intelligent distributed systems optimizations

MANCHESTER LONDON NEW YORK

Martin Zapletal @zapletal_martin#ReactiveSummit

Intelligent Distributed SystemOptimizations

@cakesolutions

https://twitter.com/zapletal_martin

val in: Array[Int]

var i = 0var result = 0while (i <= in.size) { result += in(i) i += 1}

Motivation

val in: Array[Int]

in.foldLeft(0)(_ + _)

Distributed System Optimizations

● Performance, cost, reliability, uptime● Logical, physical, intelligence, .... Optimized

single machine

Optimized single

machine

Optimized single

machine

Optimized distributed

system

Logical Optimizations

● Databases● Spark● Akka Streams● SystemML● TensorFlow● Free Monad

[6]

val users = spark.read.json("...").as[User]val posts = spark.read.json("...").as[Post]

users .where(not(not($"active"))) .join(posts, posts("author") === users("id")) .select($"userName", initcap($"title")) .filter($"userName" !== "Mike") .filter(_ => 1 == 1) .groupBy($"userName") .agg(count("*")) .select($"userName", $"count(1)")

Author, text, title

Active, id, age, name, userName

NOT(NOT(Active))

userName, initcap(title)

Username != Mike

userName, count

posts

users

filter

join

project

filter

aggregate

project

Graph Optimizations

Graph Optimizations

posts users

Author, text, title Active, id, age, name, userName

filterActive && Username != Mike

join

project userName

aggregate

project userName, count

posts

usersAuthor, text, title


filter NOT(NOT(Active))

join

project userName, initcap(title)

filter Username != Mike

aggregate


projectprojectAuthor, title Id, userName

Id == author

Id == author

Graph Optimizations

posts users

Author, text, title Active, id, age, name, userName

filterActive && Username != Mike

join

userName

aggregate


posts

usersAuthor, text, title


filter NOT(NOT(Active))

join

project userName, initcap(title)

filter Username != Mike

aggregate


projectprojectAuthor, title Id, userName

Id == author

Id == author

project

Physical Optimizations

posts users

filter

5 billion records, 500 GB

join

projectproject

100 million records 20 GB

t1.value=200 1 million records, 100 MB

Id == author

[3, 8]

Whole Stage Code Generationprivate void agg_doAggregateWithKeys() throws java.io.IOException { agg_hashMap = agg_plan.createHashMap();

while (inputadapter_input.hasNext() && !stopEarly()) { InternalRow inputadapter_row = (InternalRow) inputadapter_input.next(); boolean inputadapter_isNull = inputadapter_row.isNullAt(0); UTF8String inputadapter_value = inputadapter_isNull ? null : (inputadapter_row.getUTF8String(0)); long inputadapter_value1 = inputadapter_row.getLong(1);

UnsafeRow agg_unsafeRowAggBuffer = null; UnsafeRow agg_fastAggBuffer = null;

if (agg_fastAggBuffer == null) { agg_holder.reset();

agg_rowWriter.zeroOutNullBytes();

if (inputadapter_isNull) { agg_rowWriter.setNullAt(0); } else { agg_rowWriter.write(0, inputadapter_value); }

[0, 1, 2, 4, 5, 7]

Manual Optimizations● Coordination avoidance● Single writer principle● Load balancing● Speculative Execution● Colocation● Decoupling● Caching● Publish Subscribe● Batching● Rate Limiting● Low Level Optimizations (memory, object allocation, GC, CPU, IO, parallelization, …)● ...

Machine learning

● Stock market predictions● Product recommendations● Facial recognition● Object recognition● Speech understanding● Self-driving cars● Distributed system optimizations● ...

