Intelligent DevOps - Driving Continuous Delivery by Harnessing the Power of Analytics and Autonomics

World®
’16
Pre-Con Education: Intelligent DevOps –
Driving Continuous Delivery by Harnessing
the Power of Analytics and Autonomics
Shamim Ahmed - Sr. Director Pre Sales - CA Technologies
DO5X48E
DEVOPS

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Abstract
Digital technologies are rapidly transforming businesses. These systems need to be extremely
responsive to fickle customer sentiment, work across a variety of devices (from laptops to
wearables) and network conditions. Such scenarios put even more pressure on IT systems and
processes to be not only more responsive, but adaptive to meet digital business needs. While
automation solutions within current DevOps implementations help to address agility need, such
automation is typically driven by static rules using conventional scripting and orchestration
techniques.
Such techniques incur high maintenance overheads to keep them updated relative to changing
circumstances. The recent emergence of predictive analytics and cognitive technologies (such as
autonomics) have opened the possibility to drive adaptive automation within Continuous
Delivery implementations. Such automation is able to self-heal and self-configure based on
changing situations.
Join this session and learn how CA is helping to power the next generation of such intelligent
DevOps implementations.
Shamim
Ahmed
CA Technologies
Sr. Director PreSales

DIGITAL TRANSFORMATION IMPACT
CHALLENGES FOR DEVOPS
SOLUTION FOUNDATIONS: ANALYTICS AND AUTONOMICS
WHAT INTELLIGENT DEVOPS LOOKS LIKE
CA SOLUTIONS FOR INTELLIGENT DEVOPS
Agenda
1
2
3
4
5

Digital Transformation and its Impact

AUTONOMICS GENOMICS
ROBOTICS
INTERNET OF THINGS AV/VR
OMNI CHANNEL
Digital Transformation

The Digital Ecosystem
"The explosion of data
sources and complexity
of information makes
manual classification
and analysis infeasible
and uneconomical"
- Gartner top 10 Strategic Technology Trends for 2016

Intelligent Analytics is the New Battleground
> 50%
large organizations
globally will compete
using advanced analytics
& proprietary algorithms
40%
enterprises' net-new
analytics investment will go
to predictive / prescriptive
75%
technology-oriented business
intelligence competency
centers will evolve to focus
on information value
generation
Source: Gartner

How Enterprises are Transforming
AGILE ADAPTIVE COGNITIVE

Challenges Posed for DevOps

Key Challenges Posed by Adaptive Digital Systems
AUTOMATION
MAINTENANCE
STATIC RULES BIG (un-structured) DATA
IOT, IOE
CX SECURITY
STRUCTURED
AUTOMATION

What we need for next-gen DevOps
AUTOMATED & SELF-
HEALING AUTOMATION
DYNAMIC/
ADAPTIVE RULES
BIG DATA ANALYTICS
IOT-Ops
CX-Ops SEC-Ops
INTELIGENT/ADAPTIVE/
COGNITIVE AUTOMATION
+ +
+
+
+
+
+

Classic DevOps techniques do not fully exploit data on
either side of the “release boundary”
People
Process
Tools
Data

The Key Transformations Needed
TRADITIONAL
DEVOPS
INTELLIGENT
DEVOPS
Focus on automation,
efficiencies, metrics,
speed, quality
Focus on CX, adaptive
automation, predictive
outcomes and insight

CONTINUOUS DELIVERY MATURITY
Manual
Scripted
Orchestrated
Continuous
Intelligent
BUSINESS VALUE
LEVEL 1 LEVEL 2 LEVEL 3 LEVEL 4 LEVEL 5
?
Organization
depth and breadth of
agile practice
*Application release automation view
Continuous
Optimization
End-to-End
Connection
Consistency &
Collaboration
Heroes &
Heavy Lifting
Intelligent DevOps Extends the Maturity Journey
MATURITY

Intelligent DevOps is part of the “Third Way”

System of Intelligence for DevOps
Foundation for Intelligent DevOps
DevOps Insight/Analytics
DevOps Autonomics

Solution Foundations: Analytics and
Autonomics Overview

Systems of
Intelligence
SOI
Visualization
(Big) Data
Analytics
Intelligent
Process
Automation
Digital
Technology
Integrations

Analytics Overview
What is happening?
Why is this happening?
What will happen and why?
What should we do?
Are we asking the right questions?
Predictive
analytics
Descriptive
Analytics
Diagnostic
Analytics
Prescriptive
Analytics
Deep Learning

