AWS Application Discovery Service (ADS) | Deep Dive.

AWS Application Discovery Service (ADS) - Deep Dive.

Scope:

•  Key Use Cases,
•  Discovery Methods,
•  ADS Architecture Overview,
•  Data Collected by ADS,
•  Integrations with Other AWS Services,
•  ADS in the Migration Acceleration Program,
•  Planning with Application Dependency Mapping,
•  Limitations,
•  Best Practices (Expert Level),
•  Data Export Capabilities,
•  Recommended Architecture Pattern,
•  Final thoughts.

Intro:

•  AWS Application Discovery Service (ADS) is a tool used during large-scale migration planning to:
• collect detailed inventory, 
• configuration, 
• performance, 
• dependency data from on-premises environments to AWS.
•  AWS Application Discovery Service (ADS) is a foundational component of the AWS:
• Migration Acceleration Program (MAP) 
• Used to build migration plans, 
• Total Cost of Ownership (TCO) models, 
• Wave planning, 
• Application dependency maps.

ADS discovers key components:

• Servers (physical & virtual)
• Processes
• Network dependencies
• Application components
• Utilization patterns
• Performance metrics (CPU, memory, I/O)
• Installed applications and versions

NB:

• This information feeds migration planning tools such as:
• Migration Hub, 
• AWS Migration Evaluator,  
• AWS Migration Hub Strategy Recommendations.

 1. Key Use Cases

Use Case	Description
Application Dependency Mapping	Identifies all systems, services, ports, and flows involved in communication.
Migration planning	Build migration waves, grouping interdependent servers.
Right-sizing on AWS	Uses actual performance data to recommend optimal EC2 sizes.
TCO estimation	Combines utilization with licensing/inventory data.
Portfolio discovery	Inventory of all systems, versions, OS, and configurations.
Modernization pathway identification	Links with Strategy Recommendations to analyze modernization candidates.

NB:

• ADS is essential for enterprise migrations to avoid:
• Breaking dependencies during lift-and-shift
• Overprovisioning AWS resources
• Security issues due to unmanaged endpoints
• Incorrect grouping of servers

 2. Discovery Methods

• ADS provides three ways to collect data:

A. Agent-Based Discovery

• Uses lightweight agents installed on Windows or Linux servers.

Collects:

• Hardware specs
• Running processes
• Open network connections
• Port mappings
• Inter-server dependencies
• Performance metrics (CPU, RAM, IOPS)
• Installed applications
• Process-level details (better than network-only)

Pros:

• Most detailed dataset
• Dependency mapping is highly accurate
• Required for deep migration analysis
• Works on physical & virtual machines

Cons:

• Requires agent installation
• Harder for restricted/locked-down servers

B. Agentless Discovery (using VMware connector)

Runs as an OVA inside vCenter.

Collects:

• VM metadata (CPU, memory, disks)
• Host relationships
• Network info
• Performance metrics (from hypervisor)
• No OS-level process or dependencies

Pros:

• No agent installation
• Enterprise-friendly for VMware
• Quick initial discovery

Cons:

• No process-level or network mapping
• No application identification

C. Data Import (CSV/XLSX)

Useful for:

• Legacy environments
• Mainframes
• Unsupported OS (Solaris, AIX, HP-UX)
• Manual portfolio entries

Used for:

• TCO estimates
• Migration Hub portfolio completeness
• Wave planning when ADS cannot collect data

 3. ADS Architecture Overview

Main Components:

• On-premises agents / VMware Connector
• ADS Data Collectors
• Secure upload pipeline
• Migration Hub (central view)
• Migration Evaluator (TCO modeling)
• AWS Application Discovery APIs for integration

Data Flow:

1.  Agents/Connectors collect data locally
2.  Data is compressed and encrypted
3.  Sent to ADS backend in AWS
4.  Aggregated in ADS data store
5.  Visible in AWS Migration Hub
6.   Exportable to CSV/Jupyter/TCO tools

NB:

• ADS does not require inbound firewall rules — all communication is outbound only

 4. Data Collected by ADS

 Server Inventory

• Hostname, OS, kernel
• CPU count, sockets
• Memory
• Storage (size & type)
• Network interfaces

 Performance Profiling

• CPU utilization (avg, peak, p95)
• Memory consumption
• Disk throughput (IOPS / MB/s)
• Network throughput
• Daily/weekly usage patterns

🌐 Network Dependency Data

• Ports connecting IN/OUT
• Connection frequency
• Bandwidth
• Remote hosts
• Directional mapping
• Application traffic flows
• Creates a full Application Dependency Graph (ADG).

