Next-Gen Legacy Modernization- GenAI, Kubernetes, and Google Cloud in Action by Saurabh Mishrapdf

A workshop conducted by GDG on Campus KARE.

1. C2 General
Next-Gen Legacy Modernization-
Gen AI, Kubernetes, and Google
Cloud in Action
Udaipur

2. C2 General
Hello!
I am Saurabh Mishra

▪DevOps Lead working with TSYS (Global Payments)
▪Got Bachelors, degrees in Information Technology
▪GDE- GCP and Champion Innovator
▪DevOps Institute Ambassador and Organizer
▪AWS CB||Calico Big Cats ||CDF AMB|| open-appsec AMB

Feel free to follow me at:
▪LinkedIn (www.linkedin.com/connectsaurabhmishra)
▪Medium (www.medium.com/@connectsaurabhmishra)

3. C2 General
Questions are
welcome at any
time
Please silence
your phone
There are
NO STUPID
Questions
Keep the session
INTERACTIVE
Share experiences, stories and build
sustainable working practices
Most “stupid” ones are the
most welcome
Observability | Rules
3

4. C2 General
Observability | Agenda
01
02
03
04
05
06
Benefits and Challenges

Key Considerations & Best Practices

GCP Offered Services & AI Tools

Modernization Strategies

Why Modernized Legacy Apps

Understanding App Modernization

5. C2 General
2000
Introduction of Virtualization


2006
Cloud Computing


2008
DevOps


2014
Kubernetes Open-Sourced
2022
ChatGPT Launched


2023
CNCF k8sGPT Project

.
Application Modernization | Timeline

6. C2 General
2025-26
Expansion of AI on Kubernetes
Adoption of Kubernetes-native AI
tools like Kubeflow, KServe, and Ray
increases.
More AI/ML workloads shift to
hybrid and multi-cloud setups
powered by Kubernetes.
Security challenges grow with AI
workloads, leading to better
Kubernetes-native security solutions
2027-28
AI Infrastructure Evolution
AI models get more
compute-intensive, leading to
improved Kubernetes support for
heterogeneous hardware (GPUs,
TPUs, FPGAs).
AI-driven Kubernetes management
– AI begins optimizing Kubernetes
clusters (self-healing, self-scaling).
Standardized AI pipelines emerge,
making AI deployments more
seamless.
2029-30
Autonomous Kubernetes & AI
Convergence
AI-powered self-managing
Kubernetes clusters become
mainstream.
Decentralized AI models running on
Kubernetes at the edge power
next-gen IoT, AR/VR, and autonomous
systems.
AI-enhanced DevOps & SecOps –
AI-driven security automation and
governance inside Kubernetes
environments.
Application Modernization | Why we talking about Kubernetes & AI

7. C2 General



7
Application Modernization | Definition

8. C2 General
Application Modernization | Definition

Process of refactoring
an organization’s
legacy apps to cloud
native approach
.
Migrating from Monolithic
to Microservice
architecture
Updating
outdated business
systems and
applications

9. C2 General
Application Modernization | Why Modernize Legacy’s app

9
•Legacy applications are also often monolithic applications.
•Monolithic apps are difficult to update due to shared and rigid scaling.
Because all of an application’s components having shared pipelines
together, it is difficult and costly to add latest's features which leads to
complexity and other challenges.
•By modernizing an application to a microservices ( rearchitecting,
rebuilding or replacing) in which each component is smaller, loosely
coupled, and can be deployed and scaled independently of one another
efficiently.

10. C2 General
Application Modernization | Monolithic v Microservice
MONOLITHIC
03
01
04
02
MICROSERVICE
03
01
04
02
Deployment
Single codebase and deployment unit
Scalability
Scales as a whole; resource-intensive
Development
Slower due to code coupling and
dependencies
Complexity
Simpler to develop and deploy initially
Deployment
Independent deployment of services.
Scalability
Scales independently; better resource
utilization
Development
Faster with independent development
teams
Complexity
Higher operational complexity

11. C2 General
Application Modernization |Monolithic vs Microservice
11
Google Source Image

12. C2 General

12
Application Modernization | Pillars

13. C2 General
Application Modernization | Pillars
Well Defined
Process
Architecture &
Cloud Native
Technology
.
DevOps Culture &
Automation
Digital
Transformation

14. C2 General
Application Modernization | Strategies
Rearchitecting
With rearchitecting, apps functionality and
code get modified and extended to scale
better in the cloud
Rebuilding
This strategy involves completely rewriting
the application from scratch using modern
technologies and cloud-native principles
Replatforming
This strategy involves making some changes
to the application code and architecture to
take advantage of the cloud platform's
capabilities.
Refactoring
This strategy involves making significant
changes to the application's code and
architecture to improve its design and
maintainability.
.
Retiring
Discontinuing the use of an application,
often replaced by a new solution or
functionality
Rehosting (Lift and
Shift)
This strategy involves moving legacy
applications to a cloud platform without
making significant changes to the code or
architecture..

