TL;DR

A private cloud change depends on more than the performance of its compute or storage component. Map the roles of VCF and Dell infrastructure to workload requirements, ownership, lifecycle responsibilities, and evidence that the service is ready to change. The race-team illustration supports that coordination model; release decisions still depend on the actual platform and application state.

The practical lesson is simple: enterprise platform performance is not measured by how quickly one component can run in isolation. It is measured by how reliably the whole platform can respond when demand, risk, capacity, or business priority changes in the middle of the race.

On this page

• The Race Is Won by Coordination, Not Raw Speed
• Architecture at a Glance
• Scenario: Lap 37 in a Changing Storm
• Scope and Terminology Guardrails
• Assumptions Behind the Model
• Mapping the Pit Crew to Platform Roles
• VCF Is the Race Control Layer
• VCF Automation Controls Pit Release
• VCF Operations Turns Telemetry into Strategy
• PowerEdge Provides the Compute Chassis
• PowerFlex Is the Adaptive Infrastructure Layer
• PowerScale Feeds Data-Intensive Workloads
• PowerStore and PowerMax Serve Different Critical Data Profiles
• NSX Defines the Track Limits
• Decision Criteria for Every Pit Stop
• The Pit Stop Operating Loop
• Building the Model in Practical Phases
• Risks, Caveats, and Operational Gotchas
• Operational Readiness Checklist
• Conclusion
• External References

Introduction

A release decision crosses compute, storage, networking, security, platform services, and application ownership. Each team needs a shared view of what will change, which dependencies matter, and what evidence establishes readiness. The race-team metaphor illustrates how those responsibilities coordinate under pressure.

The same is true of VMware Cloud Foundation in an enterprise environment. Compute performance matters. Storage latency matters. Network throughput matters. Automation matters. Security matters. None of those capabilities, by itself, creates an effective private cloud operating model.

The difficult work is coordination.

The supplied image captures that coordination through a multicloud grand prix metaphor. The VCF race car sits in the pit while specialized crews work against a live stream of weather, tire, telemetry, workload, and strategy data. Along the bottom, the workload portfolio ranges from enterprise applications and Kubernetes to AI, databases, analytics, edge, development, and disaster recovery. On the right, the platform stack includes VMware Cloud Foundation, VCF Automation, VCF Operations, NSX, PowerEdge, PowerFlex, PowerScale, PowerStore, and PowerMax.

That is not merely a product collage. It is a useful architecture and operating-model prompt.

The central question is not, “Which product is fastest?” The more useful question is, “How should these capabilities work together so the platform can make safe, repeatable, and observable decisions under changing conditions?”

The Race Is Won by Coordination, Not Raw Speed

Traditional infrastructure projects often optimize components independently. The compute team selects a server platform. The storage team selects an array. The network team defines transport and security. The virtualization team builds clusters. The automation team adds workflows later. Operations receives dashboards after production goes live. Recovery is tested during a narrow annual exercise.

Each team may deliver a technically sound component, yet the resulting platform can still be slow to consume and difficult to operate. The handoffs become the bottleneck. Provisioning requires tickets. Policy is interpreted differently by each team. Capacity decisions are made from disconnected reports. Application owners receive infrastructure, but not a dependable service contract.

A race team cannot work that way. The wheel crew does not wait for a separate approval chain after the car enters the pit. The telemetry team does not send a monthly spreadsheet after the race. The strategy desk does not invent tire policy during the stop. Roles, thresholds, tools, and fallback actions are defined before the car arrives.

A mature VCF operating model should pursue the same discipline. The platform needs pre-engineered service patterns, known workload-domain boundaries, policy-controlled automation, integrated health signals, capacity thresholds, tested lifecycle procedures, and recovery paths that can be executed without improvisation.

Architecture at a Glance

The most important detail in this model is the feedback loop. Application demand enters through a governed service interface. VCF places the request into an appropriate workload and policy boundary. Dell infrastructure supplies the required compute and data characteristics. NSX applies network and security intent. Operations telemetry then feeds capacity, health, risk, and optimization information back into the next decision.

The platform should therefore be designed as a closed operational loop, not a one-way provisioning pipeline.

What matters here is the direction of responsibility. Consumers request an outcome, not a collection of infrastructure parts. Automation translates that intent into an approved deployment pattern. Workload boundaries contain lifecycle, policy, and failure-domain decisions. Infrastructure platforms deliver the required characteristics. Operations validates whether the service continues to meet its objectives.

