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Cloud EconomicsChris Bailey, IBM
@Chris__Bailey
“Cloud economics is a branch of knowledge concerned with the principles, costs and benefits of
cloud computing.” — searchcio.techtarget.com
Why Cloud?
Traditional Computing
§ On premise, or hosted bare metal machines
| @Chris__Bailey
Traditional Computing
§ On premise, or hosted bare metal machines
§ Capital Expenditure (CapEX): - Upfront cost, followed by depreciation
| @Chris__Bailey
Traditional Computing
§ On premise, or hosted bare metal machines
§ Capital Expenditure (CapEX): - Upfront cost, followed by depreciation
§ Requirement for accurate capacity planning - Estimate hardware for given load - Project future load requirements
| @Chris__Bailey
Traditional Computing
§ On premise, or hosted bare metal machines
§ Capital Expenditure (CapEX): - Upfront cost, followed by depreciation
§ Requirement for accurate capacity planning - Estimate hardware for given load - Project future load requirements
§ IT is seen as a cost centre
| @Chris__Bailey
Capacity Planning
§ Find a representative benchmark?
| @Chris__Bailey
SPECjEnterprise 2010 (spec.org)
Capacity Planning
§ Find a representative benchmark? - SPECjbb2015 - SPECjEnterprise2010 - SPECjms2007 - SPECjvm2008
| @Chris__Bailey
SPECjbb2015 (spec.org)
Capacity Planning
§ Find a representative benchmark? - SPECjbb2015 - SPECjEnterprise2010 - SPECjms2007 - SPECjvm2008
| @Chris__Bailey
SPECjbb2015 (spec.org)
Capacity Planning
§ Find a representative benchmark? - SPECjbb2015 - SPECjEnterprise2010 - SPECjms2007 - SPECjvm2008
| @Chris__Bailey
SPECjEnterprise2010 (spec.org)
Capacity Planning
§ Find a representative benchmark? - SPECjbb2015 - SPECjEnterprise2010 - SPECjms2007 - SPECjvm2008
| @Chris__Bailey
Capacity Planning
§ Find a representative benchmark? - SPECjbb2015 - SPECjEnterprise2010 - SPECjms2007 - SPECjvm2008
| @Chris__Bailey
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.2.1 on SPARC S7-2Jun 29, 2016 | HTML | Text 14,121.47 1 16 cores, 2 chips 1 16 cores, 2 chips
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.2.1 on SPARC S7-2Jun 29, 2016 | HTML | Text 14,400.78 1 16 cores, 2 chips 1 16 cores, 2 chip
| @Chris__Bailey
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.2.1 on SPARC S7-2Jun 29, 2016 | HTML | Text 14,121.47 1 16 cores, 2 chips 1 16 cores, 2 chips
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.2.1 on SPARC S7-2Jun 29, 2016 | HTML | Text 14,400.78 1 16 cores, 2 chips 1 16 cores, 2 chip
| @Chris__Bailey
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle Weblogic Server Standard Edition Release 12.2.1 on Oracle Server X6-2Mar 31, 2016 | HTML | Text NC 1 44 cores, 2 chips 1 44 cores, 2 chips
| @Chris__Bailey
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle Weblogic Server Standard Edition Release 12.2.1 on Oracle Server X6-2Mar 31, 2016 | HTML | Text NC 1 44 cores, 2 chips 1 44 cores, 2 chips
| @Chris__Bailey
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.1.3 on Oracle Server X5-2Apr 1, 2015 | HTML | Text 21,504.30 1 36 cores, 2 chips 1 36 cores, 2 chips
| @Chris__Bailey
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.1.3 on Oracle Server X5-2Apr 1, 2015 | HTML | Text 21,504.30 1 36 cores, 2 chips 1 36 cores, 2 chips
| @Chris__Bailey
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.1.3 on Oracle Server X5-2Apr 1, 2015 | HTML | Text 21,504.30 1 36 cores, 2 chips 1 36 cores, 2 chips
| @Chris__Bailey
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.1.3 on Oracle Server X5-2Apr 1, 2015 | HTML | Text 21,504.30 1 36 cores, 2 chips 1 36 cores, 2 chips
| @Chris__Bailey
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
- Intel® Xeon® processor E5- 2600 v3 @3.6GHz
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.1.3 on Oracle Server X5-2Apr 1, 2015 | HTML | Text 21,504.30 1 36 cores, 2 chips 1 36 cores, 2 chips
| @Chris__Bailey
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
§ Calculate transactions per core
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.1.3 on Oracle Server X5-2Apr 1, 2015 | HTML | Text 21,504.30 1 36 cores, 2 chips 1 36 cores, 2 chips
| @Chris__Bailey
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
§ Calculate transactions per core - 21,504.30 / 36 = 597.3 EjOPS/core
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.1.3 on Oracle Server X5-2Apr 1, 2015 | HTML | Text 21,504.30 1 36 cores, 2 chips 1 36 cores, 2 chips
| @Chris__Bailey
Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
PurchasingDelay
Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
PurchasingDelay
Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
PurchasingDelay
Capacity Requirement
Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
PurchasingDelay
Capacity Requirement
Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
PurchasingDelay
Predicted Load + 10%Capacity Requirement
Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted LoadCapacity Requirement
PurchasingDelay
Predicted Load + 10%
Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
Capacity Requirement
PurchasingDelay
Predicted Load + 10%
Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
Capacity Requirement
PurchasingDelay
Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
Capacity Requirement
PurchasingDelay
Excess Capacity
Excess Capacity
Capacity Planning
§ Size for peak demand - Allow for Black Friday / Cyber Monday - Or just changes in load throughout each day
| @Chris__Bailey
Capacity Planning
§ Size for peak demand - Allow for Black Friday / Cyber Monday - Or just changes in load throughout each day
§ Allow for High Availability (HA) and Disaster Recovery (DR) - Rule of three for HA - 50% extra capacity - Full fail-over for DR - 100% extra capacity
| @Chris__Bailey
Capacity Planning
§ Size for peak demand - Allow for Black Friday / Cyber Monday - Or just changes in load throughout each day
§ Allow for High Availability (HA) and Disaster Recovery (DR) - Rule of three for HA - 50% extra capacity - Full fail-over for DR - 100% extra capacity
§ Oh, and what about capacity for integration, stress and performance test?
