||
# coding: utf-8
|
||||
|
||||
import os
|
||||
import json
|
||||
import pandas as pd
|
||||
import findspark
|
||||
findspark.init()
|
||||
import pyspark
|
||||
import pyspark.sql
|
||||
import sys
|
||||
import gzip
|
||||
from pyspark import AccumulatorParam
|
||||
from pyspark.sql.functions import lit
|
||||
from pyspark.sql import Window
|
||||
from pyspark.sql.types import *
|
||||
from decimal import *
|
||||
|
||||
if len(sys.argv) is not 5:
|
||||
print(sys.argv[0] + " {cluster} {tmpdir} {maxram} {joindir}")
|
||||
sys.exit()
|
||||
|
||||
joindir=sys.argv[4]
|
||||
|
||||
cluster=sys.argv[1]
|
||||
|
||||
spark = pyspark.sql.SparkSession.builder \
|
||||
.appName("task_slowdown") \
|
||||
.config("spark.driver.maxResultSize", sys.argv[3]) \
|
||||
.config("spark.local.dir", sys.argv[2]) \
|
||||
.config("spark.driver.memory", sys.argv[3]) \
|
||||
.getOrCreate()
|
||||
sc = spark.sparkContext
|
||||
|
||||
df = spark.read.parquet(joindir + "/figure-8-join-" + cluster + ".parquet")
|
||||
|
||||
### TESTING ONLY
|
||||
#df = df.limit(10000)
|
||||
|
||||
# READING MACHINE EVENTS DATA, sort them and save them as broadcast variable
|
||||
print("Starting to read machine events...")
|
||||
dfm = pd.read_csv("~/google_2019/machine_events/" + cluster + "_machine_events.csv", converters={
|
||||
'time': lambda x: -1 if x == '' else int(x),
|
||||
'machine_id': lambda x: str(x),
|
||||
'capacity.cpus': lambda x: -1 if x == '' else Decimal(x),
|
||||
'capacity.memory': lambda x: -1 if x == '' else Decimal(x)})
|
||||
print("Dropping remove events...")
|
||||
dfm = dfm[(dfm.type!=2)&(dfm.time!=-1)&(dfm["capacity.cpus"]!=-1)&(dfm["capacity.memory"]!=-1)]
|
||||
print("Dropping missing data events...")
|
||||
dfm = dfm[dfm.missing_data_reason.isnull()]
|
||||
print("Projecting on useful columns...")
|
||||
dfm = dfm[["time", "machine_id", "capacity.cpus", "capacity.memory"]]
|
||||
print("Sorting by time...")
|
||||
dfm = dfm.sort_values(by=["machine_id", "time"])
|
||||
print("Converting to broadcast variable...")
|
||||
dfm = sc.broadcast([tuple(r) for r in dfm.to_numpy()])
|
||||
print("Done with machine events.")
|
||||
|
||||
def get_machine_time_resources(machine_id):
|
||||
def aux(i, j):
|
||||
mid = (i + j) // 2
|
||||
if dfm.value[mid][1] > machine_id:
|
||||
if mid - 1 < 1 or i == j:
|
||||
return None
|
||||
return aux(i, mid - 1)
|
||||
elif dfm.value[mid][1] < machine_id:
|
||||
if mid + 1 > j or i == j:
|
||||
return None
|
||||
return aux(mid + 1, j)
|
||||
else:
|
||||
start = mid
|
||||
while dfm.value[start][1] == machine_id and start >= 0: # once found, search for oldest log for machine
|
||||
start -= 1
|
||||
start += 1
|
||||
end = mid
|
||||
while end < len(dfm.value) and dfm.value[end][1] == machine_id:
|
||||
end += 1
|
||||
return dfm.value[start:end]
|
||||
return aux(0, len(dfm.value)-1)
|
||||
|
||||
def increment_reserv_bucket(bucket, ceils, taskid, reserv, last_term_by_id):
|
||||
idx = 0
|
||||
while idx < len(ceils) and ceils[idx] < reserv:
|
||||
idx += 1
|
||||
|
||||
if taskid not in bucket:
|
||||
bucket[taskid] = [0] * (len(ceils) + 1)
|
||||
bucket[taskid][idx] += 1
|
||||
|
||||
def bucket_sum_per_termination(bucket, last_term_by_id):