Observability

● Observability○ Logs○ Metrics○ Traces

● Analytics● Understanding● Actions● Reactive

[9]

Metrics and Monitoring

private[this] val latency = Histogram .build() .name(s"requests_latency_seconds") .help("Request latency in seconds.") .register(registry)

val timer = latency.startTimer()

…

timer.observeDuration()

[9]

Metrics and Monitoring

● Time series database● Aggregate queries

Actor Processing Time (max)

200

150

100

50

0

600

400

200

0

800

600

400

200

0

Running Actors

Processed Messages (1 Minute Rate)

17:30 17:45 18:00 18:15

17:30 17:45 18:00 18:15

17:30 17:45 18:00 18:15

6

4

2

0

Actor mailbox Time (max)

17:30 17:45 18:00 18:15

[10, 11]

Logs and Context

● Automated context

log.logInfo( s"""{ | "traceId"="$traceId", | "userId"="$userId", | "scope"="$scope", | "service"="$service", | "userData"=${userData.asset} | "message"="Don't log like this! Automate context!" |""".stripMargin)

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"eve

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"someAttri

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"someOtherAttri

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"dateTimePublish

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"eventTyp

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"company":

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"assetDescr

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some asse

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"updateId": "0",

"userId

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"userD

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"versi

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"data": {

"updateAsse

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{ "datetime": "2017-09-20T15:01:28.834Z",

"level": "info",

"scope": "net.cakesolutions.microservice1.dispatcher.AsyncRequestDispatcher",

"traceId": "5760302508966145110",

"service": "microservice1",

"userId": "067e6162-3b6f-4ae2-a171-2470b63dff00",

"assetId": "54947df8-0e9e-4471-a2f9-9af509fb5889",

"serviceUrl": "http://cake.qa.us-east-1.solutions.net/microservice2/067e6162-3b6f-4ae2-a171-2470b63dff00"

}{ "datetime": "2017-09-20T15:01:28.920Z",

"level": "info",

"scope": "net.cakesolutions.microservice2.dispatcher.DispatchRequestHandler",

"traceId": "5760302508966145110",




"eventPublished": {

"someAttribute1": "2",

"someOtherAttribute1": "None",

"dateTimePublished": "2017-09-20T15:01:28.917Z",

"eventType": "updateAsset",

"company": "cakeSolutions",

"assetDescription": "some asset description",

"updateId": "0",

"userId": "54947df8-0e9e-4471-a2f9-9af509fb5889",

"userDetails": "no way to notice the userId here actually is incorrect",

"version": "v1",

"data": {

"updateAssetData":

"http://www.longurlmaker.com/go?id=7s80lengthened87EzURL11aDecentURLsd136ShoterLinkdeepSnipURLcfar%2Breac

hingt108cNutshellURLgbspread%2Bout13EasyURLenduringtall0xURl.ie18418stretchedbastretchingxm62d1faraway0URl.ie

ny0expandedNe1longish419Smallr39far%2Bofflofty1EzURL151Shim1aURLPieaShredURLURL.co.ukNanoRefenduringxlof

tynfar%2BreachingtallMooURL57i09stretchedk014longish090rangyn9c5h5YATUCremoteb1Doiop00dgreat2a01spun%2Bo

ut50301URL2aspread%2Bout01blnkf51gangling2101k9far%2Boff0a0910d0Shortlinksfaraway78Redirxdrawn%2Bout3Doio

p" }

}} "datetime": "2017-09-20T15:01:28.834Z",

"level": "info",


"traceId": "5760302508966145110",





}{ "datetime": "2017-09-20T15:01:28.834Z",

"level": "info",


"traceId": "5760302508966145110",




"serviceUrl": "http://cake.

{ "tim

estamp": "2017-09-20T15:01:28.931Z",

"logLevel": "warn",

"scope": "net.cakesolutions.microservice1.asset.EventParser.recoverFromMissingData",

"traceId": "5760302508966145110",




"missingDataFailure": {

"MissingData": {

"failure": {

"property": "referrer",

"optional": "tru

e"

}

}

}} "datetime": "2017-09-20T15:01:28.834Z",

"level": "info",


"traceId": "5760302508966145110",





}{ "datetime": "2017-09-20T15:01:28.834Z",

"level": "info",


"traceId": "5760302508966145110",




"serviceUrl": "http://cake.

