How to surface dependencies before cutoverThe VM migrates successfully. It boots on Nutanix AHV. The infrastructure team marks the task complete. Then the phone rings. The application is down. Users cannot authenticate. Transactions are failing. The VM works, but nothing connected to it does.
This scenario repeats across VMware to Nutanix migration projects because the technical transfer of a virtual machine is the easy part. The hard part is understanding what that VM depends on and what depends on it. Dependencies that were never documented, that nobody remembers creating, that accumulated organically over years of production operations. Gartner predicts that by 2028, 55% of enterprises will initiate proofs of concept for alternative infrastructure products to replace their VMware deployments. Many of those projects will stumble on dependency issues.
This article explains why dependency mapping matters more than any other factor in migration success. Research from 451 Research highlights that legacy system limitations and data dependencies are primary causes of migration failures, emphasizing the need for careful planning. You will learn where hidden dependencies lurk, how to surface them before they surface themselves during a cutover, and what a structured approach to risk assessment looks like in practice.
Why dependencies stay hidden
Most organizations do not have a complete, accurate map of how their systems connect. This is not negligence. It is the natural result of how enterprise IT evolves. Gartner analyst Michael Warrilow observed that migrating from VMware requires untangling many aspects of years of investment. Dependencies are a significant part of that complexity.
Applications get deployed under deadline pressure. The developer documents the database connection but not the monitoring agent, the logging service, or the authentication call. Over time, integrations accumulate. A new reporting tool queries the database directly. A backup service adds an agent. A security scanner starts polling the API.
Each change makes sense in isolation. Nobody has the job of maintaining a holistic map. The CMDB captures what was true at the time of the last audit, not what is true today. Tribal knowledge fills the gaps, stored in the heads of engineers who may have left the company or moved to different roles.
When migration planning begins, teams inherit this accumulated complexity. They see VMs in vCenter. They see rows in a spreadsheet. They do not see the invisible threads connecting those VMs to the rest of the environment.
The categories of dependencies that cause failures
Not all dependencies carry equal risk. Understanding the categories helps prioritize discovery efforts.
Infrastructure dependencies
These are the foundational services that almost every VM needs: DNS, DHCP, NTP, Active Directory, certificate authorities. They are easy to overlook because they are everywhere. A VM migrates to a new network segment that cannot reach the domain controller. Authentication fails. The application is dead.
Infrastructure dependencies are typically well documented at the service level but poorly documented at the consumer level. You know where Active Directory runs. You may not know every application that queries it.
Application-tier dependencies
These are the connections between components of a distributed application: web servers to application servers, application servers to databases, microservices to message queues. They are often documented in architecture diagrams that were accurate when the system launched but have drifted since.
Application-tier dependencies are the most common source of migration failures. Teams migrate a web server without realizing it calls an API on a VM still running in VMware. The network path changes. Latency increases. The connection times out.
Data dependencies
These are less obvious. A VM may not have a direct connection to another system but may depend on data that flows through intermediate processes. A nightly ETL job extracts data from one database and loads it into another. If the source database migrates but the ETL job's configuration still points to the old location, the data pipeline breaks silently.
Data dependencies often manifest as delayed failures. Everything looks fine on day one. The problem appears a week later when the batch process runs.
Operational dependencies
These include monitoring agents, backup services, patch management tools, and configuration management systems. They are invisible to the application but essential to operations. A VM migrates successfully, but the monitoring agent cannot reach its server. The VM drops off the dashboard. When an issue occurs, nobody is alerted.
Operational dependencies are frequently missed because they are managed by different teams than those running the migration.
How to surface dependencies before cutover
Effective dependency mapping uses multiple sources and validation methods.
Network traffic analysis
The most reliable way to discover dependencies is to observe actual communication patterns. Network flow data, firewall logs, and application performance monitoring tools capture who talks to whom. This evidence-based approach surfaces connections that documentation misses.
The limitation is that traffic analysis only captures active communication. A dependency that fires once a month during a batch job may not appear in a week of observation. Timing matters.
Configuration inspection
Application configuration files, connection strings, and service registrations contain explicit references to dependent systems. Automated scanning can extract these references at scale. The challenge is that configurations may contain outdated entries or use DNS aliases that obscure the actual target.
Stakeholder interviews
Application owners and support engineers carry knowledge that does not exist anywhere else. They remember the workaround that was implemented three years ago, the undocumented integration that marketing requested, the dependency that only matters during quarter-end processing.
Interviews are time-consuming but irreplaceable. They also create accountability. When an application owner confirms their dependencies, they take ownership of the accuracy of that information.
CMDB correlation
Configuration management databases may not be current, but they provide a starting point. Correlating CMDB relationships with observed traffic and configuration data highlights gaps and conflicts that need resolution.