|
||||
result = {-1: None, 4: None, 5: None, 6: None, 7: None, 8: None}
|
||||
for tid, vs in bucket.items():
|
||||
term = last_term_by_id[tid]
|
||||
if result[term] is None:
|
||||
result[term] = vs
|
||||
else:
|
||||
result[term] = [sum(x) for x in zip(result[term], vs)]
|
||||
return result
|
||||
|
||||
def for_each_joined(x):
|
||||
machine_id = x[0]
|
||||
ts = x[1]
|
||||
|
||||
ts = sorted(ts, key=lambda x: x.time)
|
||||
last_req_by_id = {} # map taskid -> last known req [cpu, ram] (data removed when task terminates)
|
||||
|
||||
reserv_ceils = [0, 0.2, 0.4, 0.6, 0.8, 1]
|
||||
cpu_reservs_by_id = {} # map taskid -> [n, a, b, c, d, e, f] where:
|
||||
# n: count of event with unknown machine config
|
||||
# a: count of event with res. reserv. <0.2
|
||||
# b: count of event with res. reserv. [0.2, 0.4)
|
||||
# c: count of event with res. reserv. [0.4, 0.6)
|
||||
# d: count of event with res. reserv. [0.6, 0.8)
|
||||
# e: count of event with res. reserv. [0.8, 0.1)
|
||||
# f: count of event with res. reserv. >=1
|
||||
ram_reservs_by_id = {}
|
||||
|
||||
request_ceils = [0.025, 0.05, 0.075]
|
||||
cpu_request_by_id = {} # map taskid -> [a, b, c, d] where <0.025, [0.025, 0.05), [0.05,0.075), >=0.075
|
||||
ram_request_by_id = {}
|
||||
|
||||
util_ceils = reserv_ceils
|
||||
cpu_util_by_id = {}
|
||||
ram_util_by_id = {}
|
||||
|
||||
last_term_by_id = {} # map taskid -> last termination
|
||||
machine_logs = get_machine_time_resources(machine_id)
|
||||
|
||||
for i, t in enumerate(ts):
|
||||
if machine_logs is not None and len(machine_logs) > 1 and machine_logs[1][0] >= t.time:
|
||||
machine_logs.pop(0)
|
||||
if t.id not in last_term_by_id:
|
||||
last_term_by_id[t.id] = -1
|
||||
if t.rcpu is not None and t.rram is not None:
|
||||
last_req_by_id[t.id] = (t.rcpu, t.rram)
|
||||
# 8b
|
||||
tot_req = [sum(x) for x in zip(*last_req_by_id.values())]
|
||||
if machine_logs is not None:
|
||||
reserv_cpu = tot_req[0] / machine_logs[0][2]
|
||||
reserv_ram = tot_req[1] / machine_logs[0][3]
|
||||
else:
|
||||
reserv_cpu = -1
|
||||
reserv_ram = -1
|
||||
increment_reserv_bucket(cpu_reservs_by_id, reserv_ceils, t.id, reserv_cpu, last_term_by_id)
|
||||
increment_reserv_bucket(ram_reservs_by_id, reserv_ceils, t.id, reserv_ram, last_term_by_id)
|
||||
# 8a
|
||||
increment_reserv_bucket(cpu_request_by_id, request_ceils, t.id, t.rcpu, last_term_by_id)
|
||||
increment_reserv_bucket(ram_request_by_id, request_ceils, t.id, t.rram, last_term_by_id)
|
||||
# 8c
|
||||
increment_reserv_bucket(cpu_util_by_id, util_ceils, t.id, t.acpu, last_term_by_id)
|
||||
increment_reserv_bucket(ram_util_by_id, util_ceils, t.id, t.aram, last_term_by_id)
|
||||
if t.type >= 4 and t.type <= 8:
|
||||
last_term_by_id[t.id] = t.type
|
||||
|
||||
resobj = {'rcpu': cpu_request_by_id, 'rram': ram_request_by_id, 'rscpu': cpu_reservs_by_id,
|
||||
'rsram': ram_reservs_by_id, 'ucpu': cpu_util_by_id, 'uram': ram_util_by_id}
|
||||
|
||||
for k, v in resobj.items():
|
||||
resobj[k] = bucket_sum_per_termination(v, last_term_by_id)
|
||||
|
||||
return resobj
|
||||
|
||||
def fold_resobjs(ro1, ro2):
|
||||
if ro1 is None:
|
||||
return ro2
|
||||