| @Chris__Bailey
Most enterprises have hardware utilisation below 20%
Most enterprises have hardware utilisation below 20% and below 10% for workloads with peaks
Capacity Planning and Agility
§ Time to obtain capacity affects operational agility
§ Market opportunities lost because of lead time for new projects
| @Chris__Bailey
Capacity Planning and Agility
§ Time to obtain capacity affects operational agility
§ Market opportunities lost because of lead time for new projects
§ Case Study: A fashion retailer can show measureable increase in sales if a item similar to that seen in the media can be placed on their on-line store landing page within 1 hr of it appearing in public.
§ Each product placement is different so they need a fast, agile, approach that does not jeopardize their on-line stores availability and quality.
| @Chris__Bailey
Cloud Nirvana
What Cloud Promises
“a virtual, dynamic environment which maximizes use, is infinitely scalable, always available and needs minimal upfront investment or commitment”
§ Take your code – host it on someone else's machine and pay only for the resource you use for the time you use it
§ AND be able to do that very quickly and repeatedly in parallel
| @Chris__Bailey
Cloud Values
§ Much shorter “purchasing delay”
§ Return integration, stress and performance test environments after use
§ Cloud guarantees remove need for DR
| @Chris__Bailey
Traditional Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
Capacity Requirement
PurchasingDelay
Excess Capacity
Excess Capacity
Cloud Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
Capacity Requirement
Excess Capacity
Excess Capacity
Cloud Capacity Planning
| @Chris__Bailey
Time
Cap
acity
Predicted Load
Capacity Requirement
Excess Capacit
y
Capacity Planning and Agility
§ Case Study: A fashion retailer can show measureable increase in sales if a item similar to that seen in the media can be placed on their on-line store landing page within 1 hr of it appearing in public.
§ Each product placement is different so they need a fast, agile, approach that does not jeopardize their on-line stores availability and quality.
| @Chris__Bailey
Capacity Planning and Agility
§ Case Study: A fashion retailer can show measureable increase in sales if a item similar to that seen in the media can be placed on their on-line store landing page within 1 hr of it appearing in public.
§ Each product placement is different so they need a fast, agile, approach that does not jeopardize their on-line stores availability and quality.
Achievement Unlocked!
| @Chris__Bailey
Other advantages of Cloud§ Globally distributed data centres and locality
| @Chris__Bailey
https://aws.amazon.com/about-aws/global-infrastructure/
Other advantages of Cloud§ Globally distributed data centres and locality
| @Chris__Bailey
https://aws.amazon.com/about-aws/global-infrastructure/
Trouble in Paradise?
OpEx vs CapEx…
§ Rent vs Buy
| @Chris__Bailey
OpEx vs CapEx…
§ Rent vs Buy
§ No upfront cost, but no end to cost
| @Chris__Bailey
OpEx vs CapEx…
§ Rent vs Buy
§ No upfront cost, but no end to cost
§ Drive for operational efficiency
| @Chris__Bailey
OpEx vs CapEx…
§ Rent vs Buy
§ No upfront cost, but no end to cost
§ Drive for operational efficiency
§ Focus on ROI for individual apps and app features
| @Chris__Bailey
How is Cloud cost calculated?
| @Chris__Bailey
Provider Type Memory CPUs Cost/monthAmazon EC2 Linux t2.nano 512MB 1 vCPU $4.68
Linux t2.micro 1GB 1 vCPU $9.36Linux t2.small 2GB 1 vCPU $18.72
Linux t2.medium 4GB 2 vCPUs $37.44Digital Ocean Standard 512MB 1 Core Processor $5.00
Standard 1GB 1 Core Processor $10.00Standard 2GB 2 Core Processors $20.00
Pivotal Cloud Foundry App Instance 512MB 4 vCPUs $10.80App Instance 1GB 4 vCPUs $21.60App Instance 2GB 4 vCPUs $43.20
Heroku Standard 1x 512MB 1 Share $25.00Standard 2x 1GB 2 Shares $50.00
IBM Bluemix Instant Runtimes 512MB 4 vCPUs $24.15Instant Runtimes 1GB 4 vCPUs $49.35
Containers 512MB 4 vCPUs $10.22Containers 1GB 4 vCPUs $20.59
$£€¥ = GB/Hr
Movement from CPU to Memory based costs is a fundamental shift for Application Architectures
§ Find a representative benchmark? - SPECjEnterprise2010
§ Analyse the results
- Intel® Xeon® processor E5- 2600 v3 @3.6GHz
Capacity Planning
Tested By System Name ResultJava EE Server DB Server
Nodes CPU Nodes CPU
Oracle Corporation Oracle WebLogic Server Standard Edition Release 12.1.3 on Oracle Server X5-2Apr 1, 2015 | HTML | Text 21,504.30 1 36 cores, 2 chips 1 36 cores, 2 chips
| @Chris__Bailey
Reading the fine print…