15. C2 General
Application Modernization |Phases
Operations and Maintenance
.
Assess and Planning



Architecting and modernize
applications

16. C2 General
DEVOPS & CI/CD
Cloud Build, Artifact Registry, Cloud Deploy
Application Modernization |GCP Offered Services
API MANAGEMENT
APIGEE, Pub/Sub
SECURITY &
COMPLIANCE
Cloud Armor, Identity-Aware Proxy
COMPUTE
Cloud Run, GKE, Compute Engine, App
Engine, Cloud Function
DATA
MODERNIZATION
Cloud SQL, Cloud Spanner, Firestore,
BigQuery

17. C2 General
Application Modernization | Predective v Gen V Multi-Modality Gen AI
.

18. C2 General
Application Modernization |Phases
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19. C2 General
Application Modernization | Google Cloud AI Ecosystem
Predictive AI

• Purpose: Forecasts outcomes based on historical data.
How it Works: Uses machine learning models (regression, classification, time-series
analysis) to predict future events or trends.

Examples:
•Credit scoring in finance
•Demand forecasting in supply chain
•Fraud detection in banking
•Predicting equipment failure in manufacturing

Limitations: Cannot generate new content; it only analyzes and predicts based on existing data.

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20. C2 General
Application Modernization | Google Cloud AI Ecosystem
Generative AI (GenAI)

•Purpose: Creates new content such as text, images, code, or audio.

How it Works: Uses deep learning models (Transformers, GANs, VAEs) to generate data similar
to its training set.

Examples:
•ChatGPT (text generation)
•DALL·E (image generation)
•Codex (code generation)
•MusicLM (music generation)

Limitations: Can hallucinate (generate incorrect but convincing content), may require large
computational resources.

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21. C2 General
Application Modernization | Google Cloud AI Ecosystem
Multimodal Generative AI

• Purpose: Combines multiple data types (text, image, audio, video) for generation and
understanding.
•
How it Works: Uses models that process and generate across multiple modalities, like
OpenAI’s GPT-4V (vision + text)
•
Examples:
•GPT-4V (understands text + images)
•Gemini (processes and generates text, images, and audio)
•Stable Diffusion with text-to-image features
•
Limitations: Higher complexity, requires extensive training datasets, and can be more
expensive.


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22. C2 General
Application Modernization | Google Cloud AI Ecosystem
Code Analysis and Service Decomposition

•Cloud AI and Vertex AI
Use AI models to analyze monolithic application codebase, identify tightly coupled components and suggest service boundaries.
Vertex AI can build custom models for code clustering and modularization.
•Recommendations AI
Provides suggestions for service decomposition based on usage patterns and dependencies

Dependency Mapping and Visualization

•Cloud Trace : Analyze dependencies and performance bottlenecks within application.
•Create a clear dependency graph of services and modules.
•BigQuery: Analyze logs and application data for usage patterns to define service boundaries.

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23. C2 General
Application Modernization | Google Cloud AI Ecosystem Contd.
Refactoring with AI Assistance

•Duet AI for Google Cloud IDEs:Use AI-powered code recommendations within Google Cloud-supported IDEs to
refactor monolithic code into microservices.
•Auto-generate boilerplate code for APIs, service interfaces, and configurations.
•Code Suggestions: Google’s AI coding tools can recommend breaking down business logic into domain-driven
microservices.

Data Migration and Partioning

•BigQuery ML:Identify and partition data schema for microservices by using machine learning models to analyze
database patterns.
•Cloud Spanner:Use AI insights to distribute data effectively across microservices while maintaining consistency and
scalability.

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24. C2 General
Application Modernization | Google Cloud AI Ecosystem Contd.
Testing and Automation

•AI-Driven CI/CD Pipelines with Cloud Build: Automate testing and deployment for individual microservices using AI for identifying
and prioritizing test cases.
•Error Reporting and Debugger: AI-powered tools to detect issues and debug during the transformation process.


Observability and Monitoring

•Operations Suite (formerly Stackdriver):AI-enhanced logging, monitoring, and tracing to ensure your microservices operate
efficiently post-transformation.
Use AI anomaly detection to identify and fix issues in real time.
•Cloud Monitoring with Vertex AI: Monitor traffic patterns and load distribution among microservices using predictive analytics.


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25. C2 General
Application Modernization | Google Cloud AI Ecosystem Contd.
Performance Automation

•Cloud Run with AI Load Balancing: Leverage AI-based traffic routing and scaling capabilities to optimize individual microservices.
•AutoML and Recommendations AI: Predict service load and resource requirements to optimize the deployment of microservices


Migration Tools in Google Cloud

•Application Modernization Platform: Offers tools like Anthos for containerizing and orchestrating microservices.
•Migrate for Compute Engine: AI-powered insights for modernizing existing workloads into containerized microservices.
•GKE Autopilot: Automatically manages scaling and resource allocation for your new microservices.