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,

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: "20

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set d

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"up

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,

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user

Det

ails

": "n

o w

ay to

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the

user

Id h

ere

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v1",

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{

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}

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"dat

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",

"lev

el":

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",

"sco

pe":

"net

.cak

esol

utio

ns.m

icro

serv

ice1

.ass

et.E

vent

Pro

cess

or",

"t

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0302

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6614

5110

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icro

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2-3b

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8-0e

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"a

sset

Det

ails

": "(

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(Ass

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ier(

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))"

} { "d

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akes

olut

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rose

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sset

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ntP

arse

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471-

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9",

"a

sset

Det

ails

": "(

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"} {

"tim

esta

mp"

: "20

17-0

9-20

T15:

01:2

8.93

1Z",

"l

ogLe

vel":

"war

n",

"s

cope

": "n

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"t

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Id":

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"ser

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": "m

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"use

rId":

"067

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6f-4

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a171

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"a

sset

Id":

"549

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"m

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re":

{

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re":

{

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}

}

}}

{ "datetime": "2017-09-20T15:01:28.834Z",

"level": "info", "scope": "net.cakesolutions.microservice1.dispatcher.AsyncRequestDispatcher",

"traceId": "5760302508966145110",





}{

"datetime": "2017-09-20T15:01:28.920Z",

"level": "info", "scope": "net.cakesolutions.microservice2.dispatcher.DispatchRequestHandler",

"traceId": "5760302508966145110",




"eventPublished": { "someAttribute1": "2",






"updateId": "0", "userId": "54947df8-0e9e-4471-a2f9-9af509fb5889",


"version": "v1", "data": { "updateAssetData":






p" } }

}{

"dataTime": "2017-09-20T15:01:28.925Z",

"level": "info", "scope": "net.cakesolutions.microservice1.asset.EventProcessor",

"traceId": "5760302508966145110",




"assetDetails": "(List(AssetIdentifier(Provider,12341567433.1122-2344,11220303,0128479),


AssetIdentifier(Provider,09320418u7.1092384,p91234,0182374))),(List(AssetIdentifier(Provider,10293890.10000,12-83,10

7237977799)),AssetUrl(http://cake.qa.solutions.us-east-1.net/v4/assets/describe/ad8ecd76-736e-4816-8c59-484ba1d229a

4/descriptior,AmsId(ad8ecd76-736e-4816-8c59-484ba1d229a4),0,0,0)),(List(AssetIdentifier(Provider,1948756.1403509740

38424,2754208,1948756)),DescriptorFile(Format,http://example.org/some/Descriptor/file.mp4,0,0,0)),(List(AssetIdentifier(P

rovider,1948720.14035097403837,2754264,1948720)),DescriptorFile(Format,http://example.org/some/other/Descriptor/file/

video.mp4,0,0,0))"}

{ "dateTime": "2017-09-20T15:01:28.925Z",

"logLevel": "info", "scope": "net.cakesolutions.microservice1.asset.EventParser",

"traceId": "9044230965916828392",




"assetDetails": "(List(AssetIdentifier(Provider,19460718.140350974037952,2762537,19460718),


AssetIdentifier(Provider,19460718.140350974037952,2762537,19460718)),null),(List(AssetIdentifier(Provider,19487588.14

0350974038400,2754237,19487588)),AssetUrl(http://cake.qa.solutions.us-east-1.net/v4/assets/describe/ad8ecd76-736e-4

816-8c59-484ba1d229a4/descriptior,AmsId(ad8ecd76-736e-4816-8c59-484ba1d229a4),0,0,0)),(List(AssetIdentifier(Provide

r,1948756.140350974038424,2754208,1948756)),DescriptorFile(Format,http://example.org/some/Descriptor/file.mp4,0,0,0)

),(List(AssetIdentifier(Provider,1948720.14035097403837,2754264,1948720)),DescriptorFile(Format,http://example.org/so

me/other/Descriptor/file/video.mp4,0,0,0))"

}{

"timestamp": "2017-09-20T15:01:28.931Z",

"logLevel": "warn", "scope": "net.cakesolutions.microservice1.asset.EventParser.recoverFromMissingData",

"traceId": "5760302508966145110",





"MissingData": { "failure": {


"optional": "true"

} } }}

{ "datetime": "2017-09-20T15:01:28.834Z", "level": "info", "scope": "net.cakesolutions.microservice1.dispatcher.AsyncRequestDispatcher", "traceId": "5760302508966145110", "service": "microservice1", "userId": "067e6162-3b6f-4ae2-a171-2470b63dff00", "assetId": "54947df8-0e9e-4471-a2f9-9af509fb5889", "serviceUrl": "http://cake.qa.us-east-1.solutions.net/microservice2/067e6162-3b6f-4ae2-a171-2470b63dff00"

