In this session, we discuss the AWS Cloud Adoption Framework and how you can use AWS Application Discovery Service to plan your migration. Learn how to analyze the data collected by Application Discovery Service agents combined with other sources of data, such as configuration management database (CMDB) exports using services like Amazon Athena and Amazon QuickSight. Learn how to profile workloads and identify interdependencies and utilization patterns to help you make decisions, like identifying assets to validate accuracy and completeness of data, finding the right size EC2 instance types, and planning migration waves and sequencing.

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Nirav Kothari
Principal Consultant, Amazon Web Services
Srikanth Ganesan
Cloud Architecture Manager, Motorola Solutions
ENT308
Migration Planning with
Application Discovery Service
Massud Sarwari
Cloud Infrastructure Architect, Amazon Web Services

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How customers approach migrations today
How Application Discovery Service (ADS) helps with migrations
How to explore data collected by Application Discovery Service
1
2
3
Agenda

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How AWS customers are migrating?
AWS Cloud Adoption Framework
Application 3 Application 3
Application 2 Application 2
Application 1 Application 1
Existing
IT estate
evaluation
Planning and
discovery
Operation
Application design
Migration and
validation
Strategy Plan Build Run

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What customers have told us:
Migration Challenges
Plan
• Performing analysis on
discovery data is time
consuming
• Determining how and when to
migrate an application is
difficult and carries risk
Migrate
• Lack of visibility into
application migrations slows
progress and velocity
• Tracking across multiple use
cases and migration tools is
complex
Discover
• Current asset management
databases are typically
inaccurate
• Discovering only servers is not
sufficient and presents an
incomplete view of your IT
portfolio

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AWS Application Discovery Service
streamlines planning for your cloud migration

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Key Benefits using Application Discovery Service
Integrated with Migration Hub
Available Globally
Discovery Flexibility: Agent vs Agentless
Plan your MigrationAssess Total Cost of Ownership
Securely capture discovery data

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How does Application Discovery Service work?
Overview
• Agents deployed on source hosts
• Windows & Linux support
• Capture system inventory, performance,
and dependencies
• Capture and store discovered data
securely to AWS
• API & console access to discovered
assets
• Output to CSV
• Can be imported into a third-party
migration or visualization tool
Discovery
Agents
Discovery DB
AWS Application
Discovery Service
On-premises data center
Encrypted
data
Internet

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Customer feedback on Application Discovery Service
Data Extraction is time consuming
Need for a way to bulk export
Support native visualization
1
2
3

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Application Discovery Service’s
Data Exploration

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3
2
1
How does ADS Data Exploration work?
Overview
Export data collected by
agents to a local target
export.py
Convert data to parquet and
upload to S3 convert_csv.py
Create table structure and query
data in Amazon Athena
discovery_athena.ddl
Agents collect server data
S3 Bucket
Amazon Athena
Amazon Quicksight
4
Visualize queried data in
Amazon Quicksight
Export Data

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ADS Database in Amazon Athena
▪ Hosts & Operating Systems
▪ Network Interfaces
▪ Processes
▪ Resources & System Performance
▪ Network Connections
application_discovery_service_database

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Operating System Info in Amazon Athena
account_number
agent_id
os_name
os_version
cpu_type
host_name
hypervisor
timestamp
os_info
bigint
string
string
string
string
string
string
timestamp
SELECT os_name,
COUNT(DISTINCT host_name)
FROM os_info
GROUP BY os_name

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Operating System Info in Amazon Athena
account_number
agent_id
os_name
os_version
cpu_type
host_name
hypervisor
timestamp
os_info
bigint
string
string
string
string
string
string
timestamp
SELECT os_name,
COUNT(DISTINCT host_name)
FROM os_info
GROUP BY os_name
os_name Count
Linux – RedHat Enterprise Linux Server release 6.7 x86_64 5
WindowsOS – Windows 2008 Standard Server x64 SP 1 14
WindowsOS – Windows 2008 Enterprise Server x64 SP 1 2
WindowsOS – Windows 2012 Standard Server x64 27

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Operating System Info in Amazon QuickSight

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System Performance in Amazon Athena
SELECT DISTINCT os.host_name,
os.os_name,
os.os_version,
MAX(sp.total_num_cores) AS cores,
MAX(sp.total_num_cpus) AS cpu,
MAX(sp.total_ram_in_mb) AS ram,
MAX(sp.total_disk_size_in_gb) AS disk_size_total,
MAX(sp.total_disk_free_size_in_gb) AS disk_size_free
FROM system_performance AS sp, os_info AS os
WHERE sp.agent_id = os.agent_id
GROUP BY os.host_name, os.os_name, os.os_version

