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Application Modernization in the Cloud Age
Tom Fisher
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Ability to bring new application functionality online in real time
Bring new initiatives on line quickly
Adjust rapidly to changes in the competitive landscape
BusinessAgility
Responsiveness Cost EffectivenessScalability
Application ModernizationWhy it’s Occurring and is Important
Immediately respond to user concerns
Rapidly diagnose and remediate complex issues
Maintain SLI, SLO, and SLA goals
Scale-up instantly as application demand peaks
Scale-down as application demand ebbs
Add and use resource credits based upon application scaling requirements
Reduce or eliminate application downtime by applying on-demand resources
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3
The Evolution of Application
Architectures
● Monolithic
● 1948-1997
● SOA
● 1998-2011
● VMs
● 1972 – IBM
● 1999 – VMware
● Microservices
● 2011-
● Waterfall Design
● 1970
● Agile
● 2000
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4
Why Application Architectures Evolved
● Mobile Internet
● Faster and more reliable backbone
networks
● Highly distributed services became
much more viable
● Compiled to JIT VM code
● Smalltalk ~ 80s
● First JVM – 1994
● Faster processors, multi-core,
denser storage, etc.
Mobile Network Average Speed Peak Speed
2G 0.1Mbps 0.3Mbps
3G 3Mbps 7.2Mbps
3G (HSPA+) 6Mbps 42Mbps
4G LTE 20Mbps 150Mbps
4G LTE Advanced 42Mbps 1Gbps
5G 500-700Mbps 10 or 20Gbps
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5
The 5 most common Application Modernization Options
RehostMove applications to cloud Infrastructure as a Service (IaaS) without altering their architecture
Refactor Change the application code to fit a Platform-as-a-Service (PaaS) model
Rearchitect Modify or extend the existing application code to become cloud-native
RebuildRebuild application on PaaS, remove code for the existing platform, and
rearchitect to take full advantage of cloud-native features
ReplaceReplace existing application with a commercial Software-as-a-Service (SaaS)
application
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6
Where Enterprise Observability Helps Application Modernization
Application
Modernization
Option
Cloud Service Type Cloud Configuration Type
IaaS PaaS SaaS Single Cloud Multi-Cloud Hybrid-Cloud
Rehost
Refactor
Rearchitect
Rebuild
Replace
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
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7
The CI/CD Pipeline
CI/CD Tools
Enable automation and monitoring
for apps dev, integration and testing
to deployment
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Discover and address 'unknown unknowns’
Issues you don't know exist
Catch and resolve issues early in development
Automatically scale observability
Enable automated remediation and self-diagnosing application infrastructure
Observability’s Role in CI/CD
Pipeline Optimization Shift-Left Observability
For Building Better Software Faster by
Optimizing Unit Test and Auto Test
And of Course, for Production
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Optimizing the CI/CD Pipeline
● Unit Test Values
● Automated Profiling provides code level
details for triage
● Auto Test (and Production) Values
● Automation
● Discovers/maps apps, services, infrastructures, events, and dependencies
● Context
● Ingests all observability metrics, traces each request, profiles every process and
updates dependency maps in real time
● Intelligent Action
● Machine Learning Analytics for optimizing application performance
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Key Enterprise Observability Capabilities
for
Application Modernization
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Automation
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Automatic Profiling
● Automatic and continuous code level profiling
● JVM, PHP, NodeJS, etc. tracers
● Profiler Sensor from the Agent
● Always on, but not profiling all the time
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Enterprise Observability Automation
ImmediateAutomated tech stack discovery3 secs for contextual understandingImmediate situation info for DevOps
ExactEVERY request is a Distributed TraceOrganize data into context Automatic exact root cause for any issue
Effortless
Automatic:- Metrics and traces collection- Data storage and organization- Analytics or dashboards building- Code Profiling
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Context Guide
Dynamic Graph for rapid troubleshooting.
A “GPS” for enterprise applications.
Dynamic GraphContinuously updated, full stack, internal data model of application structure and dependencies
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Context
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Application Dependency Maps
For Each Application
• Application service
dependencies
• Calls between services
• Application architecture layout
view
• Dashboards, flows, calls and
issues service views
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Golden Signals for All Services● Application Perspectives
● Latency
● Traffic
● Errors
● Saturation
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Error and Log Messages
● Error messages
● Service errors that happen during code execution
● Log Messages
● Collected from a log message with severity WARN or higher
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Intelligent Action
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Machine Learning
● Signals Instana trains on
● Call Rate (sudden drops)
● Error Rate (sudden increase)
● Latency (sudden increase)
● Signals tracked from a variety of sources
● Traces
● Endpoint, services, app perspectives
● Metrics
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Unbounded Analytics
Distributed Trace Analytics
Finds EVERY slow request ● Unbounded Analytics focuses on
● Distributed Traces
● Logs
● End User Monitoring
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Smart Alerts
22
● Use Case Based Alerting
● Alert suggestions and recommendations
● Performance, Availability, Errors, Bugs
● Automated and manageable alerts
● Customizable Scenarios, Real Time visualization, Seasonality
● Arbitrary filtering
● Scope limitations, Traffic narrowing
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Instana
is
Enterprise Observability
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Instana Values for Key Stakeholders
24
● Developers
● Test new code functionality before committing
● DevOps
● Enable smooth CI/CD pipeline integration
● SREs
● Ensure pre and in-production reliability and availability
● Ops
● Continuously monitor and respond to potential problems and alerts generated by Machine Learning and AIOps
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Application Modernization in the Cloud Age
Tom Fisher
Thank You!