Hardware Vendor: Oracle CorporationModel Name: Oracle Server X5-2Processor: Intel Xeon processor E5-2699 v3 (Intel Turbo Boost Technology up to 3.6 GHz)MHz: 2300# of CPUs: 36 cores, 2 chips, 18 cores/chip, 2 threads/core (Hyper-Threading)Memory (MB): 262144Disks: 2x 600GB SAS-2 HDD and 2x 400GB SSDNetwork Interface: 4x 10Gbit onboard and 3x 10GBit Dual-port NICsJVM Options:-server -Xms18g -Xmx18g -Xmn16g -Xss256k -XX:MetaspaceSize=200M -XX:+AggressiveOpts -XX:+UseParallelOldGC -XX:ParallelGCThreads=9 -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps-XX:InitialCodeCacheSize=200m -XX:ReservedCodeCacheSize=200m -XX:+UseCompressedOops -XX:-UseBiasedLocking -XX:+AlwaysPreTouch -XX:InitialSurvivorRatio=24 -XX:SurvivorRatio=24 -XX:TargetSurvivorRatio=90-XX:-UseAdaptiveSizePolicy -XX:+UseLargePages -Djava.net.preferIPv4Stack=true -Djaxws.transport.streaming=true -XX:-TieredCompilation -Dweblogic.ProductionModeEnabled=true-Dweblogic.management.discover=false -Dweblogic.diagnostics.debug.DebugLogger.DISABLED=true -Doracle.jdbc.defaultRowPrefetch=200 -Djavax.xml.parsers.DocumentBuilderFactory=weblogic.xml.jaxp.RegistryDocumentBuilderFactory -Dweblogic.threadpool.MinPoolSize=30 -Dsun.net.inetaddr.ttl=0 -Dnetworkaddress.cache.ttl=0-Dcom.sun.xml.ws.spi.db.BindingContextFactory=com.sun.xml.ws.db.glassfish.JAXBRIContextFactory -Djavax.xml.bind.JAXBContext=com.sun.xml.bind.v2.ContextFactory-Dweblogic.MuxerClass=weblogic.socket.NIOSocketMuxer -Dweblogic.SocketReaders=2
Server instances were started using numactl binding 1 instance per 9 cores.
| @Chris__Bailey
Reading the fine print…
Hardware Vendor: Oracle CorporationModel Name: Oracle Server X5-2Processor: Intel Xeon processor E5-2699 v3 (Intel Turbo Boost Technology up to 3.6 GHz)MHz: 2300# of CPUs: 36 cores, 2 chips, 18 cores/chip, 2 threads/core (Hyper-Threading)Memory (MB): 262144Disks: 2x 600GB SAS-2 HDD and 2x 400GB SSDNetwork Interface: 4x 10Gbit onboard and 3x 10GBit Dual-port NICsJVM Options:-server -Xms18g -Xmx18g -Xmn16g -Xss256k -XX:MetaspaceSize=200M -XX:+AggressiveOpts -XX:+UseParallelOldGC -XX:ParallelGCThreads=9 -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps-XX:InitialCodeCacheSize=200m -XX:ReservedCodeCacheSize=200m -XX:+UseCompressedOops -XX:-UseBiasedLocking -XX:+AlwaysPreTouch -XX:InitialSurvivorRatio=24 -XX:SurvivorRatio=24 -XX:TargetSurvivorRatio=90-XX:-UseAdaptiveSizePolicy -XX:+UseLargePages -Djava.net.preferIPv4Stack=true -Djaxws.transport.streaming=true -XX:-TieredCompilation -Dweblogic.ProductionModeEnabled=true-Dweblogic.management.discover=false -Dweblogic.diagnostics.debug.DebugLogger.DISABLED=true -Doracle.jdbc.defaultRowPrefetch=200 -Djavax.xml.parsers.DocumentBuilderFactory=weblogic.xml.jaxp.RegistryDocumentBuilderFactory -Dweblogic.threadpool.MinPoolSize=30 -Dsun.net.inetaddr.ttl=0 -Dnetworkaddress.cache.ttl=0-Dcom.sun.xml.ws.spi.db.BindingContextFactory=com.sun.xml.ws.db.glassfish.JAXBRIContextFactory -Djavax.xml.bind.JAXBContext=com.sun.xml.bind.v2.ContextFactory-Dweblogic.MuxerClass=weblogic.socket.NIOSocketMuxer -Dweblogic.SocketReaders=2
Server instances were started using numactl binding 1 instance per 9 cores.
| @Chris__Bailey
Reading the fine print…
Hardware Vendor: Oracle CorporationModel Name: Oracle Server X5-2Processor: Intel Xeon processor E5-2699 v3 (Intel Turbo Boost Technology up to 3.6 GHz)MHz: 2300# of CPUs: 36 cores, 2 chips, 18 cores/chip, 2 threads/core (Hyper-Threading)Memory (MB): 262144Disks: 2x 600GB SAS-2 HDD and 2x 400GB SSDNetwork Interface: 4x 10Gbit onboard and 3x 10GBit Dual-port NICs
7.1GB / core
JVM Options:-server -Xms18g -Xmx18g -Xmn16g -Xss256k -XX:MetaspaceSize=200M -XX:+AggressiveOpts -XX:+UseParallelOldGC -XX:ParallelGCThreads=9 -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps-XX:InitialCodeCacheSize=200m -XX:ReservedCodeCacheSize=200m -XX:+UseCompressedOops -XX:-UseBiasedLocking -XX:+AlwaysPreTouch -XX:InitialSurvivorRatio=24 -XX:SurvivorRatio=24 -XX:TargetSurvivorRatio=90-XX:-UseAdaptiveSizePolicy -XX:+UseLargePages -Djava.net.preferIPv4Stack=true -Djaxws.transport.streaming=true -XX:-TieredCompilation -Dweblogic.ProductionModeEnabled=true-Dweblogic.management.discover=false -Dweblogic.diagnostics.debug.DebugLogger.DISABLED=true -Doracle.jdbc.defaultRowPrefetch=200 -Djavax.xml.parsers.DocumentBuilderFactory=weblogic.xml.jaxp.RegistryDocumentBuilderFactory -Dweblogic.threadpool.MinPoolSize=30 -Dsun.net.inetaddr.ttl=0 -Dnetworkaddress.cache.ttl=0-Dcom.sun.xml.ws.spi.db.BindingContextFactory=com.sun.xml.ws.db.glassfish.JAXBRIContextFactory -Djavax.xml.bind.JAXBContext=com.sun.xml.bind.v2.ContextFactory-Dweblogic.MuxerClass=weblogic.socket.NIOSocketMuxer -Dweblogic.SocketReaders=2
Server instances were started using numactl binding 1 instance per 9 cores.
