For ESXi host-side crash analysis the two indispensable log files are produced by the VMkernel itself:

• vmkernel.log – records scheduler, storage, network, device-driver and CPU exception details that directly reveal panic or PSOD causes.

• vmkwarning.log – a real-time subset of vmkernel messages containing warnings and non-fatal hardware errors that often precede a crash.

VMware and HPE service guides instruct collecting these two files first when a ProLiant server running ESXi experiences a purple-screen or unexpected reboot.

C. BIOS logs – Stored by iLO/BIOS, helpful for firmware or POST issues but they do not capture VMkernel panics occurring after ESXi boots.

D. Lustre logs – Lustre is a parallel file system for HPC; its logs exist only when the software is installed and are unrelated to a vanilla ESXi hypervisor crash.

1. VMware KB 1004795 “Location of ESXi log files

” Table 1 (vmkernel.log & vmkwarning.log listed as primary diagnostic files for PSOD)

2. VMware vSphere 8.0 Documentation > Monitoring and Troubleshooting > “Collecting ESXi Host Log Files”

section “Kernel and Warning Logs”

para 2.

3. HPE ProLiant Gen10 Troubleshooting Guide for VMware ESXi

chap 6 “Purple Screen of Death (PSOD)”

pp. 95-96 (lists vmkernel.log & vmkwarning.log as mandatory attachments for HPE Support).

4. MIT OpenCourseWare 6.033 “System Diagnostics” lecture notes

slide 14 (uses ESXi vmkernel/vmkwarning examples for crash forensics).

HPE Private Cloud AI is built upon a cloud-native architecture using Kubernetes to orchestrate AI and machine learning workloads. To manage specialized hardware like GPUs efficiently, Kubernetes employs the device plugin framework. This framework allows hardware vendors (such as NVIDIA) to advertise their resources (e.g., GPUs) to the kubelet on a node. The Kubernetes scheduler can then account for these resources, enabling it to place pods that request GPUs onto the appropriate nodes, ensuring efficient allocation and utilization for demanding AI tasks.

B. Kubernetes ConfigMaps: ConfigMaps are used to store and manage non-sensitive configuration data for applications, not for discovering or scheduling hardware resources like GPUs.

C. Kubernetes DaemonSets: A DaemonSet ensures a pod runs on all or specific nodes. While a GPU device plugin might be deployed using a DaemonSet, the DaemonSet itself is a deployment controller, not the feature that manages the GPU resource.

D. Pod autoscaling only: Pod autoscaling (e.g., HPA) adjusts the number of pod replicas based on metrics. It does not manage the underlying hardware discovery, advertisement, or allocation of GPUs to pods.

1. Kubernetes Documentation

"Device Plugins": "Kubernetes provides a device plugin framework to enable Pods to access specialized hardware features such as GPUs... Without a device plugin

the Kubernetes scheduler is unaware of the device-specific resources on a Node

and is unable to schedule Pods that require those resources." (Source: Kubernetes.io official documentation

kubernetes.io/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/)

2. Kubernetes Documentation

"Schedule GPUs": "Kubernetes includes stable support for managing NVIDIA GPUs across different nodes in a cluster... you can schedule Pods that request nvidia.com/gpu resources. To enable this

you must have the appropriate NVIDIA drivers installed on the nodes and you must run the corresponding Kubernetes device plugin from NVIDIA." (Source: Kubernetes.io official documentation

kubernetes.io/docs/tasks/manage-gpus/scheduling-gpus/)

3. HPE Private Cloud AI Solution Brief: "HPE Private Cloud AI is a turnkey solution... built on a modern

cloud-native architecture [Kubernetes]... It provides self-service access to shared resources

including powerful NVIDIA GPUs

for AI model development and tuning." This establishes the context that the solution uses Kubernetes and manages GPUs

for which the standard mechanism is the device plugin. (Source: HPE Document a00138329enw

Page 2

"Solution overview")

The HPE Moonshot system's fundamental design principle is to deliver high-density, energy-efficient computing through a chassis that houses numerous lightweight, workload-optimized server cartridges. This architecture is ideal for workloads that can be massively parallelized, such as certain AI inference, data analytics, and high-performance computing (HPC) tasks. The primary advantage is achieving high throughput by distributing tasks across many power-efficient nodes, leading to significant reductions in total cost of ownership (TCO) through lower power, cooling, and space requirements compared to traditional scale-out servers.

A. High GPU density is a characteristic of systems like the HPE Apollo 6500, which are specifically designed for GPU-accelerated AI training and HPC, not the primary design focus of Moonshot.

C. Integration with SAP HANA is incorrect. SAP HANA is a large, in-memory database workload that requires scale-up systems with large memory capacity, such as HPE Superdome Flex.

D. Focus on archival storage is incorrect. HPE Moonshot is a compute platform. Archival storage solutions are part of the HPE storage portfolio, such as HPE StoreEver tape libraries.

1. HPE Moonshot System Family Guide: "The HPE Moonshot System uses an innovative new architecture that results from one simple design tenet: to align the right workload with the right compute... It provides a very dense

very low power compute platform..." This document emphasizes the core value proposition of density and energy efficiency with many nodes for specific workloads. (Reference: HPE Moonshot System Family Guide

Page 2

"Introduction").