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System Performance in Amazon Athena
host_name os_name cores cpu ram disk_size_
total
disk_size_
free
disk_read_ops disk_write_ops
squid-proxy-
p
Windows OS – Windows
2012 Standard Server
x64
1 1 4095.55 99.66 80.47 76.39 249.19
workflow-
mgr-p
Windows OS – Windows
2012 Standard Server
x64
4 4 16383.55 531.52 462.33 281.65 644.63
cac-p Windows OS – Windows
2012 Standard Server
x64
2 2 8191.55 99.66 69.99 90.48 267.54
a84-t Windows OS – Windows
2008 Standard Server
x64
4 2 4095.55 2205.89 845.53 84.30 303.63
db-int-p Windows OS – Windows
2008 Standard Server
x64
8 4 32767.55 1799.89 1044.53 102.36 22556.84

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System Performance in Amazon QuickSight

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Demo

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Migration Planning Using Athena and QuickSight
Amazon Athena
AWS Application
Discovery Service
Agent Data
CMDB
Other Data
Amazon QuickSight
Visualize
Other Sources

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Migration Planning Using Athena and QuickSight
Amazon Athena
AWS Application
Discovery Service
Agent Data
CMDB
Other Data
Validate data completeness1
Analyze operating costs2
Identify running software3
Analyze interdependencies4
Group and prioritize workloads5
Plan migration waves6Amazon QuickSight
Visualize
Other Sources

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Srikanth Ganesan
Cloud Architecture Manager, Motorola Solutions
ENT308
Motorola Solutions case study

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Motorola Solutions
1. Mission critical communication company
2. 17,000 employees across the globe with $6.4 billion revenue in 2017
3. Headquartered in Chicago

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Why Motorola Solutions IT migrated to Cloud
1. Reduce Data center foot print
2. Optimize current cost structure
3. Improve provisioning speed
4. Innovate by modernizing IT
5. Compute optimization

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Migration Methodology
Strategy
•Cloud First
•Datacenters footprint
reduction
App Discovery
•Application owners
•Inventory database
•Dependency mapping
•Performance
Landing Zone
Creation
VPCs/Connectivity
•DNS
•Backup
•Security suite
Plan, Migrate
and Validate
•Non-prod
•Internal
•Internet facing
Applications
Run
•Operate
•Automate
•Optimize

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Migration Strategy
1. Retire non-relevant workloads
2. Re-host (Lift & Shift of Cloud Compatible)
3. Re-platform to utilize AWS managed offerings,
such as RDS
4. Re-architect to augment with AWS Native
services
5. Retain (Not in scope)
6. Replace
70% Cloud Compatible IT
assets migrated to AWS

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Where does ADS fit in?
Challenges:
(Remaining 30% & Non-Cloud compatibles IT workload)
1. Legacy
2. Unaware of all possible dependencies
3. Multiple tools for gathering system performance data
Solution: AWS ADS
1. AWS native solution
2. Gather performance metrics
3. Identify dependency mapping

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Motorola Solutions ADS Implementation
AWS Application
Discovery Service
S3 Bucket Amazon Athena
Outbound
Port 443
Data Export

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Additional resources
AWS Discovery Service Documentation
https://aws.amazon.com/documentation/application-discovery/
Export to Athena GitHub Scripts:
http://github.com/awslabs/aws-discovery-utils

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Principal Consultant, AWS Professional Services
Email: kotharin@amazon.com
Nirav Kothari
Cloud Infrastructure Architect
Email: msarwari@amazon.de
Massud Sarwari
Speaker Contact

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2. Select the session you attended
3. Tap on Session Evaluation to submit your feedback
Submit Session Feedback

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Appendix: Athena queries example for
data exploration

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Migration Planning Using Athena and QuickSight
Amazon Athena
AWS Application
Discovery Service
Agent Data
CMDB
Other Data
Validate data completeness1
Analyze operating costs2
Identify running software3
Analyze interdependencies4
Group and prioritize workloads5
Plan migration waves6Amazon QuickSight
Visualize
Other Sources