elif ro2 is None:
|
||||
return ro1
|
||||
else:
|
||||
for k in ro1.keys():
|
||||
for kk in ro1[k].keys():
|
||||
if ro1[k][kk] is None:
|
||||
ro1[k][kk] = ro2[k][kk]
|
||||
elif ro2[k][kk] is None:
|
||||
continue
|
||||
else:
|
||||
ro1[k][kk] = [sum(x) for x in zip(ro1[k][kk], ro2[k][kk])]
|
||||
return ro1
|
||||
|
||||
# TODO: partition by id and in the for-each-row
|
||||
# function implement lookup to dfm.value to understand its memory capacity
|
||||
|
||||
import random
|
||||
|
||||
result = df.rdd \
|
||||
.groupBy(lambda x: x.mid) \
|
||||
.partitionBy(2000, lambda x: random.randint(0, 2000-1)) \
|
||||
.map(for_each_joined) \
|
||||
.fold(None, fold_resobjs)
|
||||
|
||||
d = os.path.dirname(os.path.realpath(__file__))
|
||||
|
||||
with open(d + "/" + cluster + "_figure8.json", "w") as f:
|
||||
json.dump(result, f)
|
||||
|
||||
# vim: set ts=4 sw=4 et tw=120:
|
|
@ -0,0 +1,275 @@
|
|||
#!/usr/bin/env python3
|
||||
# coding: utf-8
|
||||
|
||||
import os
|
||||
import json
|
||||
import pandas as pd
|
||||
import findspark
|
||||
findspark.init()
|
||||
import pyspark
|
||||
import pyspark.sql
|
||||
import sys
|
||||
import gzip
|
||||
from pyspark import AccumulatorParam
|
||||
from pyspark.sql.functions import lit
|
||||
from pyspark.sql import Window
|
||||
from pyspark.sql.types import *
|
||||
from decimal import *
|
||||
|
||||
TESTDATA = True
|
||||
|
||||
if len(sys.argv) is not 4:
|
||||
print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
|
||||
sys.exit()
|
||||
|
||||
cluster=sys.argv[1]
|
||||
|
||||
spark = pyspark.sql.SparkSession.builder \
|
||||
.appName("task_slowdown") \
|
||||
.config("spark.driver.maxResultSize", "128g") \
|
||||
.config("spark.local.dir", sys.argv[2]) \
|
||||
.config("spark.driver.memory", sys.argv[3]) \
|
||||
.getOrCreate()
|
||||
sc = spark.sparkContext
|
||||
|
||||
#
|
||||
# READING INSTANCE EVENTS DATA
|
||||
#
|
||||
dfepath = "/home/claudio/google_2019/instance_events/" + cluster
|
||||
dfepath += "/" + cluster + ("_instance_events00000000000?.json.gz" if TESTDATA else "_instance_events*.json.gz")
|
||||
dfe = spark.read.json(dfepath)
|
||||
try:
|
||||
dfe["collection_type"] = dfe["collection_type"].cast(ByteType())
|
||||
except:
|
||||
dfe = dfe.withColumn("collection_type", lit(None).cast(ByteType()))
|
||||
|
||||
#
|
||||
# READING INSTANCE USAGE DATA
|
||||
#
|
||||
dfupath = "/home/claudio/google_2019/instance_usage/" + cluster
|
||||
dfupath += "/" + cluster + ("_instance_usage00000000000?.csv.gz" if TESTDATA else "_instance_usage*.csv.gz")
|
||||
usage_schema = StructType() \
|
||||
.add("start_time", LongType(), True) \
|
||||
.add("end_time", LongType(), True) \
|
||||
.add("collection_id", StringType(), True) \
|
||||
.add("instance_index", StringType(), True) \
|
||||
.add("machine_id", StringType(), True) \
|
||||
.add("alloc_collection_id", LongType(), True) \
|
||||
.add("alloc_instance_index", StringType(), True) \
|
||||
.add("collection_type", ByteType(), True) \
|
||||
.add("average_usage_cpus", DoubleType(), True) \
|
||||
.add("average_usage_memory", DoubleType(), True) \
|
||||
.add("maximum_usage_cpus", DoubleType(), True) \
|
||||
.add("maximum_usage_memory", DoubleType(), True) \