| @Chris__Bailey
§ Project Management Triangle § Fast § Good § Cheap
§ Pick any two…
Performance Optimisation
Euler Diagram
Cheap
Fast Good
| @Chris__Bailey
§ Performance Triangle § Throughput § Latency § Memory footprint
§ Enterprise software has traditionally chosenthroughput and latency over memory § Data caching § Garbage Collection § Function Inlining
LowMemory
HighThroughput
LowLatency
Performance Optimisation
Euler Diagram
| @Chris__Bailey
Garbage Collection Tradeoff
| @Chris__Bailey
Mem
ory
Heap Size
Garbage Collection Tradeoff
| @Chris__Bailey
Mem
ory
Live in-use data(Retained Set)
Heap Size
Garbage Collection Tradeoff
| @Chris__Bailey
Mem
ory
Live in-use data(Retained Set)
Heap Size
Temporary Data(Garbage)
Temporary Data(Garbage)
Garbage Collection Tradeoff
| @Chris__Bailey
Mem
ory
Live in-use data(Retained Set)
Heap Size
Temporary Data(Garbage)
Temporary Data(Garbage)
Garbage Collection Tradeoff
| @Chris__Bailey
Mem
ory
Live in-use data(Retained Set)
Heap Size
Temporary Data(Garbage)
Temporary Data(Garbage)
Garbage Collection Tradeoff
| @Chris__Bailey
Mem
ory
Live in-use data(Retained Set)
Heap Size
Temporary Data(Garbage)
Temporary Data(Garbage)
40 to 70%
Garbage Collection Tradeoff
| @Chris__Bailey
Mem
ory
Live in-use data(Retained Set)
Heap Size
Temporary Data(Garbage)
Temporary Data(Garbage)
40 to 70%
30 to 60% additional memory for additional performance
Optimising for the Cloud
Optimising for the Cloud#1: Choose The Right Language
Memory Usage by Language
| @Chris__Bailey
0
17.5
35
52.5
70
C++
Memusage(MB)
C
Memory Usage by Language
| @Chris__Bailey
0
17.5
35
52.5
70
C++ Go Sw
i:Rust
Memusage(MB)
ModernNativeC
Memory Usage by Language
| @Chris__Bailey
0
17.5
35
52.5
70
C++ Go Sw
i:Rust
Ruby PH
P
Python
JavaScript
Erlang
Dart
Memusage(MB)
ModernNative ScriptingC
Memory Usage by Language
| @Chris__Bailey
0
17.5
35
52.5
70
C++ Go Sw
i:Rust
Ruby PH
P
Python
JavaScript
Erlang
Dart
Java
Scala
Clojure
Memusage(MB)
ModernNative Scripting JVMC
Throughput by Language
| @Chris__Bailey
C
0
25
50
75
100
C++
DuraOon
Throughput by Language
| @Chris__Bailey
ModernNativeC
0
25
50
75
100
C++ Go Sw
i:Rust
DuraOon
Throughput by Language
| @Chris__Bailey
ModernNative ScriptingC
0
25
50
75
100
C++ Go Sw
i:Rust
Ruby PH
P
Python
JavaScript
Erlang
Dart
DuraOon
Throughput by Language
| @Chris__Bailey
ModernNative Scripting JVMC
0
25
50
75
100
C++ Go Sw
i:Rust
Ruby PH
P
Python
JavaScript
Erlang
Dart
Java
Scala
Clojure
DuraOon
Normalised Throughput/Memory Language
| @Chris__Bailey
C
0
20
40
60
80
C++
DuraOon
Normalised Throughput/Memory Language
| @Chris__Bailey
ModernNativeC
0
20
40
60
80
C++ Go Sw
i:Rust
DuraOon
Normalised Throughput/Memory Language
| @Chris__Bailey
ModernNative ScriptingC
0
20
40
60
80
C++ Go Sw
i:Rust
Ruby PH
P
Python
JavaScript
Erlang
Dart
DuraOon
Normalised Throughput/Memory Language
| @Chris__Bailey
ModernNative Scripting JVMC
0
20
40
60
80
C++ Go Sw
i:Rust
Ruby PH
P
Python
JavaScript
Erlang
Dart
Java
Scala
Clojure
DuraOon
Selecting the Language
§ Ruby on Rails app, 100 instances at 500MB / instance - 50GB of memory required at $24.15 GB/month - = $14,490/year
| @Chris__Bailey
Selecting the Language
§ Ruby on Rails app, 100 instances at 500MB / instance - 50GB of memory required at $24.15 GB/month - = $14,490/year
§ Switching to Java provides a 7x saving: - = $2,070/year
| @Chris__Bailey
Selecting the Language
§ Ruby on Rails app, 100 instances at 500MB / instance - 50GB of memory required at $24.15 GB/month - = $14,490/year
§ Switching to Java provides a 7x saving: - = $2,070/year
§ Switching to Swift provides 42x saving: - = $345/year