Scenario: Lap 37 in a Changing Storm

Consider a mixed enterprise environment running several service classes:

• a revenue application with strict availability and latency requirements
• a Kubernetes platform supporting internal development teams
• an AI inference service with bursty accelerator and data demand
• analytics pipelines reading large volumes of unstructured data
• database services with predictable transaction profiles
• an edge workload with intermittent connectivity
• a disaster-recovery tier that must remain ready without consuming production-class resources continuously

Now introduce the equivalent of the storm shown in the image. Demand rises quickly. One storage pool approaches a protection threshold. A network policy change is waiting for approval. An AI service requests more capacity. A planned lifecycle activity is scheduled for the same evening. An application owner reports intermittent latency, but the infrastructure teams do not yet know whether the cause is compute contention, storage behavior, east-west traffic, or an application change.

A component-centered organization creates a war room and begins collecting screenshots.

A platform-centered organization already has a race plan. It knows which service objectives apply, which telemetry sources are authoritative, which scaling actions are permitted, which changes require approval, which workload domain owns the service, and which rollback point is valid. It may still need engineering judgment, but it does not need to invent the operating model during the incident.

Scope and Terminology Guardrails

The race metaphor is useful only when its limits are explicit.

Multicloud Does Not Mean One Universal Control Plane

VMware Cloud Foundation is a private cloud platform. It can participate in a broader hybrid or multicloud strategy, but it should not be described as a universal controller for every public-cloud service. A practical multicloud operating model standardizes selected concerns such as identity, policy, service definitions, observability, connectivity, deployment evidence, and recovery expectations. It does not erase platform-specific architectures or operating constraints.

The race series may span multiple tracks, but each track still has different rules, weather, surfaces, and support boundaries.

The Car Represents a Service Boundary

The VCF car in the image should not be interpreted as one giant cluster carrying every workload. A better mapping is a governed platform service composed of management capabilities and one or more workload boundaries. Different workloads may require different availability, lifecycle, storage, network, security, and performance profiles.

Standardization should occur at the service-pattern level. Uniformity across every workload is neither realistic nor desirable.

Product Roles Are Not Automatic Integrations

Placing product names next to one another does not create an integrated platform. Each relationship must be designed, validated, monitored, supported, and owned. Compatibility, lifecycle order, failure behavior, certificate handling, identity, API limits, support boundaries, and upgrade sequencing must be treated as engineering decisions.

A visually unified pit crew still needs documented procedures for every tool it touches.

Primary Storage Is Not Backup

PowerFlex, PowerScale, PowerStore, and PowerMax provide different primary data-service characteristics. Replication, snapshots, and local protection features can strengthen resilience, but they do not automatically satisfy independent backup, cyber-recovery, retention, legal-hold, or disaster-recovery requirements.

The platform design must distinguish availability from recoverability. A fast car that cannot be rebuilt after a major failure is not operationally complete.

Assumptions Behind the Model

This article uses a VMware Cloud Foundation 9.1 documentation baseline for platform terminology and architecture concepts. The PowerFlex implementation reference is specifically scoped to VMware Cloud Foundation 9.0, because that is the version identified by the Dell implementation guide. That distinction matters. A design should never generalize a version-specific implementation statement into an unsupported claim about every release.

The model also assumes:

• the organization wants a repeatable private cloud service rather than a collection of individually managed clusters
• workload classes can be expressed through measurable service objectives and policy
• platform teams have authority to standardize provisioning, lifecycle, observability, security, and recovery controls
• Dell platform choices are selected according to workload requirements rather than logo consistency
• automation is governed through version-controlled templates, policy, approvals, and evidence
• operations data is trusted only after telemetry sources, ownership, retention, and alert-routing responsibilities are defined
• recovery plans are tested at the service level, not inferred from component redundancy

These assumptions are not minor details. They determine whether the race-team model produces operational advantage or becomes a polished diagram layered over traditional silos.

Mapping the Pit Crew to Platform Roles

The image becomes more useful when every visual role is translated into a specific platform responsibility.