}

{ "timestamp": "2017-09-20T15:01:28.931Z", "logLevel": "warn", "scope": "net.cakesolutions.microservice1.asset.EventParser.recoverFromMissingData", "traceId": "5760302508966145110", "service": "microservice1", "userId": "067e6162-3b6f-4ae2-a171-2470b63dff00", "assetId": "54947df8-0e9e-4471-a2f9-9af509fb5889", "missingDataFailure": { "MissingData": { "failure": { "property": "referrer", "optional": "true" } } }}

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}

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"version": "v1", "data": { "updateAssetData": "http://www.longurlmaker.com/go?id=7s80lengthened87EzURL11aDecentURLsd136ShoterLinkdeepSnipURLcfar%2Breac





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Traces

[12]

clientservice1

service1service1service1

service1service1service1

service2

service2

service2

service2

service2

service2service2

service2

service2

service2

service3

181.126ms : client-calls-service1180.527ms : service1-internal-method1

605µs : service1-internal-method2

54.152ms : service1-internal-method3


46µs : service1-internal-method540.910ms : service1-internal-method6





11.492ms : service2-internal-method4105µs : service2-internal-method5

11.494ms : service2-internal-method610.511ms : service2-internal-method7


29.816ms : service2-internal-method912.153ms : service2-internal-method10


client x4 service1 x10 service3 x2 service2 x11unknown-service x2

Economies of scale

[13, 14]

Economies of scale

● Find the perfect balance● Align the risk taken by a service with the risk the business is willing to

bear● Explicit decisions

[15]

Node Assignment

Node Assignment

Matrix MultiplyNetwork Bound

QR FactorizationMem Bandwidth Bound

[16]

Node Assignment

128 192 256 320 384 448 512 576 640 704 768 832 896 960 1024 1088 1152

Function 1

Function 2

Function 3

Function 4

Function 5

Function 6

...

Serverless

Automated ScalingRate of game events

Number of workers

40,000/s

30,000/s

20,000/S

10,000/s

30

20

10

0

[17, 18]

Automated ScalingRate of game events

Number of workers

40,000/s

30,000/s

20,000/S

10,000/s

30

20

10

0

Configurationval data = spark.read.format("libsvm") .load("sample_multiclass_classification_data.txt")

val splits = data.randomSplit(Array(0.6, 0.4), seed = 1234L)val train = splits(0)val test = splits(1)

val layers = Array[Int](4, 5, 4, 3)

val trainer = new MultilayerPerceptronClassifier() .setLayers(layers) .setBlockSize(128) .setSeed(1234L) .setMaxIter(100)

val model = trainer.fit(train)

val result = model.transform(test)val predictionAndLabels = result.select("prediction", "label")val evaluator = new MulticlassClassificationEvaluator() .setMetricName("accuracy")

evaluator.evaluate(predictionAndLabels)

Configurationval spark = SparkSession .builder() .config("spark.reducer.maxSizeInFlight", maxSizeInFlight) .config("spark.reducer.maxReqsInFlight", maxReqsInflight) .config("spark.shuffle.file.buffer", shuffleFileBuffer) .config("spark.memory.fraction", sparkMemoryFraction) .config("spark.shuffle.service.index.cache.entries", shuffleServiceIndexCacheEntries) .config("spark.memory.storageFraction", sparkMemoryStorageFraction) .config("spark.shuffle.memoryFraction", sparkShuffleMemoryFraction) .config("spark.storage.memoryFraction", sparkStorageMemoryFraction) .config("spark.storage.unrollFraction", sparkStorageUnrollFraction) .config("spark.broadcast.blockSize", sparkBroadcastBlockSize) .config("spark.executor.cores", sparkExecutorCores) .config("spark.default.parallelism", defaultParallelism) .config("spark.files.maxPartitionBytes", sparkFilesMaxPartitionBytes) .config("spark.files.openCostInBytes", sparkFilesOpenCostInByes) .config("spark.rpc.message.maxSize", sparkRpcMessageMaxSize) .config("spark.storage.memoryMapThreshold", sparkSorageMemoryMapThreshold) .config("spark.cores.max", sparkCoresMax) .config("spark.speculation", "true") .config("spark.speculation.interval", sparkSpeculationInterval) .config("spark.speculation.multiplier", sparkSpeculationMultiplier) .config("spark.speculation.quantile", sparkSpeculationQuantile) .config("spark.task.cpus", sparkTaskCpus)