Examples of Analytics Use cases
What are the past defect trends?
What were the root cause
of the past defects?
What defects are we likely to find?
What preventive measures
should we take?
Have we overlooked anything for
defect prevention?
Predictive
analytics
Descriptive
Analytics
Diagnostic
Analytics
Prescriptive
Analytics
Deep Learning

Autonomics
Overview
DO THINK LEARN
Structured Data Un-Structured Data

Examples of Autonomics Use Cases
DO THINK LEARN
Scripting Orchestration Autonomic Cognitive
• Self-healing virtual
services
• Self-healing
tests/suites
• Self-healing test data
• Self-healing
topologies
• Autonomous release
decision making
• Build automation
• Test Automation
scripting
• Environment
provisioning scripting
• Deployment
automation scripting
• Builds of builds
• Model-based
topology deployment
• E2E Continuous
Delivery
• Automated test
automation
• NLP-based test
reverse engineering
• Dynamic build
promotion

How Analytics and Autonomics work together
Model Progressive Refinement

What Does Next-Gen Intelligent DevOps
Look Like?

Intelligent DevOps Characteristics
§ Failure detection and prevention
§ Scripted automation
§ Limited process automation based on static rules
§ Individual metrics based on structured data in
lifecycle silo
§ Limited insight and perspectives
§ Reactive: deals with “Past”
§ Failure prediction
§ Automated automation
§ Intelligent process automation based on dynamic
inference
§ Analytics based on large scale structured and
unstructured data across the lifecycle
§ Focus on Insights
§ Predictive: from “What did happen?” to “What would
happen?
TRADITIONAL
DEVOPS
INTELLIGENT
DEVOPS

Portfolio backlog
prioritization and
value analysis
Improved
scheduling and
capacity
Validated
requirements in
backlog
Improved
estimates
Predict failures
based on code
commits
Dynamic code
promotion
criteria
Predict
defects/failures
Optimized Tests
Scenario
identification
based on
trends
CX Scores
Anomaly
detection
Better load
balancing
Examples of DevOps Intelligence
PLAN DEFINE DEVELOP TEST RELEASE OPERATEProcess
Intelligent DevOps based on Continuous Insight
Examples of
DevOps
Intelligence
Examples of
Outcomes
Investment
decision
analytics
Code
quality
analytics
Test
Optimization
& Defect
prediction
A/B
Testing
Log & CX
Analytics
CX-based
requirements

Define Intelligence Example
OMNI-CHANNEL
FEEDBACK INGESTION
FEATURE
REQUESTS
HATED
FEATURES
USABILITY NPS
Req
Validation
UX Fixes Remediation
BIG DATA
PROCESSING
INSIGHTS
SOLUTIONS
A/B Test
Scenarios
SOI

Develop Intelligence Example
Code
Coverage data
Code Quality data
Build data Unit test data
SOI SCM data
§ Failure mode analytics
§ Quality risk prediction
§ Promotion decision analytics
§ Team performance analytics

SOI
§ Identify missing test cases
§ Performance scenarios
§ Test data
§ Defect/schedule prediction
§ App stability prediction
§ Test optimization
§ Fix rate prediction
§ Root case prediction
§ Self-healing tests/test data
§ Self-healing virtual services
Test Intelligence Example
Test execution data
Defect data
Test schedule data
Test environment data
Production log data

SOI
§ Correlating data
(requirements thru release)
across lifecycle
§ Deployment failure
prediction
§ Self-healing
topologies/workflows
§ Deployment workflow
optimization
§ A/B scenario identification
Release/Deploy Intelligence Example
Test execution data
Build data
Topology data
Release payload
and schedule data
Deployment data

SOI
§ Capacity allocation
§ Anomaly detection
§ Automated event correlation
§ CX/NPS scores
§ Test scenarios/data
§ Mobile analytics
Ops Intelligence Example
Log data Ticket data
CX Data Incident data
Monitoring data

How We Can do Better X-Lifecycle Intelligence: Data Hub Integration
Dev Hub Test Hub Ops Hub
DEPLOYMENT HUB

Example of Development Hub
IDE
Build
Code Test
Testing
Backlog
CI
SCM
Data
Virtualization
Test Data
Topology
Deployment

Why we Need a Hub
Integrated
Orchestration:
Focus of
Control
Integrated
Change
management:
Understand
Impact
Scale:
Support Agile
at Scale
Integrated
Reporting:
One Source
of Truth
Integrated
Analytics:
Intelligent
Insight