|
||||
.add("random_sample_usage_cpus", DoubleType(), True) \
|
||||
.add("random_sample_usage_memory", DoubleType(), True) \
|
||||
.add("assigned_memory", DoubleType(), True) \
|
||||
.add("page_cache_memory", DoubleType(), True) \
|
||||
.add("cycles_per_instruction", DoubleType(), True) \
|
||||
.add("memory_accLesses_per_instruction", DoubleType(), True) \
|
||||
.add("sample_rate", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_00", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_10", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_20", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_30", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_40", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_50", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_60", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_70", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_80", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_90", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_91", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_92", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_93", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_94", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_95", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_96", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_97", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_98", DoubleType(), True) \
|
||||
.add("cpu_usage_dist_99", DoubleType(), True)
|
||||
|
||||
dfu = spark.read.format("csv") \
|
||||
.option("header", False) \
|
||||
.schema(usage_schema) \
|
||||
.load(dfupath)
|
||||
|
||||
# READING MACHINE EVENTS DATA, sort them and save them as broadcast variable
|
||||
print("Starting to read machine events...")
|
||||
dfm = pd.read_csv("~/google_2019/machine_events/" + cluster + "_machine_events.csv")
|
||||
print("Dropping remove events...")
|
||||
dfm = dfm[(dfm.type==1)|(dfm.type==3)]
|
||||
print("Dropping missing data events...")
|
||||
dfm = dfm[dfm.missing_data_reason.notnull()]
|
||||
print("Projecting on useful columns...")
|
||||
dfm = dfm[["time", "machine_id", "capacity.cpus", "capacity.memory"]]
|
||||
print("Sorting by time...")
|
||||
dfm = dfm.sort_values(by=["machine_id", "time"])
|
||||
print("Converting to broadcast variable...")
|
||||
dfm = sc.broadcast([tuple(r) for r in dfm.to_numpy()])
|
||||
print("Done with machine events.")
|
||||
|
||||
def tabid(x):
|
||||
return Decimal(x.collection_id) + Decimal(x.instance_index) / Decimal(2**64)
|
||||
|
||||
# interpolate machine data by extending each last machine report before a gap to cover it
|
||||
def clean_usage(x):
|
||||
return [tabid(x), Decimal(x.average_usage_cpus), Decimal(x.average_usage_memory), int(x.start_time),
|
||||
int(x.end_time), x.machine_id]
|
||||
|
||||
def interpolate_usage(ts):
|
||||
ts = sorted(ts, key=lambda x: x[3])
|
||||
l = len(ts)
|
||||
for i in range(1, l-1):
|
||||
if ts[i+1][3] > ts[i][4]:
|
||||
ts[i][4] = ts[i+1][3]
|
||||
return ts
|
||||
|
||||
dfu = dfu.rdd \
|
||||
.filter(lambda x: x.start_time is not None and x.end_time is not None and
|
||||