Random trials

● Labelled training examples (feature vector, label)● Classification, regression, clustering, ...● Find an algorithm that for given feature vector finds the correct label● Optimization of an objective function with respect to model parameters

Features Label

100mb,10,100kb,0.75,0.1,0.001,0.75,0.75,0.1,1g,2,8,10000,1000000,100,1g,1ms,1.5,0.01,1g

61.9799

1kb,2147483647,10mb,0.75,0.001,0.1,0.001,0.5,0.001,10mb,8,2,10000,1000000,100,1kb,1ms,1.1,0.75,512mb

12.2511

100mb,10,1mb,0.75,0.5,0.001,0.1,0.001,0.5,100mb,4,1,100000000,1000,1000,10mb,100ms,1.5,0.01,8g

20.7461

... ...

Supervised Learning

Supervised Learning

Supervised Learningdef deep_nn(x, hidden1_units, hidden2_units): with tf.name_scope('hidden1'): weights1 = tf.Variable(tf.truncated_normal([20, hidden1_units], stddev=1.0 / math.sqrt(float(20))), name='weights') biases1 = tf.Variable(tf.zeros([hidden1_units]), name='biases') hidden1 = tf.nn.relu(tf.matmul(x, weights1) + biases1)

with tf.name_scope('hidden2'): weights2 = tf.Variable(tf.truncated_normal([hidden1_units, hidden2_units], stddev=1.0 / math.sqrt(float(hidden1_units))), name='weights') biases2 = tf.Variable(tf.zeros([hidden2_units]), name='biases') hidden2 = tf.nn.relu(tf.matmul(hidden1, weights2) + biases2)

with tf.name_scope('linear'): weights3 = tf.Variable(tf.truncated_normal([hidden2_units, 1], stddev=1.0 / math.sqrt(float(hidden2_units))), name='weights') biases3 = tf.Variable(tf.zeros([1]), name='biases') logits = tf.matmul(hidden2, weights3) + biases3 return logits

Supervised Learning example_batch, label_batch = read_example_batch(filename=FLAGS.data_file, batch_size=batch_size) x = tf.placeholder(tf.float32, [None, 20]) y = deepnn(x, hidden1_units, hidden2_units) y_ = tf.placeholder(tf.float32, [None, 1]) mse = tf.reduce_sum(tf.pow(y-y_, 2))/(2*batch_size) train_step = tf.train.GradientDescentOptimizer(learning_rate=0.25).minimize(mse)

sess = tf.Session() with sess.as_default(): sess.run(tf.global_variables_initializer()) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord)

for i in range(1000): examples, labels = sess.run([example_batch, label_batch]) sess.run(train_step, feed_dict={x: examples, y_: labels})

Supervised Learning

Problem solved

PROBLEM SOLVED!

Data● What about training data?● Random runs● In-line optimization● Synthetic data● Generative models● Unsupervised learning

General case

Akka-httpKafka

Microservice CockroachDB

Dynamo

Redis Cache

Akka Cluster

Load Balancer

Lambda

General case● Complex● Long running● Large number of variables (many unknown)● Many possible actions● Temporal reward

Reinforcement Learning● Trial and error (positive reinforcement)● Set of states S● Set of actions A● Reward r

● P(s’ | a, s)● s0, a0, r1 -> s1, a1, r2 -> …● Q(s, a) = r + γ * maxa’(Q(s’, a’))

[19. 20]

Environment

Agent

Interpreter

Reward

State Action

Deep Reinforcement Learning● Complex hierarchical actions and planning● Temporal reward

[21]

Deep Reinforcement Learning - Gym

RewardNumber of errors, Cost

Concurrent user load 10,000 12,000 20,000 ...

Instances and sizes 1,1,2 ? ? ?