Beyond Hubs – DevOps Data Lake
PLAN DEVELOP TEST RELEASE OPERATEPROCESS
X-LIFECYLE INSIGHT
DATA HUBS
LIFECYCLE
DATA LAKE

Example of X-Lifecycle Intelligence: Continuous Adaptive Test
Optimization
Existing Manual Test
Cases
Statically Optimized Test
Cases
Optimizer
with NLP
Unstructured
Partial coverage
Test bloat
Low effectiveness
Analog –
difficult to maintain
Dynamically -Optimized
Test Cases based on
Continuous Insight
New Requirements and
Biz Processes
Fewest test steps
(reduce by > 30%)
Greater coverage
Higher effectiveness
Fully digitized –
easy to maintain
Continuous optimization
Highest effectiveness
Reduction > 50%
PLAN OPSDEV RELEASE

CA Solutions for Intelligent DevOps

CA Solutions Across the DevOps Lifecycle
Define Develop Build DIT QA Release OperatePlan
Process
Stage
CA APIM
CA DevTest (SV + AppTest + CAI), Blazemeter
CA TDM
CA ARD
CA RA/CDE
CA Unified
Monitoring
CA ITSM
CA PPM, Agile Central

Analytics as Part of CA’s DNA
REAL TIME, BIG DATA ANALYTICS EMBEDDED THROUGHOUT OUR PORTFOLIO
AGILE MANAGEMENT AGILE OPERATIONS DEVELOPER PRODUCTS
CONTINUOUS DELIVERY MAINFRAME SECURITY
API analyticsRelease management analytics Access incident responseIntelligent Operations

CA Analytics is a Common Technology Component
embedded in our products
Best of Breed Open Source
Technologies
(Hadoop, Spark, Kafka, Elasticsearch)
Industry Proven Architecture
(Lambda & Micro Services)
Single Code Line, Multiple
Deployment Options
(Cloud, On Prem, Mainframe)
Encapsulated Complex
Technology through Simple
Consumable Interfaces
(RESTful APIs)
CA Analytics

Jarvis: CA Reference Architecture for SOI
DATA COLLECTORS
Data Aggregator
Agents
…
Probes
Machine/Log File Data
DATA-DRIVEN APPS
Existing CA Apps
Custom-build Apps
New CA Apps
Partner Apps
ANALYTICS ENGINE
Data Injection API
Data Access API
Service Layer
Data Extraction
Data Processing (Speed)
Data Processing (Batch)
Data Bus
Session IET03T: “Analytics at CA – What’s Cooking? Project Jarvis”
Wednesday, November 16: 04:30 PM - 05:15 PMCA Accelerator Zone

§ Real time advisor for
code delivery
§ Architectural modeling
§ Risk reporting
§ Team optimizer
CA Develop Intelligence: CodePilot.ai
Code
Deep machine learning
Neural map of code
Session IET07TV: “Unlock Developer Productivity with CodePilot.ai”
Thursday, November 17: 04:30 PM - 05:15 PM, CA Accelerator Zone

CA Apps Hub using
Agile Central
IDE
Build
Code
Test
Testing
Backlog
CI
SCM
Data
Virtualiz
ation
Test
Data
Topolog
y
Deployment
Agile Central
Studio
Visual
Eclipse
IntelliJ
Electric
Cloud
Cloud
Bees
Junit
Nunit
TestNG CA AppTest
ALM, UFT
CA
TDM
CA SV
CA
Rally
CA
RA
CA
RA

Plan Define Unit test Code Check in Build Provision Test Release
Scaling Agile Development with a CA Enterprise Apps Hub
Integrated analytics
Decision engine
IDE BuildUnit test
Agile Pod
2
SCM
Unit test Build
Agile Pod
N
IDE SCM
Unit test IDE Build
Agile Pod
1
SCM
Integrated to
CA AC Hub
IntelliJ
…
Agile Central

CA Test Intelligence: Learning Virtual Service
BRINGS TRUE ARTIFICIAL INTELLIGENCE (AI) TO
SERVICE VIRTUALIZATION
§Virtualize services without requiring any knowledge or
decoding of the service protocols
§Applies a genome sequence alignment algorithm,
discovers byte-level patterns in message protocols
§Now virtualize a much wider range of protocols without
requiring a new DPH
HIGH ACCURACY: 99.6 – 100%
§Increased speed and accuracy with Entropy Weighting +
Message Clustering
§The more data a service observes the data, more
intelligent it becomes
§Perfect for performance testing where we deal with tons
of data