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Validate Data Completeness – Destination IP (1/3)
SELECT DISTINCT destination_ip,
CASE
WHEN (
SELECT COUNT(*)
FROM network_interface
WHERE ip_address = destination_ip) = 0
THEN 'no'
WHEN (
SELECT COUNT(*)
FROM network_interface
WHERE ip_address = destination_ip) > 0
THEN 'yes'
END AS agent_running
FROM source_process_connection
WHERE destination_ip LIKE '192.%' OR destination_ip LIKE '10.%' OR
destination_ip LIKE '172.%'

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Validate Data Completeness – Destination IP (2/3)

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Validate Data Completeness – Destination IP (3/3)
Destination IP Addresses
with ADS Agent running

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Analyze Operating Costs (1/4)
SELECT h.host_name,
h.cpu AS cpu,
CAST(h.ram AS integer) AS ram,
i.instance_type AS instance_type,
ROUND(i.instance_type_price * 24 * 365) AS instance_type_price
FROM hosts h,
instance_types i,
instance_types j
WHERE
(h.cpu = i.cpu) AND
(h.cpu = j.cpu) AND
(CAST(h.ram AS integer) = j.ram) AND
(CAST(h.ram AS integer) = i.ram)
GROUP BY h.host_name, h.cpu, h.ram, i.instance_type, i.instance_type_price

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Analyze Operating Costs (2/4)

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Analyze Operating Costs (3/4)

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Analyze Operating Costs (4/4)

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Identify Running Software (1/3)
SELECT *
FROM destination_ports dports,
_iana_service_ports ianap
WHERE (dports.destination_port = ianap.port_number) AND
(ianap.transport_protocol = 'tcp')
ORDER BY destinationHostname

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Identify Running Software (2/3)

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Identify Running Software (3/3)

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Analyze Interdependencies (1/3)
SELECT sn1.application_name AS sourceApplication,
source.host_name AS sourceHostname,
con.source_ip,
sn2.application_name AS destinationApplication,
destination.host_name AS destinationHost,
con.destination_ip,
COUNT(con.destination_ip) AS destinationIpCount
FROM destination_process_connection con,
hostname_ip source, hostname_ip destination,
_cmdb sn1, _cmdb sn2
WHERE (con.source_ip = source.ip_address) AND
(con.destination_ip = destination.ip_address) AND
(sn1.host_name = source.host_name) AND
(sn2.host_name = destination.host_name) AND
(sn1.host_name != sn2.host_name)
GROUP BY sn1.application_name, source.host_name, con.source_ip,
sn2.application_name, destination.host_name, con.destination_ip

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Analyze Interdependencies (2/3)

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Analyze Interdependencies (3/3)

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Group and Prioritize Workloads (1/3)
SELECT cmdb.application_name,
COUNT(DISTINCT cn.source_ip) AS numSources,
COUNT(cn.source_ip) AS numConnections,
COUNT(DISTINCT cmdb.host_name) AS numServers,
CAST(CEIL(
LOG2(COUNT(DISTINCT cmdb.host_name)*
COUNT(DISTINCT cn.source_ip)*
COUNT(cn.source_ip)*
COUNT(DISTINCT cn.destination_port)/
MIN(cmdb.rto))) AS integer
) AS technical_complexity,
cmdb.business_criticality
FROM destination_process_connection cn, hostname_ip h1, _cmdb cmdb
WHERE cmdb.host_name = h1.host_name AND
cn.source_ip = h1.ip_address
GROUP BY cmdb.application_name, cmdb.business_criticality, cmdb.rto
ORDER BY technical_complexity DESC

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Group and Prioritize Workloads (2/3)

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Group and Prioritize Workloads (3/3)

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Plan Migration Waves (1/3)
SELECT cmdb.application_name || ' ' || cmdb.environment_type AS appEnvironment,
CASE
WHEN cmdb.environment_type = 'Production'
THEN 5+app.technical_complexity+app.business_criticality
WHEN cmdb.environment_type = 'Test'
THEN 3+app.technical_complexity+app.business_criticality
WHEN cmdb.environment_type = 'Development'
THEN app.technical_complexity+app.business_criticality
END AS wave,
app.numservers AS numServers
FROM application_complex_critical app, _cmdb cmdb
WHERE app.application_name = cmdb.application_name
GROUP BY cmdb.application_name, cmdb.environment_type,
app.technical_complexity, app.business_criticality,
app.numservers
ORDER BY wave ASC

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Plan Migration Waves (2/3)

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Plan Migration Waves (3/3)

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