past experiences and future challenges using automatic performance modelling to complement testing

Past Experiences and Future Challenges using Automatic Performance Modelling to

Complement Testing

Paul Brebner, CTO

A NICTA/Data61/CSIRO Spin-out Company

16/03/2016 © Performance Assurance Pty Ltd 1

Performance modelling background

• My background is analysis of distributed systems, middleware, GRID, architecture, performance, benchmarking (e.g. SPECjAppServer), sensor web performance, etc

• Since 2007 project in NICTA to develop tools to assist mostly government systems of systems to perform better in advance

• Service Oriented Performance Modelling tool• Model driven (SOA performance meta model)

• GUI

• Simulation for metric prediction

• Enables modelling at level of workloads, composite and simple services, servers.

• Used during early, middle, later lifecycle for lots of real systems

16/03/2016 Performance Assurance Pty Ltd 2

Performance modelling background

• BUT Manual model building (structure, parameterisation, calibration) is• Time consuming

• Expensive

• Error prone

• Limited to model complexity that can be built manually

• Not easily repeatable or maintainable

• Not accurate enough for some problems (need high quality and quantity of performance data)

• Not fast enough for agile development

• Last 3 years we have been a start up company, have to make $$$$$$• Most customers have APM products

• All customers want to increase speed and number of releases, reduce time and costs of testing

• Solution is to use automatic model building from APM data• Cheaper and faster and more accurate

• Solves new problems, e.g. DevOps


Automatic performance modelling from APM data

• Only use available APM data

• Use automatable (or potentially automatable) ways of getting the data from the APM into our Service Oriented Performance Modelling (SOPM) modelling/simulation tool (SaaS)

• Automatically build and parameterise the performance data from the APM data

• Multiple model types with various trade-offs, accuracy for capacity/response times, and model complexity/ability to change model aspects• Currently different model types are produced as part of the APM ->

modelling tool transformation phase


Application

DynatraceSF

Dynatrace

SF

PurePathDash

Browser

PP XML Converter

ModelXML

ModellingSaaS

1

23

4

5

SF Dynatrace Session File

PP XML

Dynatrace Server REST API PurePath XML File

ModelXML XML Model File

KEY

Dynatrace Transaction flow dashboard


Produces: Simple capacity model


Dynatrace PurePath Dashboard (detailed per transaction call tree)


Produces: Transactional model (portion)


Experiences with three projects

• Project 1• P2V migration

• Project 2• C2V test -> prod

• Project 3• DevOps

• Focus of this talk, come to main ICPE talk for others


Project 3

• Devops• Focus on response time SLAs

• Deployment/resources

• Faster cycle time

• More releases

• Less and cheaper testing

• Challenge• Proprietary in-house APM tool

• “Profile point” times only

• Required pre-processing (using Hive)


Focus

• Risk service• Heavily used

• Multiple services

• New services added all the time

• Services had different time and memory profiles

• Would a new service break the SLA?

• Baseline model accurate to 10% response time


Alternatives modelled

• Changing transaction mix

• Changing arrival rates

• Making some services asynchronous, concurrent

• Adding new risk assessment services

• More complex• Optimising deployment of services to multiple servers taking into account

memory and CPU usage, and response time

• A type of box/bin packing problem

• 4 services out of 30 used 50% of CPU


Challenges

• Pre-processing APM data “profile points”

• Low load for APM data sample c.f. target load• Used calibration from load tests on pre-production to improve accuracy

• No CPU time breakdown from APM data• But GC had a profile point (and was significant)

• Transaction types not in APM data• Had to infer them, either too few or too many


DevOps

• Goal is to shift left and shift right• Shift right

• Build and continuously maintain performance model of production to accurately model response times, scalability, capacity and resource requirements under target production loads

• Shift left• Calibrate production performance model for development

• Enable developers to make code changes, explore impact with unit tests and development APM to incrementally rebuild performance models

• To understand likely performance and scalability impact

• Speed up development cycle as no longer have to wait (weeks) for performance testing


Existing Dev, Test, Prod lifecycle: Delays in feedback: Takes weeks per iteration, test env is a bottleneck, environments are different

Dev Test Prod

Late Feedback Late Feedback

Deploy to test Deploy to prod

DevOps + APM: earlier but not completely accurate performance feedbacki.e. environments are different so APM data is different across lifecycle

Dev Test Prod

Late Feedback

Deploy to test Deploy to prod

APM APM APM

Earlier Feedback

DevOps + APM + Modelling: Earlier more accurate performance predictions -> decreased cycle time


Dev Test ProdDeploy to test Deploy to prod

APM APM APM

Early Feedback

Base Model

Dev Model

Incremental updates toBase model with dev changes

Baseline model buildDev Model Update

Calibrate prod model for dev

Benefits

• Changes in code in Dev• Unit test

• APM performance data

• Incrementally update calibrated performance model

• Predict performance and scalability impact for Prod env

• Cheaper and faster than waiting for testing and deployment to Prod

• Sensitivity analysis could determine areas of greater sensitivity to changes and thresholds• These would be subject to more rigorous modelling and testing


DevOps + APM + Modelling: In reality lots of dev, different environments


Dev Test

ProdDeploy to prod

APM APM APM

Base Model

Dev Model

Baseline model build

Dev

APM

Dev Model

Dev

APM

Dev Model

Dev

APM

Dev Model

Challenges

• Calibration of performance models for use in Dev from Test and Prod

• Once predictions are made how do we test if they are supported by the APM data or not? i.e. if null hypothesis is “changes in dev will have no impact on prod”, how do we determine if this is supported by evidence or not?

• Is it scalable?• Lots of developers and changes to subsets of code

• Concurrent and compounding changes would need centralised model with all changes incorporated

• What about changes to infrastructure code that could impact everything?

• How to support this in Dev APM and modelling tools

• ROI• Depending on cost of testing, cost of initial setting up APM and modelling tools and

incremental costs, number of tests and modelling predictions per cycle, and value of reduced cycle times and earlier performance predictions, ROI may occur earlier or later or never…

• Example• Assumes model calibrated once per release from performance APM data

• Assumes one actual load test per release

• What’s tradeoff between multiple tests per release vs 1 test and multiple modelling predictions?


Costs: Modelling cheaper after 3 changes


0

5000

10000

15000

20000

25000

30000

35000

40000

45000

0 2 4 6 8 10 12

Co

st (

$)

Number of changes tested/modelled

Costs of LoadTest only and HybridApproach

LoadTestOnly Modelling

Speed: Average hours to test/model a number of code changes (per model calibration)


0

10

20

30

40

50

60

70

80

90

0 2 4 6 8 10 12

Ave

rage

tim

e (h

ou

rs)

Number of changes per calibrated model

Average hours to test/model changes

LoadTestAvgHourPerChange ModellingAvgHoursPerChange

Send us your data

• Free trial of simple Dynatrace capacity models

• http://www.performance-assurance.com.au/send-us-your-data/

• http://www.performance-assurance.com.au/introduction-to-automatic-model-building/

• Send us a sample Dyntrace session file and we’ll send you a link to a demo capacity model

• Particularly interested in trending technologies and use cases, e.g. Micro-services, Containers, Big Data, IoT, etc

• Free Personal Dynatrace license from: http://bit.ly/dtpersonal


http://www.performance-assurance.com.au/send-us-your-data/

http://www.performance-assurance.com.au/introduction-to-automatic-model-building/

http://bit.ly/dtpersonal

past experiences and future challenges using automatic performance modelling to complement testing

Technology