Building a risk assessment framework
Once dependencies are mapped, the next step is assessing which ones pose migration risk.
- Criticality: How important is the dependent service? A connection to a tier-one production database carries more risk than a connection to a development test server.
- Portability: Can the dependency be satisfied after migration? If the dependent service is also migrating, the question is sequencing. If the dependent service is staying on VMware, the question is cross-platform connectivity.
- Latency sensitivity: Some applications tolerate network latency changes. Others fail when response times increase by milliseconds. Migrations that change network paths may introduce latency that breaks timing-sensitive integrations.
- Validation complexity: How difficult is it to verify that the dependency works post-migration? A simple health check is low risk. A dependency that can only be tested during a monthly batch run is high risk.
- Rollback impact: If the migration fails and the VM rolls back, what happens to the systems that already updated their configurations to point to the new location? Dependencies create rollback complexity that must be planned.
VirtualReady and automated dependency mapping
ReadyWorks VirtualReady automates much of the dependency discovery process. The platform ingests data from vCenter, CMDBs, ticketing systems, and network monitoring tools. Machine learning models identify relationships based on naming conventions, network communication patterns, and application metadata.
The dependency map visualizes connections between VMs, services, and external systems. Risk scores highlight relationships that require attention. Application owners receive automated requests to validate dependencies within their scope.
When dependencies are confirmed, they become part of the bundle definition. The platform ensures that related VMs migrate together or in the correct sequence. If a dependency cannot be satisfied post-migration, the system flags a blocker that must be resolved before cutover.
This approach compresses dependency discovery from weeks of manual investigation to days of automated analysis followed by targeted validation.
Common dependency patterns and how to address them
Shared database servers. Multiple applications connect to the same database VM. Migrating the database affects all consumers. Address this by migrating the database and its consumers as a single bundle, or by establishing cross-platform connectivity that allows phased migration.
- Authentication services: Active Directory, LDAP, and SSO services are dependencies for nearly every application. Ensure network paths to authentication services remain available throughout the migration. Consider extending AD sites or deploying read-only domain controllers in the target environment.
- Load balancers and reverse proxies: These services distribute traffic to backend VMs. Migrating backends without updating load balancer configurations breaks traffic flow. Coordinate changes across layers or migrate entire application stacks together.
- Monitoring and logging infrastructure: Agents on migrated VMs must reach collection servers. Update agent configurations or ensure network connectivity before cutover. Validate that logs and metrics flow correctly before declaring success.
- Backup services: Backup agents and schedules may need reconfiguration after migration. Include backup validation in post-migration testing. A VM without working backups is a liability, not a success.
The cost of discovering dependencies late
When dependencies surface during cutover instead of during planning, the costs compound.
- Immediate outage. The application fails. Users are impacted. The incident process activates.
- Rollback overhead. The team reverses the migration, but systems that updated their configurations must also roll back. Complexity increases with each dependent system.
- Schedule disruption. The failed cutover consumes the maintenance window. The next attempt must wait for the next available window, which may be weeks away.
- Stakeholder confidence erosion. Leadership questions whether the migration is under control. Application owners become reluctant to approve future cutovers. The program slows.
- Technical debt accumulation. Workarounds implemented during the incident become permanent fixtures. The environment grows more complex instead of simpler.
Investing in dependency mapping before migration costs a fraction of what late discovery costs during execution.
Making dependency mapping part of the process
Dependency discovery should not be a one-time event at the start of the migration. It should be a continuous process that runs throughout the program.
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During planning, map known dependencies and identify gaps that require investigation.
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During remediation, resolve blockers and validate that dependencies can be satisfied post-migration.
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During cutover, verify connectivity to all mapped dependencies before declaring success.
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During stabilization, monitor for unexpected communication patterns that indicate missed dependencies.
This continuous approach catches issues early when they are cheap to fix rather than late when they are expensive to remediate.
FAQ
What percentage of migration failures are caused by dependencies?
Industry data suggests that 40 to 50 percent of migration-related incidents trace back to undocumented or incorrectly mapped dependencies.
How long does dependency mapping take?
With automated tools, initial discovery takes one to two weeks. Validation with stakeholders adds another two to four weeks depending on organization size.
Can dependencies be discovered without network traffic analysis?
Yes, but accuracy decreases. Configuration inspection and stakeholder interviews provide partial coverage. Traffic analysis provides evidence-based validation.
What if a dependency cannot be resolved before migration?
Options include maintaining cross-platform connectivity, migrating dependent systems first, or accepting the dependency as a known risk with rollback plans.
How does VirtualReady handle dependencies across multiple vCenters?
The platform normalizes data across vCenters and other sources, building a unified dependency map that spans the entire virtual estate.
One next step
Discover the dependencies hiding in your VMware environment. Download the VM Accelerator for free to gather the necessary data for your first dependency map within two weeks.