 Application Inventory

• Application name & version
• Installed packages
• Services running
• Middleware (Apache, IIS, JBoss, WebSphere)
• Database engines (SQL Server, Oracle, MySQL)

 5. Integrations with Other AWS Services

Service	Integration
AWS Migration Hub	Central repository for all discovered assets.
Migration Evaluator	TCO modeling using metrics.
AWS MGN (Application Migration Service)	Pre-migration sizing.
AWS SMS (Server Migration Service)	Legacy integration.
AWS Strategy Recommendations	Modernization analysis.
Migration Hub Refactor Spaces	Migration blueprint generation.

NB:

• ADS is most powerful when coupled with Migration Hub, as it allows wave planning and dependency visualization.

 6. ADS in the Migration Acceleration Program (MAP)

ADS supports each MAP phase:

Phase 1: Assess

• Portfolio discovery
• Performance baseline
• Licensing inventory
• TCO estimation using real data

Phase 2: Mobilize

• Group workloads into migration waves
• Identify blockers
• Determine refactor vs rehost

Phase 3: Migrate and Modernize

•  Provide sizing inputs to:
• AWS MGN
• SMS
• CloudEndure
• Validate dependency requirements during cutover

 7. Planning with Application Dependency Mapping

• Dependency mapping is crucial to avoid migration breakage.

ADS identifies:

•  Application tiers
•  Web → App → DB
•  Shared services (DNS, AD, NTP)
•   Inter-app communication
•  Legacy integration endpoints
•  Batch jobs

This allows teams to:

•  Build accurate migration wave groups
•  Discover hidden dependencies
•  Prevent outages

 8. Limitations

Not everything is supported.

Discovery Limitations:

• No support for AIX, Solaris, HP-UX (manual import required)
• VMware connector works only with vCenter (not standalone ESXi)
• Does not capture SMB shares, NFS mounts in detail
• No deep database schema discovery
• No layer-7 protocol intelligence
• Agents cannot be installed on locked-down OS

Functional Limits:

• Data retention: typically 1 year
• No real-time analysis (data is near real-time but not streaming)
• Cannot detect business-level application structure automatically

 9. Best Practices (Expert Level)

A. Deploy ADS early

• Start data collection at least 30–90 days before migration planning, so:
• Full cyclic workloads are captured
• Seasonal peaks are visible

B. Use both agent + agentless

• Agentless for broad inventory
• Agent-based for deep analysis

C. Tag discovered applications

Using Migration Hub:

• map servers → applications
• ensure dependency accuracy

D. Gather 3–4 peak workload cycles

• Critical for accurate sizing.

E. Integrate with CMDB

• Export ADS data to enrich CMDB (ServiceNow/Atlassian/etc.).

F. Build migration waves based on:

• dependency groupings
• common maintenance windows
• business functions
• latency sensitivity

G. Secure ADS data

• Keep data private by locking down IAM roles.

 10. Data Export Capabilities

ADS allows exporting:

• Server inventory (CSV)
• Utilization metrics
• Dependency mapping data
• Application groupings
• Portfolio data

Exports are frequently used in:

• Excel/PowerBI dashboards
• Jupyter notebooks
• MAP TCO tools
• Hybrid architecture diagrams

 11. Recommended Architecture Pattern

• A modern migration discovery architecture:

On-Prem Servers → ADS Agents → ADS Backend → Migration Hub

               → VMware Connector (optional)

               → TCO Engine → Migration Evaluator

               → Strategy Recommendations → Modernization Path

Final thoughts

• AWS Application Discovery Service (ADS) is a foundational tool in large-scale cloud migrations.
• AWS Application Discovery Service (ADS) ensures:
• Accurate planning, 
• Dependency analysis, 
• Capacity estimation,  
• Portfolio rationalization — preventing costly missteps during migration.