| @Chris__Bailey
Selecting the Language
| @Chris__Bailey
C++ Go Swift Rust Ruby PHP Python JScript Erlang Dart Java Scala Clojure
C++ 4.02 10.96 14.65 461.69 251.83 689.22 40.25 359.13 75.16 65.18 79.88 169.17
Go 0.25 2.73 3.64 114.79 62.61 171.37 10.01 89.29 18.69 16.21 19.86 42.06
Swift 0.09 0.37 1.34 42.11 22.97 62.86 3.67 32.75 6.86 5.95 7.29 15.43
Rust 0.07 0.27 0.75 31.52 17.19 47.06 2.75 24.52 5.13 4.45 5.45 11.55
Ruby 0.00 0.01 0.02 0.03 0.55 1.49 0.09 0.78 0.16 0.14 0.17 0.37
PHP 0.00 0.02 0.04 0.06 1.83 2.74 0.16 1.43 0.30 0.26 0.32 0.67
Python 0.00 0.01 0.02 0.02 0.67 0.37 0.06 0.52 0.11 0.09 0.12 0.25
JScript 0.02 0.10 0.27 0.36 11.47 6.26 17.13 8.92 1.87 1.62 1.98 4.20
Erlang 0.00 0.01 0.03 0.04 1.29 0.70 1.92 0.11 0.21 0.18 0.22 0.47
Dart 0.01 0.05 0.15 0.19 6.14 3.35 9.17 0.54 4.78 0.87 1.06 2.25
Java 0.02 0.06 0.17 0.22 7.08 3.86 10.57 0.62 5.51 1.15 1.23 2.60
Scala 0.01 0.05 0.14 0.18 5.78 3.15 8.63 0.50 4.50 0.94 0.82 2.12
Clojure 0.01 0.02 0.06 0.09 2.73 1.49 4.07 0.24 2.12 0.44 0.39 0.47
From
To
Selecting the Language
| @Chris__Bailey
C++ Go Swift Rust Ruby PHP Python JScript Erlang Dart Java Scala Clojure
C++ 4.02 10.96 14.65 461.69 251.83 689.22 40.25 359.13 75.16 65.18 79.88 169.17
Go 0.25 2.73 3.64 114.79 62.61 171.37 10.01 89.29 18.69 16.21 19.86 42.06
Swift 0.09 0.37 1.34 42.11 22.97 62.86 3.67 32.75 6.86 5.95 7.29 15.43
Rust 0.07 0.27 0.75 31.52 17.19 47.06 2.75 24.52 5.13 4.45 5.45 11.55
Ruby 0.00 0.01 0.02 0.03 0.55 1.49 0.09 0.78 0.16 0.14 0.17 0.37
PHP 0.00 0.02 0.04 0.06 1.83 2.74 0.16 1.43 0.30 0.26 0.32 0.67
Python 0.00 0.01 0.02 0.02 0.67 0.37 0.06 0.52 0.11 0.09 0.12 0.25
JScript 0.02 0.10 0.27 0.36 11.47 6.26 17.13 8.92 1.87 1.62 1.98 4.20
Erlang 0.00 0.01 0.03 0.04 1.29 0.70 1.92 0.11 0.21 0.18 0.22 0.47
Dart 0.01 0.05 0.15 0.19 6.14 3.35 9.17 0.54 4.78 0.87 1.06 2.25
Java 0.02 0.06 0.17 0.22 7.08 3.86 10.57 0.62 5.51 1.15 1.23 2.60
Scala 0.01 0.05 0.14 0.18 5.78 3.15 8.63 0.50 4.50 0.94 0.82 2.12
Clojure 0.01 0.02 0.06 0.09 2.73 1.49 4.07 0.24 2.12 0.44 0.39 0.47
From
To
C
Selecting the Language
| @Chris__Bailey
C++ Go Swift Rust Ruby PHP Python JScript Erlang Dart Java Scala Clojure
C++ 4.02 10.96 14.65 461.69 251.83 689.22 40.25 359.13 75.16 65.18 79.88 169.17
Go 0.25 2.73 3.64 114.79 62.61 171.37 10.01 89.29 18.69 16.21 19.86 42.06
Swift 0.09 0.37 1.34 42.11 22.97 62.86 3.67 32.75 6.86 5.95 7.29 15.43
Rust 0.07 0.27 0.75 31.52 17.19 47.06 2.75 24.52 5.13 4.45 5.45 11.55
Ruby 0.00 0.01 0.02 0.03 0.55 1.49 0.09 0.78 0.16 0.14 0.17 0.37
PHP 0.00 0.02 0.04 0.06 1.83 2.74 0.16 1.43 0.30 0.26 0.32 0.67
Python 0.00 0.01 0.02 0.02 0.67 0.37 0.06 0.52 0.11 0.09 0.12 0.25
JScript 0.02 0.10 0.27 0.36 11.47 6.26 17.13 8.92 1.87 1.62 1.98 4.20
Erlang 0.00 0.01 0.03 0.04 1.29 0.70 1.92 0.11 0.21 0.18 0.22 0.47
Dart 0.01 0.05 0.15 0.19 6.14 3.35 9.17 0.54 4.78 0.87 1.06 2.25
Java 0.02 0.06 0.17 0.22 7.08 3.86 10.57 0.62 5.51 1.15 1.23 2.60
Scala 0.01 0.05 0.14 0.18 5.78 3.15 8.63 0.50 4.50 0.94 0.82 2.12
Clojure 0.01 0.02 0.06 0.09 2.73 1.49 4.07 0.24 2.12 0.44 0.39 0.47
From
To
C
Modern Native
Selecting the Language
| @Chris__Bailey
C++ Go Swift Rust Ruby PHP Python JScript Erlang Dart Java Scala Clojure
C++ 4.02 10.96 14.65 461.69 251.83 689.22 40.25 359.13 75.16 65.18 79.88 169.17
Go 0.25 2.73 3.64 114.79 62.61 171.37 10.01 89.29 18.69 16.21 19.86 42.06
Swift 0.09 0.37 1.34 42.11 22.97 62.86 3.67 32.75 6.86 5.95 7.29 15.43
Rust 0.07 0.27 0.75 31.52 17.19 47.06 2.75 24.52 5.13 4.45 5.45 11.55
Ruby 0.00 0.01 0.02 0.03 0.55 1.49 0.09 0.78 0.16 0.14 0.17 0.37
PHP 0.00 0.02 0.04 0.06 1.83 2.74 0.16 1.43 0.30 0.26 0.32 0.67