Action

Nothing

Inc inst size

Dec inst size

Add inst

Remove inst

State

Deep Reinforcement Learning - Gymdef _cost(self, dist_sys, load): cost = 0 can_handle_load = 0 for i in range(len(dist_sys)): if dist_sys[i] == INST_SIZE_0: cost += INST_SIZE_0_COST can_handle_load += INST_SIZE_0_LOAD elif dist_sys[i] == INST_SIZE_1: cost += INST_SIZE_1_COST can_handle_load += INST_SIZE_1_LOAD elif dist_sys[i] == INST_SIZE_2: cost += INST_SIZE_2_COST can_handle_load += INST_SIZE_2_LOAD errors = min((abs(min(can_handle_load - load, 0)) *

ERROR_COEFFICIENT + max(cost, 0)), MAX_COST)

Deep Reinforcement Learning - Gymdef _step(self, action): if (action == ADD_NODE): ... elif (action == INCREASE_INSTANCE_SIZE): ... elif (action == REMOVE_NODE): ... elif (action == DECREASE_INSTANCE_SIZE): ... else: self.dist_sys = self.dist_sys cost = self._cost(self.dist_sys, self.current_load[self.progress]) reward = -1 * ((2 * (cost / MAX_COST)) - 1) self.progress += 1 done = self.progress >= len(self.current_load) - 1 return self.current_load[self.progress], reward, done

Deep Reinforcement Learning - Agentclass agent(): def __init__(self, s_size,a_size): self.inputs = tf.placeholder(shape=[None, s_size],dtype=tf.float32) W = tf.Variable(tf.random_uniform([s_size,a_size],0,0.01)) self.Q_values = tf.matmul(self.inputs,W)

self.largest_Q_value_index = tf.argmax(self.Q_values,1) self.largest_Q_value = tf.reduce_max(self.Q_values)

self.next_Q_values = tf.placeholder(shape=[1,a_size],dtype=tf.float32) loss = tf.reduce_sum(tf.square(self.next_Q_values - self.Q_values)) trainer = tf.train.GradientDescentOptimizer(learning_rate=0.1) self.updateModel = trainer.minimize(loss)

[22, 23]

Deep Reinforcement Learning - Agentwith tf.Session() as sess: sess.run(init) for i in range(num_episodes): state = env.reset() d = False j = 0 while j < env.episode_length() + 1: j+=1 fixed_in = fixed_length_history(episode_history[:,0]) chosen_action, all_Q_values = sess.run( [myAgent.largest_Q_value_index,myAgent.Q_values], feed_dict={myAgent.inputs:fixed_in}) if np.random.rand(1) < random_action_probability: chosen_action[0] = env.action_space.sample()

[22, 23]

Deep Reinforcement Learning - Agent next_state,r,d,_ = env.step(chosen_action[0])

episode_history.append([state, chosen_action, r, next_state]) next_largest_Q_value = sess.run( myAgent.largest_Q_value, feed_dict={myAgent.inputs:next_fixed_in}) all_Q_values[0,a[0]] = r + gamma * next_largest_Q_value _ = sess.run( myAgent.updateModel, feed_dict={myAgent.inputs:fixed_in,myAgent.next_Q_values:all_Q_values}) state = next_state

[22, 23]

Learn

[24]

Learn

[25]

Conclusion

● Optimize your code and architecture● Measure, gather data● Use the data!● Continuously improve and evolve

Questions


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@zapletal_martin @cakesolutions


[email protected]