Race-Team Role	Platform Capability	Primary Responsibility	Operational Question
Race control	VMware Cloud Foundation	Platform structure, workload boundaries, lifecycle coordination, service governance	Which approved platform pattern should run this workload?
Pit release	VCF Automation	Catalog, policy, orchestration, placement, approvals, quotas	Is this request safe and ready to provision?
Telemetry and strategy wall	VCF Operations	Health, performance, capacity, diagnostics, trend analysis	Is the service meeting its objectives, and what action is required?
Chassis and power unit	PowerEdge	Compute capacity, accelerator options, hardware lifecycle	Does the compute profile match workload demand and failure-domain design?
Adaptive infrastructure crew	PowerFlex	Software-defined block storage and flexible infrastructure scaling	Where should block capacity and performance be allocated?
Data-intelligence feed	PowerScale	Scale-out file and unstructured-data services	How will data-heavy workloads access and grow shared data efficiently?
Agile application-data crew	PowerStore	Flexible all-flash block and file services	Does the workload need a versatile, consolidated data platform?
Mission-critical data crew	PowerMax	High-end enterprise data services for demanding critical workloads	Does the service justify the operational and economic profile of a mission-critical array?
Track limits and stewarding	NSX	Segmentation, routing, network policy, security enforcement	Which communications are allowed, observed, and denied?
Recovery strategy	Backup and recovery controls	Independent protection, restore, cyber-recovery, and DR validation	Can the service be recovered within its stated objectives?

The mapping does not declare one universally correct design. It provides a decision framework. Each role exists to answer a different operational question, and overlap must be resolved intentionally.

VCF Is the Race Control Layer

VMware Cloud Foundation provides the structure that turns virtualization, network, storage, automation, and operations capabilities into a governed private cloud platform. Its value is not that every workload uses the same infrastructure. Its value is that the organization can define a consistent way to create, operate, secure, and evolve services across approved platform patterns.

Workload boundaries are central to that model. They create places to express lifecycle alignment, ownership, availability, capacity, and policy decisions. A management environment should not become an unrestricted landing zone for application workloads. An AI or analytics service should not consume resources under the same assumptions as a small development environment. A regulated application should not inherit network and identity policy accidentally from a neighboring service.

Race control does not drive the car. It establishes the rules, coordinates the event, resolves exceptions, and ensures the system remains inside a supportable operating envelope.

VCF Automation Controls Pit Release

Automation is often reduced to speed. That is too narrow. In enterprise platforms, automation should make approved decisions repeatable, observable, and reversible.

A service request should enter through a defined interface such as a catalog item, API, infrastructure-as-code workflow, or platform template. The request should carry enough metadata to support placement, quota, ownership, network, security, backup, lifecycle, cost, and support decisions. Approval logic should be proportional to risk. Low-risk, standardized requests can proceed quickly. Exceptions should enter a visible review path rather than bypass the platform through manual changes.

The most useful automation artifacts are not scripts that create resources. They are service contracts encoded as templates and policy. A strong template explains:

• which workload class it serves
• which platform and storage profile it selects
• which network segments and security policies apply
• which monitoring and logging integrations are mandatory
• which backup or recovery policy is attached
• which owner and cost context are recorded
• which validation checks define successful delivery
• which rollback action is available if validation fails

The pit crew does not simply change tires faster. It changes the correct tires, with the correct pressure, under the correct race condition, while preserving evidence that the stop was completed safely.

VCF Operations Turns Telemetry into Strategy

Operations data has value only when it changes a decision. Dashboards that display thousands of signals without ownership, thresholds, or action paths are equivalent to a pit wall covered in gauges that nobody trusts.

VCF Operations should connect infrastructure health to service health. That requires more than collecting metrics. The platform team must define what good looks like for each service class, which indicators represent user impact, which conditions are early warnings, and who owns each response.

Useful operating signals include:

• capacity headroom by workload domain and service class
• compute, memory, storage, and network contention
• policy drift and configuration deviation
• hardware and component health
• application or service-level symptoms
• growth rate and exhaustion forecasts
• failed automation and lifecycle tasks
• backup success, restore validation, and recovery readiness
• security events and microsegmentation policy anomalies
• change correlation across application and infrastructure layers

The objective is not a single dashboard for every persona. Platform engineers, application teams, security teams, service owners, and leaders need different views of the same evidence. The underlying data must be correlated, but the operational experience should remain role-aware.

PowerEdge Provides the Compute Chassis

PowerEdge represents the physical compute foundation in the pit-crew model. Server selection should begin with workload and failure-domain requirements, not with maximum specifications.