Python 0.00 0.01 0.02 0.02 0.67 0.37 0.06 0.52 0.11 0.09 0.12 0.25
JScript 0.02 0.10 0.27 0.36 11.47 6.26 17.13 8.92 1.87 1.62 1.98 4.20
Erlang 0.00 0.01 0.03 0.04 1.29 0.70 1.92 0.11 0.21 0.18 0.22 0.47
Dart 0.01 0.05 0.15 0.19 6.14 3.35 9.17 0.54 4.78 0.87 1.06 2.25
Java 0.02 0.06 0.17 0.22 7.08 3.86 10.57 0.62 5.51 1.15 1.23 2.60
Scala 0.01 0.05 0.14 0.18 5.78 3.15 8.63 0.50 4.50 0.94 0.82 2.12
Clojure 0.01 0.02 0.06 0.09 2.73 1.49 4.07 0.24 2.12 0.44 0.39 0.47
From
To
C
Modern Native
Scripting
Selecting the Language
| @Chris__Bailey
C++ Go Swift Rust Ruby PHP Python JScript Erlang Dart Java Scala Clojure
C++ 4.02 10.96 14.65 461.69 251.83 689.22 40.25 359.13 75.16 65.18 79.88 169.17
Go 0.25 2.73 3.64 114.79 62.61 171.37 10.01 89.29 18.69 16.21 19.86 42.06
Swift 0.09 0.37 1.34 42.11 22.97 62.86 3.67 32.75 6.86 5.95 7.29 15.43
Rust 0.07 0.27 0.75 31.52 17.19 47.06 2.75 24.52 5.13 4.45 5.45 11.55
Ruby 0.00 0.01 0.02 0.03 0.55 1.49 0.09 0.78 0.16 0.14 0.17 0.37
PHP 0.00 0.02 0.04 0.06 1.83 2.74 0.16 1.43 0.30 0.26 0.32 0.67
Python 0.00 0.01 0.02 0.02 0.67 0.37 0.06 0.52 0.11 0.09 0.12 0.25
JScript 0.02 0.10 0.27 0.36 11.47 6.26 17.13 8.92 1.87 1.62 1.98 4.20
Erlang 0.00 0.01 0.03 0.04 1.29 0.70 1.92 0.11 0.21 0.18 0.22 0.47
Dart 0.01 0.05 0.15 0.19 6.14 3.35 9.17 0.54 4.78 0.87 1.06 2.25
Java 0.02 0.06 0.17 0.22 7.08 3.86 10.57 0.62 5.51 1.15 1.23 2.60
Scala 0.01 0.05 0.14 0.18 5.78 3.15 8.63 0.50 4.50 0.94 0.82 2.12
Clojure 0.01 0.02 0.06 0.09 2.73 1.49 4.07 0.24 2.12 0.44 0.39 0.47
From
To
C
Modern Native
Scripting
Selecting the Language
| @Chris__Bailey
C++ Go Swift Rust Ruby PHP Python JScript Erlang Dart Java Scala Clojure
C++ 4.02 10.96 14.65 461.69 251.83 689.22 40.25 359.13 75.16 65.18 79.88 169.17
Go 0.25 2.73 3.64 114.79 62.61 171.37 10.01 89.29 18.69 16.21 19.86 42.06
Swift 0.09 0.37 1.34 42.11 22.97 62.86 3.67 32.75 6.86 5.95 7.29 15.43
Rust 0.07 0.27 0.75 31.52 17.19 47.06 2.75 24.52 5.13 4.45 5.45 11.55
Ruby 0.00 0.01 0.02 0.03 0.55 1.49 0.09 0.78 0.16 0.14 0.17 0.37
PHP 0.00 0.02 0.04 0.06 1.83 2.74 0.16 1.43 0.30 0.26 0.32 0.67
Python 0.00 0.01 0.02 0.02 0.67 0.37 0.06 0.52 0.11 0.09 0.12 0.25
JScript 0.02 0.10 0.27 0.36 11.47 6.26 17.13 8.92 1.87 1.62 1.98 4.20
Erlang 0.00 0.01 0.03 0.04 1.29 0.70 1.92 0.11 0.21 0.18 0.22 0.47
Dart 0.01 0.05 0.15 0.19 6.14 3.35 9.17 0.54 4.78 0.87 1.06 2.25
Java 0.02 0.06 0.17 0.22 7.08 3.86 10.57 0.62 5.51 1.15 1.23 2.60
Scala 0.01 0.05 0.14 0.18 5.78 3.15 8.63 0.50 4.50 0.94 0.82 2.12
Clojure 0.01 0.02 0.06 0.09 2.73 1.49 4.07 0.24 2.12 0.44 0.39 0.47
From
To
C
Modern Native
Scripting
Selecting the Language
| @Chris__Bailey
C++ Go Swift Rust Ruby PHP Python JScript Erlang Dart Java Scala Clojure
C++ 4.02 10.96 14.65 461.69 251.83 689.22 40.25 359.13 75.16 65.18 79.88 169.17
Go 0.25 2.73 3.64 114.79 62.61 171.37 10.01 89.29 18.69 16.21 19.86 42.06
Swift 0.09 0.37 1.34 42.11 22.97 62.86 3.67 32.75 6.86 5.95 7.29 15.43
Rust 0.07 0.27 0.75 31.52 17.19 47.06 2.75 24.52 5.13 4.45 5.45 11.55
Ruby 0.00 0.01 0.02 0.03 0.55 1.49 0.09 0.78 0.16 0.14 0.17 0.37
PHP 0.00 0.02 0.04 0.06 1.83 2.74 0.16 1.43 0.30 0.26 0.32 0.67
Python 0.00 0.01 0.02 0.02 0.67 0.37 0.06 0.52 0.11 0.09 0.12 0.25
JScript 0.02 0.10 0.27 0.36 11.47 6.26 17.13 8.92 1.87 1.62 1.98 4.20
Erlang 0.00 0.01 0.03 0.04 1.29 0.70 1.92 0.11 0.21 0.18 0.22 0.47
Dart 0.01 0.05 0.15 0.19 6.14 3.35 9.17 0.54 4.78 0.87 1.06 2.25
Java 0.02 0.06 0.17 0.22 7.08 3.86 10.57 0.62 5.51 1.15 1.23 2.60
Scala 0.01 0.05 0.14 0.18 5.78 3.15 8.63 0.50 4.50 0.94 0.82 2.12
Clojure 0.01 0.02 0.06 0.09 2.73 1.49 4.07 0.24 2.12 0.44 0.39 0.47