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References[0] https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-sql-whole-stage-codegen.html[1] https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/6122906529858466/293651311471490/5382278320999420/latest.html[2] http://hydronitrogen.com/in-the-code-spark-sql-query-planning-and-execution.html[3] https://databricks.com/blog/2016/05/23/apache-spark-as-a-compiler-joining-a-billion-rows-per-second-on-a-laptop.html[4] http://www.vldb.org/pvldb/vol4/p539-neumann.pdf[5] https://blog.acolyer.org/2016/05/23/efficiently-compiling-efficient-query-plans-for-modern-hardware/[6] https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-rdd-lineage.html[7] https://databricks.com/blog/2017/02/16/processing-trillion-rows-per-second-single-machine-can-nested-loop-joins-fast.html[8] https://www.slideshare.net/SparkSummit/costbased-optimizer-framework-for-spark-sql-spark-summit-east-talk-by-ron-hu-and-zhenhua-wang[9] https://medium.com/@copyconstruct/monitoring-in-the-time-of-cloud-native-c87c7a5bfa3e[10] https://www.lightbend.com/blog/lightbend-monitoring-now-integrates-with-datadog-for-monitoring-akka-based-reactive-applications[11] https://www.lightbend.com/blog/how-to-get-monitoring-right-for-streaming-and-fast-data-systems-built-with-spark-mesos-akka-cassandra-and-kafka?utm_content=buffera2917&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer[12] http://zipkin.io/[13] https://www.theverge.com/2016/7/21/12246258/google-deepmind-ai-data-center-cooling[14] http://www.npr.org/sections/thetwo-way/2017/03/03/518322734/amazon-and-the-150-million-typo.[15] http://money.cnn.com/2016/09/07/technology/delta-computer-outage-cost/[16] https://www.slideshare.net/SparkSummit/ernest-efficient-performance-prediction-for-advanced-analytics-on-apache-spark-spark-summit-east-talk-by-shivaram-venkataraman[17] https://www.slideshare.net/SparkSummit/auto-scaling-systems-with-elastic-spark-streaming-spark-summit-east-talk-by-phuduc-nguyen[18] https://cloud.google.com/blog/big-data/2016/03/comparing-cloud-dataflow-autoscaling-to-spark-and-hadoop[19] https://www.youtube.com/watch?v=URWXG5jRB-A[20] https://en.wikipedia.org/wiki/Reinforcement_learning[21] https://stats.stackexchange.com/questions/234891/difference-between-convolution-neural-network-and-deep-learning[22] https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0[23] https://github.com/awjuliani/DeepRL-Agents/blob/master/Q-Network.ipynb[24] https://www.youtube.com/watch?v=22g14GtVhXk[25] https://www.youtube.com/watch?v=C-BY3JhXTiE

https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-sql-whole-stage-codegen.html

https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/6122906529858466/293651311471490/5382278320999420/latest.html

http://hydronitrogen.com/in-the-code-spark-sql-query-planning-and-execution.html

https://databricks.com/blog/2016/05/23/apache-spark-as-a-compiler-joining-a-billion-rows-per-second-on-a-laptop.html

http://www.vldb.org/pvldb/vol4/p539-neumann.pdf

https://blog.acolyer.org/2016/05/23/efficiently-compiling-efficient-query-plans-for-modern-hardware/

https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/spark-rdd-lineage.html

https://databricks.com/blog/2017/02/16/processing-trillion-rows-per-second-single-machine-can-nested-loop-joins-fast.html

https://www.slideshare.net/SparkSummit/costbased-optimizer-framework-for-spark-sql-spark-summit-east-talk-by-ron-hu-and-zhenhua-wang

https://www.lightbend.com/blog/lightbend-monitoring-now-integrates-with-datadog-for-monitoring-akka-based-reactive-applications

https://www.lightbend.com/blog/how-to-get-monitoring-right-for-streaming-and-fast-data-systems-built-with-spark-mesos-akka-cassandra-and-kafka?utm_content=buffera2917&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

https://www.lightbend.com/blog/how-to-get-monitoring-right-for-streaming-and-fast-data-systems-built-with-spark-mesos-akka-cassandra-and-kafka?utm_content=buffera2917&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

http://zipkin.io/

https://www.theverge.com/2016/7/21/12246258/google-deepmind-ai-data-center-cooling

http://money.cnn.com/2016/09/07/technology/delta-computer-outage-cost/

https://www.slideshare.net/SparkSummit/ernest-efficient-performance-prediction-for-advanced-analytics-on-apache-spark-spark-summit-east-talk-by-shivaram-venkataraman

https://www.slideshare.net/SparkSummit/auto-scaling-systems-with-elastic-spark-streaming-spark-summit-east-talk-by-phuduc-nguyen

https://cloud.google.com/blog/big-data/2016/03/comparing-cloud-dataflow-autoscaling-to-spark-and-hadoop

https://www.youtube.com/watch?v=URWXG5jRB-A

https://en.wikipedia.org/wiki/Reinforcement_learning

https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0

https://www.youtube.com/watch?v=22g14GtVhXk

Thank you

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