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26. C2 General
Application Modernization | AI Infused Moderation

API and Service Moderation
API Threat Detection : Leverage Apigee API
Management
Service Reliability:Use Anthos Service Mesh
Error Pattern Recognition

Cost Optimization Moderation
Cost Prediction Models:Use BigQuery ML to
forecast cloud spend based on historical data

Resource Optimization:AI recommendations
from Recommender

Security Moderation

Access Control: Implement IAM (Identity and
Access Management)
DDoS Protection: Use Cloud Armor
Network Security: Utilize VPC Service Controls
API Security: Apigee API Management

User Behavior Moderation
Behavioral Analytics: Google Analytics 360 and
BigQuery ML
Fraudulent Account Detection:AI models on
Vertex AI
.
Compliance and Governance
Moderation
Data Classification : Use DLP API (Data Loss
Prevention)
Policy Enforcement:Apply AI-powered policy
enforcement with Policy Intelligence.
Real-time Compliance Monitoring:Use Cloud
SCC
.
Content Moderation
Text Moderation: Cloud Natural Language API
Image Moderation: Cloud Vision API
Video Moderation: Cloud Video Intelligence
API
Custom Moderation Models: Build custom ML
models with Vertex AI

27. C2 General
Application Modernization | AI Infused Moderation

API and Service Moderation
API Threat Detection : Leverage Apigee API
Management
Service Reliability:Use Anthos Service Mesh
Error Pattern Recognition

Cost Optimization Moderation
Cost Prediction Models:Use BigQuery ML to
forecast cloud spend based on historical data

Resource Optimization:AI recommendations
from Recommender

Security Moderation

Access Control: Implement IAM (Identity and
Access Management)
DDoS Protection: Use Cloud Armor
Network Security: Utilize VPC Service Controls
API Security: Apigee API Management

User Behavior Moderation
Behavioral Analytics: Google Analytics 360 and
BigQuery ML
Fraudulent Account Detection:AI models on
Vertex AI
.
Compliance and Governance
Moderation
Data Classification : Use DLP API (Data Loss
Prevention)
Policy Enforcement:Apply AI-powered policy
enforcement with Policy Intelligence.
Real-time Compliance Monitoring:Use Cloud
SCC
.
Content Moderation
Text Moderation: Cloud Natural Language API
Image Moderation: Cloud Vision API
Video Moderation: Cloud Video Intelligence
API
Custom Moderation Models: Build custom ML
models with Vertex AI

28. C2 General
Application Modernization | Google Cloud AI Tools
•Vertex AI: Build and deploy custom AI/ML models
•Cloud Natural Language API: Text analysis and content filtering
•Cloud Vision API: Image content moderation
•Cloud Video Intelligence API: Video content moderation
•Security Command Center: AI-powered threat detection
•BigQuery ML: Analyze and predict trends in large datasets
•Recommendations AI: Personalized content and product
recommendations
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29. C2 General
Application Modernization | Key Considerations

29

Scalability
•Horizontal scaling: Add instances using Instance Groups or GKE nodes to meet demand.
•Vertical scaling: Adjust resources to handle fluctuating workloads.
•GCP Autoscale automates scaling based on metrics like CPU usage or request load..

Single Points of Failure (SPFs)
•To address SPFs, GCP provides solutions like global load balancing, managed instance groups, multi-zone and multi-region
deployments, and redundancy for services such as Cloud SQL and Cloud Storage
Multi-Tenancy Challenges
•GCP addresses multi-tenancy challenges with tools like GKE namespaces, IAM for tenant isolation, and scalable solutions such as
Cloud Spanner and auto-scaling. Custom branding, data security with Cloud KMS, and SLA monitoring via Cloud Operations ensure
optimized tenant management.
Cloud Load Balancing & Armor
Cloud Load Balancing provides application-layer load balancing, URL-based routing, and integration with Cloud Armor for web application
firewall functionality. It supports auto-scaling and performance monitoring to ensure high availability and optimized traffic management
Database Scalability
•GCP offers database scalability through managed services like Cloud Spanner for horizontal scaling and Cloud SQL for vertical scaling
of relational databases. It also supports NoSQL solutions like Firestore and Bigtable for dynamic scaling to handle varying workloads.

30. C2 General
Application Modernization | Benefits
.
Efficient
deployment of
resources
Accelerate
Innovation & time to
market
Improve Security &
reliability
Streamline
Infrastructure

Operational Costs
Business Agility

31. C2 General


Application Modernization | Challenges


01
02
05
03
04


Data Silos and
Volume
Budget Allocation
Knowledge Deficit

Tool Fragmentation
& Integration
.
Outdated and
Legacy Systems

32. C2 General
Start small with critical appsAnthos for hybrid/Multi

Adopt Microservice Approach


Leverage CI/CD
Monitor and optimize using
Google Cloud Monitoring &
Logging
Best Practices
Application Modernization | Best Practices

33. C2 General
Application Modernization | References
33
•https://medium.com/google-cloud/application-modernization-agility-on-googl
e-cloud-e50ad2b930ed
•SKILup IT Learning — DevOps Institute
•https://cloud.google.com/solutions/camp
•https://cloud.google.com/solutions/application-modernization/
•https://landscape.cncf.io/

34. C2 General

Thank You !