From
To
C
Modern Native
Scripting
JVM
Selecting the Language
| @Chris__Bailey
C++ Go Swift Rust Ruby PHP Python JScript Erlang Dart Java Scala Clojure
C++ 4.02 10.96 14.65 461.69 251.83 689.22 40.25 359.13 75.16 65.18 79.88 169.17
Go 0.25 2.73 3.64 114.79 62.61 171.37 10.01 89.29 18.69 16.21 19.86 42.06
Swift 0.09 0.37 1.34 42.11 22.97 62.86 3.67 32.75 6.86 5.95 7.29 15.43
Rust 0.07 0.27 0.75 31.52 17.19 47.06 2.75 24.52 5.13 4.45 5.45 11.55
Ruby 0.00 0.01 0.02 0.03 0.55 1.49 0.09 0.78 0.16 0.14 0.17 0.37
PHP 0.00 0.02 0.04 0.06 1.83 2.74 0.16 1.43 0.30 0.26 0.32 0.67
Python 0.00 0.01 0.02 0.02 0.67 0.37 0.06 0.52 0.11 0.09 0.12 0.25
JScript 0.02 0.10 0.27 0.36 11.47 6.26 17.13 8.92 1.87 1.62 1.98 4.20
Erlang 0.00 0.01 0.03 0.04 1.29 0.70 1.92 0.11 0.21 0.18 0.22 0.47
Dart 0.01 0.05 0.15 0.19 6.14 3.35 9.17 0.54 4.78 0.87 1.06 2.25
Java 0.02 0.06 0.17 0.22 7.08 3.86 10.57 0.62 5.51 1.15 1.23 2.60
Scala 0.01 0.05 0.14 0.18 5.78 3.15 8.63 0.50 4.50 0.94 0.82 2.12
Clojure 0.01 0.02 0.06 0.09 2.73 1.49 4.07 0.24 2.12 0.44 0.39 0.47
From
To
C
Modern Native
Scripting
JVM
Optimising for the Cloud
Optimising for the Cloud#2: Use Intelligent Scaling
Intelligent Scaling for Capacity
| @Chris__Bailey
Time
Cap
acity
Predicted Load
Capacity Requirement
Excess Capacit
y
Intelligent Scaling for Capacity
| @Chris__Bailey
Time
Cap
acity
Predicted Load
Capacity Requirement
Excess Capacit
y
Intelligent Scaling for Capacity
| @Chris__Bailey
Time
Cap
acity
Actual Load
Capacity Requirement
Excess Capacit
y
Intelligent Scaling for Capacity
| @Chris__Bailey
Time
Cap
acity
Actual Load
Capacity Requirement
Excess Capacit
y
Intelligent Scaling for Capacity
| @Chris__Bailey
Time
Cap
acity
Actual Load
Capacity Requirement
Excess Capacit
y
Dynamic Scaling to Load
| @Chris__Bailey
00:00
Cap
acity
Actual Load
Excess Capacit
y
12:00 18:0006:0000:00
Capacity
Dynamic Scaling to Load
| @Chris__Bailey
00:00
Cap
acity
Actual LoadCapacity
Excess Capacit
y
12:00 18:0006:0000:00
Dynamic Scaling to Load
§ Daily load cycles are common for user facing applications - Particularly if your using localised availability zones
| @Chris__Bailey
“Most enterprises have hardware utilisation below 20% and below 10% for workloads with peaks”
Dynamic Scaling to Load
§ Daily load cycles are common for user facing applications - Particularly if your using localised availability zones
§ Intelligent Scaling can provide a 2x saving:
| @Chris__Bailey
“Most enterprises have hardware utilisation below 20% and below 10% for workloads with peaks”
Dynamic Scaling to Load
§ Daily load cycles are common for user facing applications - Particularly if your using localised availability zones
§ Intelligent Scaling can provide a 2x saving: - Ruby on Rails app: $14,490/year - Move to Java: $ 2,070/year
| @Chris__Bailey
“Most enterprises have hardware utilisation below 20% and below 10% for workloads with peaks”
Dynamic Scaling to Load
§ Daily load cycles are common for user facing applications - Particularly if your using localised availability zones
§ Intelligent Scaling can provide a 2x saving: - Ruby on Rails app: $14,490/year - Move to Java: $ 2,070/year - Add scaling: $ 1,035/year
| @Chris__Bailey
“Most enterprises have hardware utilisation below 20% and below 10% for workloads with peaks”
Optimising for the Cloud
Optimising for the Cloud#3: Identify and Fix Memory Leaks
Look for and Fix memory leaks§ A small leak means a very large
amount of memory over time
| @Chris__Bailey
AmericanSocietyofCivilEngineers
Look for and Fix memory leaks§ A small leak means a very large
amount of memory over time