The relevant questions include:

• what processor, memory, accelerator, and local-device profile does the workload require?
• how will hosts be grouped into clusters and failure domains?
• what capacity must remain available during maintenance or component failure?
• what firmware, driver, and platform compatibility baselines apply?
• how will hardware lifecycle align with VCF lifecycle activities?
• what telemetry is exposed to the operations layer?
• which workloads require GPU or other accelerator resources, and how will those resources be governed?

A race chassis must be fast, but it must also be predictable, serviceable, and compatible with every other part of the car. The same principle applies to compute design. Peak benchmark performance is less valuable than a supported configuration with known maintenance behavior, capacity headroom, and tested failure response.

PowerFlex Is the Adaptive Infrastructure Layer

PowerFlex fits the image’s adaptive-infrastructure theme because it provides software-defined block storage and can support flexible infrastructure scaling patterns. Dell’s VCF 9.0 implementation guidance describes using PowerFlex as principal storage for management and workload domains in that release context.

The operational value is not simply elasticity. It is the ability to shape compute and storage resources according to platform demand while preserving a governed architecture. That requires explicit decisions about node roles, protection domains, storage pools, fault sets, network design, performance isolation, capacity reserve, and lifecycle ownership.

PowerFlex is not an excuse to collapse every boundary. Flexible scaling increases the need for policy because the platform can change more dynamically. Teams must know which changes are allowed, how rebalance behavior affects workloads, which thresholds trigger expansion, and how infrastructure actions are coordinated with VCF lifecycle operations.

Adaptive infrastructure without operational guardrails is simply a faster way to create drift.

PowerScale Feeds Data-Intensive Workloads

PowerScale represents scale-out file and unstructured-data services. In the race metaphor, it is the data-intelligence feed supporting telemetry archives, analytics, content repositories, model data, and other workloads that need shared access to large data sets.

A platform team should not select it merely because the environment includes AI. The decision should be based on data shape, access protocol, throughput, concurrency, namespace growth, protection, retention, and placement requirements. AI training data, inference data, model artifacts, application content, and analytics archives can have very different operational profiles even when they all appear under the broad label of unstructured data.

The design questions include:

• how data is ingested, classified, retained, and deleted
• which workloads need concurrent access
• how namespace growth affects operations
• where data locality matters
• which protection and replication objectives apply
• how access is governed through identity and network policy
• which telemetry must be visible to platform and data owners

The platform should expose a usable data service, not merely a large file system.

PowerStore and PowerMax Serve Different Critical Data Profiles

PowerStore and PowerMax appear together in the image, but they should not be flattened into interchangeable storage tiers.

PowerStore is positioned as a flexible, scalable all-flash platform that can serve block and file use cases. In a VCF architecture, it may fit workloads that need a versatile enterprise data platform with a balanced operational profile.

PowerMax is positioned for demanding mission-critical enterprise workloads. Its fit should be justified by service objectives, scale, data protection, operational requirements, and economic value. The presence of an important application does not automatically require the highest-end platform. The decision should be tied to measurable requirements and the consequences of failure.

A useful storage-service catalog might therefore describe profiles such as:

• general-purpose virtualized application storage
• high-performance transactional storage
• large-scale software-defined block storage
• shared unstructured-data storage
• mission-critical enterprise storage
• recovery or archive storage

Each profile should include performance expectations, availability design, protection method, lifecycle ownership, capacity policy, monitoring, and cost context. Application owners should choose an approved service outcome, not negotiate an array model during every project.

NSX Defines the Track Limits

The image places NSX under secure connectivity, microsegmentation, encryption, and zero-trust themes. The strongest operational interpretation is that NSX defines and enforces the communication boundaries around workloads.

Microsegmentation should begin with application dependency and trust analysis. A platform team must understand which systems need to communicate, through which services, under which identity or context, and how exceptions are reviewed. A rule base built from broad network ranges and permanent emergency allowances will not produce meaningful segmentation even if the enforcement technology is capable.

The race steward analogy is useful. Track limits are defined before the race. Violations are observed consistently. Exceptions are explicit. Evidence is retained. The rules apply throughout the event, not only during an audit.

For VCF services, network and security design should address:

• management-plane isolation
• workload east-west segmentation
• north-south routing and inspection
• administrative access paths
• service-to-service dependencies
• DNS, time, certificates, and identity dependencies
• logging and policy-change evidence
• break-glass access and expiration
• disaster-recovery network behavior
• policy migration during application change

Zero trust is not a product setting. It is an operating discipline built from identity, least privilege, segmentation, verification, telemetry, and continuous policy management.