CA Test Intelligence: Self-Healing Virtual Service
§ Automatically learn
the differences
between test and
live environments
§ Self-heal as needed

CA Test Intelligence: Reverse Engineering of Legacy Tests
Existing Manual
Test Cases
Statically Optimized
Test Cases
CA Agile Req. Designer
(NLP)
Optimized coverage paths
Identify test data coverage
Session D05X275: “What's New in CA Agile Requirements Designer?”
Thursday, November 17: 12:45 PM – 1:30 PM, Lagoon L (L2)

CA Application Insight for Actionable Intelligence
AUTOMATE THE AUTOMATION
Mine
Defects in Live
App
Automate
Test
Creation
Automate Virtual
Service
Creation
Provision Configure Deploy Test
Environment
App, MW, DB
Build/CIDevelop
Dev Test Prod
Environments

Numerous, uncoordinated tools and technologies along the SDLC
CA Release/Deployment Hub with CA RA+CDE
CA Release Automation Deployment Hub
125
Out-of-box
integrations work
seamlessly with any
environment
Community
For integration info
& community-
supported action
packs
RDK
(Rapid Development
Kit) to adapt to new
tools & technology
1800
Actions to
orchestrate the
entire release
pipeline
Infrastructure
Provisioning
Configuration
Management
Application
Platforms / PaaS
vSphere
Version Control
/ Artifact
Repository
Planning &
Requirements
Change
Management
Test
Automation
DatabasesCI/Build
Automation

Scaling Enterprise Deployment across apps
Release A
Release B
Release C
Manage and monitor multi-application content through the release cycle
CA CDE Deployment Hub
Feature 1
Story 2
Feature 3
Feature 4
Feature n
§ Release and track features
independently or grouped
based on business
requirements
§ Map customer feedback to
user stories, features and
bug fixes moving through
the release pipeline
§ Gain visibility of and
reconcile dependencies

Analytics
Scorecard in
CA Release
Automation

CA App Experience Analytics
USER BEHAVIOR & EXPERIENCE
Outside-In View
OPERATIONAL PERFORMANCE
Inside-Out View
CA App Experience Analytics
Optimize
customer journey
Design
for the experience
Improve
digital performance

Mobile
Analytics
CA App Experience Analytics Key Capabilities
User Experience Analytics
across web, mobile, wearables
Performance
Analytics
Data Studio
& Blueprints
Flexible Dynamic
Dashboards
§ Crash Analytics
§ User Session Playback
§ App Flows
§ Heat Maps
SaaS & on
premise
end-to-end
encryption,
flexible offerings

Example: CNN Politics App – Powered by CA Technologies
CA Mobile App Analytics will enable the CNN Politics app with –
§ Visibility into real-time usage and app behavior
§ Proactive alerting
§ Symbolication – Shows reason for crash including line number and
class/method without need to troubleshoot
§ Capture user feedback, session replay and real time app performance
analytics
CA API Management tools will enable the CNN Politics app with –
§ Allowing developers to easily and quickly build APIs using CA Live API
Creator to multiple polling and election funding data sets
§ Helping CNN streamline the processing of the various election related
data streams into usable content by orchestrating CNN’s use of AWS
Lambda capabilities using CA Mobile API Gateway
§ Managing and securing the data APIs as well as other functional APIs
using CA Mobile API Gateway, for consumption by various users
globally

Questions?

Recommended Sessions
SESSION # TITLE DATE/TIME
DO5T01S
Keynote: End-to-End Integrated Continuous Delivery
Ecosystem: How to Drive Innovation and Differentiation
11/16/2016 at 10:30 am
DO5T02P
Leadership Panel: DevOps Leaders Discuss the Journey to
Continuous Delivery
11/16/2016 at 01:45 pm
DO5T14S
Analyst Keynote: Continuous Delivery: Making DevOps
Awesome
11/17/2016 at 10:30 am

Must See Demos
Modernize
App Delivery
Integrated CD
Theater 5 - DOV501P
Deliver Test
Data Faster
Test Data Manager
Theater 5 - DOV513P
Deliver
Better Apps
Service Virtualization
Theater 5 - DOV510P
Orchestrate
Your Release
Release Automation
Theater 5 - DOV512P

Thank you.
Stay connected at communities.ca.com

@CAWORLD #CAWORLD © 2016 CA. All RIGHTS RESERVED.60 @CAWORLD #CAWORLD
DevOps – Continuous Delivery
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Intelligent DevOps - Driving Continuous Delivery by Harnessing the Power of Analytics and Autonomics

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