§ Memory leaks act a little like interest on a debt
| @Chris__Bailey
Live in-use data(Principle)
Look for and Fix memory leaks§ A small leak means a very large
amount of memory over time
§ Memory leaks act a little like interest on a debt
| @Chris__Bailey
Live in-use data(Principle)
Memory Leak(Interest)
Look for and Fix memory leaks§ A small leak means a very large
amount of memory over time
§ Memory leaks act a little like interest on a debt
§ Made worse if you have to size the container ahead of time to allow for the leak
| @Chris__Bailey
Live in-use data(Principle)
Memory Leak(Interest)
Optimising for the Cloud
Optimising for the Cloud#4: Memory Analysis is the New Performance Tuning
Memory Analysis
§ Most applications are memory in-efficient - Duplication of data - Oversized collections - Empty collections
§ Initial analysis and optimisation usual has a large return on investment
| @Chris__Bailey
Memory Analysis
§ Java case study: PlantsByWebSphere sample in WebSphere AppServer 7 - 5 user test load, 206MB of “live data”:
| @Chris__Bailey
Memory Analysis
§ Java case study: PlantsByWebSphere sample in WebSphere AppServer 7 - 5 user test load, 206MB of “live data”:
| @Chris__Bailey
Collection Count Collection Size (MB)Hashtable 262,234 26.5
WeakHashMap 19,562 12.6HashMap 10,600 2.3ArrayList 9,530 0.3HashSet 1,551 1.0Vector 1,271 0.04
LinkedList 1,148 0.1TreeMap 299 0.03TOTAL 306,195 42.9
Memory Analysis
§ Java case study: PlantsByWebSphere sample in WebSphere AppServer 7 - 5 user test load, 206MB of “live data”:
§ 42.9MB (16% of memory) are collection objects - Just the collections themselves - excluding any data they store
| @Chris__Bailey
Collection Count Collection Size (MB)Hashtable 262,234 26.5
WeakHashMap 19,562 12.6HashMap 10,600 2.3ArrayList 9,530 0.3HashSet 1,551 1.0Vector 1,271 0.04
LinkedList 1,148 0.1TreeMap 299 0.03TOTAL 306,195 42.9
Memory Analysis
§ Analysis of just the Hashtable instances showed:
| @Chris__Bailey
Memory Analysis
§ Analysis of just the Hashtable instances showed:
§ 127,016 of the Hashtables were completely empty
| @Chris__Bailey
Memory Analysis
§ Looking at empty collections across all collection types:
§ 52.6% of collections are empty (22.6MB)
§ Could save 11% of heap utilisation by removing empty collections
| @Chris__Bailey
Collection Count Empty % Empty
Hashtable 262,234 127,016 48.8
WeakHashMap 19,562 19,456 99.5
HashMap 10,600 7,599 71.7
ArrayList 9,530 4,588 48.1
HashSet 1,551 866 55.8
Vector 1,271 622 48.9
TOTAL 304,748 160,156 52.6
Memory Analysis
§ Looking at empty collections across all collection types:
§ 52.6% of collections are empty (22.6MB)
| @Chris__Bailey
Collection Count Empty % Empty
Hashtable 262,234 127,016 48.8
WeakHashMap 19,562 19,456 99.5
HashMap 10,600 7,599 71.7
ArrayList 9,530 4,588 48.1
HashSet 1,551 866 55.8
Vector 1,271 622 48.9
TOTAL 304,748 160,156 52.6
Optimising for the Cloud
Optimising for the Cloud#5: Design for Memory Usage
Design for Memory
§ Practice lazy allocation of collections
| @Chris__Bailey
Design for Memory
§ Practice lazy allocation of collections
§ Don’t use default collection sizes
| @Chris__Bailey
Design for Memory
§ Practice lazy allocation of collections
§ Don’t use default collection sizes
§ Reallocate collections to shrink them if needed
| @Chris__Bailey
Design for Memory
§ Practice lazy allocation of collections
§ Don’t use default collection sizes
§ Reallocate collections to shrink them if needed
§ Use cache miss based strategies for cache sizes
| @Chris__Bailey
Summary
Cloud Economics
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