x.instance_index is not None and x.collection_id is not None and
|
||||
x.machine_id is not None) \
|
||||
.map(clean_usage).groupBy(lambda x: x[0]) \
|
||||
.flatMap(lambda x: interpolate_usage(x[1])).toDF(["id", "acpu", "aram", "start", "end", "mid"])
|
||||
|
||||
dfe = dfe.rdd \
|
||||
.filter(lambda x: x.time is not None and x.type is not None and x.machine_id is not None and
|
||||
x.instance_index is not None and x.collection_id is not None and x.resource_request is not None and
|
||||
x.resource_request.cpus is not None and x.resource_request.memory is not None) \
|
||||
.map(lambda x: [tabid(x), int(x.time), int(x.type),
|
||||
Decimal(x.resource_request.cpus), Decimal(x.resource_request.memory), x.machine_id]) \
|
||||
.toDF(["id", "time", "type", "rcpu", "rram", "mid"])
|
||||
|
||||
df = dfe.join(dfu, [dfe.id == dfu.id, dfe.mid == dfu.mid, dfe.time >= dfu.start, dfe.time < dfu.end])
|
||||
|
||||
def get_machine_time_resources(machine_id):
|
||||
def aux(i, j):
|
||||
mid = (i + j) // 2
|
||||
print(i, j, mid)
|
||||
print(dfm.value[mid])
|
||||
if dfm.value[mid][1] > machine_id:
|
||||
return aux(i, mid - 1)
|
||||
elif dfm.value[mid][1] < machine_id:
|
||||
return aux(mid + 1, j)
|
||||
else:
|
||||
start = mid
|
||||
while dfm.value[start][1] == machine_id and start >= 0: # once found, search for oldest log for machine
|
||||
start -= 1
|
||||
start += 1
|
||||
end = mid
|
||||
while dfm.value[end][1] == machine_id and end < len(dfm.value):
|
||||
end += 1
|
||||
return dfm.value[start:end]
|
||||
return aux(0, len(dfm.value)-1)
|
||||
|
||||
def increment_reserv_bucket(bucket, ceils, taskid, reserv):
|
||||
idx = 0
|
||||
while ceils[idx] < reserv and idx < len(ceils):
|
||||
idx += 1
|
||||
|
||||
if taskid not in bucket:
|
||||
bucket[taskid] = [0] * (len(ceils) + 1)
|
||||
bucket[taskid][idx] += 1
|
||||
|
||||
def bucket_sum_per_termination(bucket, last_term_by_id):
|
||||
result = {-1: None, 4: None, 5: None, 6: None, 7: None, 8: None}
|
||||
for tid, vs in bucket.items():
|
||||
term = last_term_by_id[tid]
|
||||
if result[term] is None:
|
||||
result[term] = vs
|
||||
else:
|
||||
result[term] = [sum(x) for x in zip(result[term], vs)]
|
||||
return result
|
||||
|
||||
def for_each_joined(x):
|
||||
machine_id = x[0]
|
||||
ts = x[1]
|
||||
|
||||
ts = sorted(ts, key=lambda x: x.time)
|
||||
last_req_by_id = {} # map taskid -> last known req [cpu, ram] (data removed when task terminates)
|
||||
|
||||
reserv_ceils = [0.2, 0.4, 0.6, 0.8, 1]
|
||||
cpu_reservs_by_id = {} # map taskid -> [a, b, c, d, e, f] where:
|
||||
# a: count of event with res. reserv. <0.2
|
||||
# b: count of event with res. reserv. [0.2, 0.4)
|
||||
# c: count of event with res. reserv. [0.4, 0.6)
|
||||
# d: count of event with res. reserv. [0.6, 0.8)
|
||||
# e: count of event with res. reserv. [0.8, 0.1)
|
||||
# f: count of event with res. reserv. >=1
|
||||
ram_reservs_by_id = {}
|
||||
|
||||
request_ceils = [0.025, 0.05, 0.075]
|
||||
cpu_request_by_id = {} # map taskid -> [a, b, c, d] where <0.025, [0.025, 0.05), [0.05,0.075), >=0.075
|
||||
ram_request_by_id = {}
|
||||
|
||||
util_ceils = reserv_ceils
|
||||
cpu_util_by_id = {}
|
||||
ram_util_by_id = {}
|
||||
|
||||