Decision Criteria for Every Pit Stop

The race-team model becomes actionable when platform changes are evaluated against explicit criteria. The following decision frame can be applied to capacity expansion, workload placement, storage selection, network policy, lifecycle activity, or recovery design.

Decision Criterion	Question	Evidence Required
Service objective	What business or technical outcome must be preserved?	Availability, latency, recovery, throughput, data, and support requirements
Workload fit	Which approved platform pattern matches the demand profile?	Application dependencies, usage pattern, growth, compliance, and data characteristics
Failure domain	What can fail together, and how is service maintained?	Cluster, rack, site, network, storage, identity, and external-dependency analysis
Lifecycle compatibility	Can the full stack be patched and upgraded safely?	Compatibility baseline, sequencing, maintenance capacity, rollback, and support ownership
Security boundary	Which communications and administrative actions are permitted?	Dependency map, identity model, policy, logs, exception process, and review cadence
Observability	How will success, degradation, and risk be detected?	Metrics, logs, events, service indicators, thresholds, dashboards, and alert ownership
Recovery	Can the service be restored within its objectives?	Backup policy, restore evidence, replication design, DR test results, and dependency recovery order
Economics	Is the service profile justified by business value?	Capacity model, utilization, support cost, operational effort, growth, and consequence of failure
Reversibility	What happens if the change produces an unacceptable result?	Rollback point, data-consistency checks, fallback capacity, and decision authority

The table prevents a common architecture mistake: choosing a component first and writing the justification afterward.

The Pit Stop Operating Loop

A mature platform should treat every significant change as a controlled operating loop. The same sequence applies whether the trigger is a new service request, a capacity threshold, a security finding, a lifecycle event, or an incident.

The final step is often omitted. Evidence from each execution should improve the service pattern. Repeated manual fixes indicate missing automation or poor defaults. Repeated exceptions indicate that the catalog does not match real workload needs. Repeated alert noise indicates weak service indicators. Repeated rollback difficulty indicates that changes are too large or validation is too late.

A pit crew becomes faster because it studies every stop. A platform team should do the same.

Building the Model in Practical Phases

Organizations should not attempt to create the entire race operation in one program. The operating model can be built in controlled phases with measurable exit criteria.

Establish the Service Baseline

Inventory the current VCF environment, workload domains, clusters, network boundaries, data platforms, service owners, support contracts, lifecycle baselines, recovery methods, and operational tools. Identify where ownership is unclear and where critical services depend on undocumented manual actions.

The exit criterion is not a perfect configuration database. It is a trusted map of the services, boundaries, dependencies, and owners needed to make platform decisions.

Define a Small Set of Service Profiles

Select a manageable group of workload patterns such as general enterprise applications, development platforms, Kubernetes, AI or analytics, regulated services, and disaster recovery. Define the expected compute, network, security, storage, operations, lifecycle, and recovery characteristics for each.

Avoid building dozens of catalog items before the core patterns are stable. Variation should be introduced only when it represents a real service difference.

Encode Policy and Automation

Translate the service profiles into templates, APIs, quotas, approvals, placement logic, network policy, monitoring, backup assignment, tagging, and validation. Store configuration and policy artifacts in version control. Define how exceptions are requested, approved, expired, and reviewed.

The objective is reproducibility, not automation volume.

Integrate Operations and Recovery Evidence

Connect infrastructure and service telemetry to actionable ownership. Create service-level views that correlate compute, storage, network, security, application, and change signals. Add recovery evidence, including backup success and restore-test outcomes, to the operating picture.

A service should not be marked healthy merely because its virtual machines are powered on.

Exercise the Pit Stop

Run controlled scenarios before production urgency forces them. Test capacity expansion, host or node maintenance, policy changes, failed automation, storage degradation, application rollback, certificate expiration, backup restore, site failover, and recovery of platform dependencies.

Measure detection time, decision time, execution time, validation quality, rollback reliability, communication, and evidence capture. Update the operating pattern after each exercise.

Scale Through Product Ownership

Assign owners to service profiles, automation, platform health, network security, data services, lifecycle, recovery, and user experience. Review service adoption, exception volume, operational load, capacity, risk, and customer feedback. Retire patterns that create unnecessary complexity.

A private cloud becomes a platform when teams manage it as a product with defined consumers, contracts, evidence, and improvement loops.