last_term_by_id = {} # map taskid -> last termination
|
||||
machine_logs = get_machine_time_resources(machine_id)
|
||||
|
||||
for i, t in enumerate(ts):
|
||||
if len(machine_logs) > 1 and machine_logs[1][0] >= t.time:
|
||||
machine_logs.pop(0)
|
||||
if t.id not in last_term_by_id:
|
||||
last_term_by_id[t.id] = -1
|
||||
if t.type >= 4 and t.type <= 8:
|
||||
last_term_by_id[t.id] = t.type
|
||||
del last_req_by_id[t.id]
|
||||
else:
|
||||
if t.rcpu is not None and t.rram is not None:
|
||||
last_req_by_id[t.id] = (t.rcpu, t.rram)
|
||||
|
||||
# 8b
|
||||
tot_req = [sum(x) for x in zip(*last_req_by_id.values())]
|
||||
reserv_cpu = tot_req[0] / machine_logs[0][2]
|
||||
reserv_ram = tot_req[1] / machine_logs[0][3]
|
||||
increment_reserv_bucket(cpu_reservs_by_id, reserv_ceils, t.id, reserv_cpu)
|
||||
increment_reserv_bucket(ram_reservs_by_id, reserv_ceils, t.id, reserv_ram)
|
||||
|
||||
# 8a
|
||||
increment_reserv_bucket(cpu_request_by_id, request_ceils, t.id, t.rcpu)
|
||||
increment_reserv_bucket(ram_request_by_id, request_ceils, t.id, t.rram)
|
||||
|
||||
# 8c
|
||||
increment_reserv_bucket(cpu_util_by_id, util_ceils, t.id, t.acpu)
|
||||
increment_reserv_bucket(ram_util_by_id, util_ceils, t.id, t.aram)
|
||||
|
||||
resobj = {'rcpu': cpu_request_by_id, 'rram': ram_request_by_id, 'rscpu': cpu_reservs_by_id,
|
||||
'rsram': ram_reservs_by_id, 'ucpu': cpu_util_by_id, 'uram': ram_util_by_id}
|
||||
|
||||
for k, v in resobj.items():
|
||||
resobj[k] = bucket_sum_per_termination(v, last_term_by_id)
|
||||
|
||||
return resobj
|
||||
|
||||
def fold_resobjs(ro1, ro2):
|
||||
if ro1 is None:
|
||||
return ro2
|
||||
elif ro2 is None:
|
||||
return ro1
|
||||
else:
|
||||
for k in ro1.keys():
|
||||
for kk in ro1[k].keys():
|
||||
if ro1[k][kk] is None:
|
||||
ro1[k][kk] = ro2[k][kk]
|
||||
elif ro2[k][kk] is None:
|
||||
continue
|
||||
else:
|
||||
ro1[k][kk] = [sum(x) for x in zip(ro1[k][kk], ro2[k][kk])]
|
||||
return ro1
|
||||
|
||||
# TODO: partition by id and in the for-each-row
|
||||
# function implement lookup to dfm.value to understand its memory capacity
|
||||
|
||||
result = df.rdd \
|
||||
.groupBy(lambda x: x.mid) \
|
||||
.map(for_each_joined) \
|
||||
.fold(None, fold_resobjs)
|
||||
|
||||
d = os.path.dirname(os.path.realpath(__file__))
|
||||
|
||||
with open(d + "/" + cluster + "_figure8.json", "w") as f:
|
||||
json.dump(result, f)
|
||||
|
||||
# vim: set ts=4 sw=4 et tw=120:
|
Binary file not shown.
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|
@ -0,0 +1 @@
|
|||
lost c_res_actual_per_job parts 01816-01824
|
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|
@ -0,0 +1,6 @@
|
|||
-1,73165704815830.230712890625000000,73345651566476.821899414062500000
|
||||
6,146534454172963.142395019531250000,77692052834547.042846679687500000
|
||||
5,239202281025317.192077636718750000,199303134846755.981445312500000000
|
||||
4,1410420680006233.215332031250000000,829767989678030.014038085937500000
|
||||
8,957825860821.723937988281250000,635555632653.236389160156250000
|
||||
7,8624573251690182.685852050781250000,5840241764609245.300292968750000000
|
|
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Loading…
Reference in New Issue