Risks, Caveats, and Operational Gotchas

The race metaphor can hide important engineering realities if it is treated too literally.

A Unified Console Is Not a Unified Operating Model

Dashboards can aggregate signals, but they do not resolve ownership, policy conflicts, lifecycle sequencing, or recovery responsibility. Integration work must include process and decision design, not only data collection.

Best-of-Breed Can Become Best-of-Complexity

Specialized compute and data platforms provide valuable workload fit. They also increase compatibility, support, monitoring, skills, procurement, lifecycle, and recovery complexity. The organization should add a platform profile only when the service benefit exceeds the operating cost.

Automation Can Accelerate Bad Defaults

A fast catalog that provisions oversized, underprotected, weakly segmented, or poorly monitored services increases risk. Validate the service pattern before scaling consumption.

Capacity Headroom Must Be Deliberate

Highly utilized infrastructure may appear efficient until maintenance, failure, rebalance, recovery, or demand spikes occur. Capacity policy must account for degraded operations and planned change, not only steady-state averages.

Lifecycle Is a Full-Stack Activity

VCF lifecycle work intersects with server firmware, drivers, storage, network, certificates, identity, backup agents, operations integrations, and application maintenance. Compatibility and rollback must be tested as a system.

Recovery Depends on More Than Data Copies

Successful backup jobs do not prove recoverability. Service restoration may require identity, DNS, certificates, network policy, automation, management services, application sequencing, external integrations, and business validation. Recovery tests should prove the complete dependency chain.

Multicloud Consistency Has a Limit

Common policy and governance are useful, but cloud-specific services retain different failure models, identity behavior, networking, economics, observability, and support processes. Standardize the contract where possible, and preserve platform-specific expertise where necessary.

Operational Readiness Checklist

Before declaring the platform ready for the next lap, verify the following:

• every service profile has a named owner and consumer
• workload-domain placement is intentional and documented
• compute, data, network, security, and recovery requirements are measurable
• automation includes validation and rollback, not provisioning alone
• exceptions have owners, expiration dates, and review paths
• capacity models include maintenance and failure headroom
• VCF and infrastructure lifecycle dependencies are documented
• operations views correlate service and component health
• critical alerts have an owner and a tested action
• microsegmentation policy is based on application dependency and reviewed regularly
• primary storage protection is not being mistaken for independent backup
• restore and disaster-recovery tests prove the service dependency chain
• each platform profile has a cost and complexity justification
• evidence from incidents and changes updates the standard patterns

This checklist is intentionally operational. Architecture is complete only when the organization can run, change, secure, and recover the design under real conditions.

Conclusion

The multicloud grand prix image succeeds because it reframes infrastructure as a coordinated operating system. The VCF car, Dell pit crew, NSX security boundary, automation controls, operations telemetry, and workload-domain scoreboard are valuable only when they behave as one service-delivery model.

VMware Cloud Foundation provides the race-control structure. VCF Automation governs repeatable release. VCF Operations turns telemetry into decisions. NSX defines communication boundaries. PowerEdge supplies compute. PowerFlex, PowerScale, PowerStore, and PowerMax provide differentiated data-service profiles. Independent recovery controls ensure that availability problems do not become irreversible business events.

The architecture decision is not to deploy every product shown in the image. The decision is to define a small set of supported platform patterns, assign clear ownership, encode policy, integrate evidence, and prove lifecycle and recovery behavior before demand becomes urgent.

That is how a private cloud platform wins. Not through one spectacular lap, but through repeatable service delivery when the weather changes, the workload spikes, and the next pit stop cannot be improvised.

External References

• Broadcom TechDocs: What Is VMware Cloud Foundation?
• Broadcom TechDocs: Architectural Options in VMware Cloud Foundation
• Broadcom TechDocs: VCF Automation Overview
• Broadcom TechDocs: VCF Operations Detailed Design
• Broadcom TechDocs: Working with Micro-Segmentation
• Dell Technologies Info Hub: Using Dell PowerFlex with VMware Cloud Foundation 9.0
• Dell Technologies: PowerEdge Data Center Compute Servers
• Dell Technologies: PowerScale – Scale-out NAS Storage
• Dell Technologies: PowerStore Scalable All-Flash Array Storage
• Dell Technologies: PowerMax – Enterprise NVMe Storage

The post VCF on Dell Infrastructure: Platform Roles and Change Readiness appeared first on Digital Thought Disruption.