2. HPE Moonshot 1500 Chassis QuickSpecs: "The HPE Moonshot 1500 Chassis is a 4.3U form factor that can hold up to 45 server cartridges... The chassis shares power

cooling

management

and fabric for all the server cartridges." This specification highlights the physical implementation of many lightweight nodes sharing resources

which is the basis for energy-efficient parallelism. (Reference: HPE Moonshot 1500 Chassis QuickSpecs

Page 1

"What's New" and "Standard Features").

HPE Private Cloud AI with NVIDIA is an integrated, full-stack solution designed for AI workloads. The software layer, which includes HPE AI Essentials and NVIDIA AI Enterprise, is architected on cloud-native principles. Kubernetes serves as the foundational container orchestration platform. It automates the deployment, scaling, and management of containerized AI applications and machine learning models, providing the necessary agility and resilience for enterprise-grade AI development and inference at scale.

A. OpenShift is a specific enterprise distribution of Kubernetes. While compatible, Kubernetes is the more fundamental, underlying orchestration technology itself.

B. VMware ESXi is a Type-1 hypervisor used for creating and running virtual machines, not a platform for orchestrating containerized AI workloads.

D. Docker Swarm is a container orchestration tool, but the HPE and NVIDIA solution is standardized on the more widely adopted Kubernetes ecosystem for enterprise AI.

1. HPE Machine Learning Development Environment Solution Brief: "Built on the industry-leading open-source platform

Kubernetes

HPE Machine Learning Development Environment enables you to easily prepare

build

train

and deploy your models."

Source: HPE Machine Learning Development Environment - Solution Brief (Document ID: a00103888enw)

Page 2

"A Modern Software Platform for AI at Scale".

2. NVIDIA AI Enterprise Documentation: NVIDIA AI Enterprise is a cloud-native suite of AI software that is optimized for deployment on Kubernetes

which is the orchestration layer used in the joint HPE-NVIDIA solution.

Source: NVIDIA AI Enterprise - Technical Brief (Document ID: TB-10221-001v5)

Page 4

"NVIDIA AI Enterprise is optimized for mainstream data center platforms

including bare metal servers and virtualized infrastructure with Kubernetes."

HPE Pointnext Services is the technology services organization within HPE that assists customers with their digital transformation, including AI initiatives. It provides a portfolio of advisory, professional, and operational services. These services are specifically designed to help customers strategize, implement, and adopt new technologies. This includes workshops and consulting engagements aimed at upskilling internal teams and ensuring the successful integration and adoption of AI projects within the organization, which aligns directly with the concept of "HPE AI Essentials."

HPE StoreVirtual VSA is a software-defined storage solution, not a service focused on team skills or project adoption.

HPE ProLiant iLO is an embedded server management technology for remote administration, not a consulting or educational service.

HPE NimbleOS is the operating system for HPE Nimble Storage arrays; it is a product, not a service for AI adoption.

1. HPE Pointnext Services for Artificial Intelligence

Data Sheet (a00053681enw.pdf): Page 1 states

"HPE Pointnext provides a world-class advisory and professional services team to collaborate with you on your AI journey... Our services help you get started with AI

build and accelerate your solutions

and scale your AI capabilities." This directly supports the role of Pointnext in AI project adoption and upskilling.

2. HPE Education Services for Artificial Intelligence

Data Sheet (a00094218enw.pdf): Page 1 details the training offerings

stating

"HPE Education Services offers comprehensive training designed to help you and your team develop the skills needed to successfully implement and manage your AI and big data solutions." This confirms Pointnext's role in upskilling teams.

3. HPE Pointnext Services Official Web Page: The "Advisory & Professional Services" section describes how HPE helps customers "reimagine and redefine experiences

and accelerate complex transformations." This includes strategic workshops and implementation services essential for AI project adoption. (Referenced from the official HPE website under Services > Advisory & Professional Services).

HPE GreenLake for High Performance Computing (HPC) delivers a fully managed, on-premises HPC service with a flexible, pay-per-use consumption model. This approach brings the agility and financial model of the public cloud to a customer's own data center or colocation facility. It eliminates the need for large upfront capital investment and the complexities of managing HPC infrastructure. Customers gain access to purpose-built HPC systems, software, and expertise, paying only for the capacity they consume. This allows organizations to scale resources up or down based on demand, accelerating time to insight for complex research and data-intensive workloads.

B. HPE GreenLake provides the underlying HPC infrastructure (hardware and platform software) but does not bundle specific third-party GPU design tools as its core offering.

C. While the service can be deployed in a colocation facility, HPE GreenLake's primary value is the managed, consumption-based service model, not the physical data center hosting itself.

D. Partner promotions like cloud credits are not the fundamental service. The core offering is the consumption-based infrastructure model, independent of any temporary or specific partner benefits.

1. HPE GreenLake for High Performance Computing Solution Brief (a00109698enw

March 2022): Page 2

"The HPE GreenLake edge-to-cloud platform for HPC" section states

"HPE GreenLake for HPC is a fully managed service that allows you to get the HPC experience you need... with an on-premises solution... You get a fully customized HPC solution

with a pay-for-what-you-use payment model." This directly supports the on-premises

cloud-like consumption model.

2. HPE GreenLake for High Performance Computing (HPC) Data Sheet (a50002549enw

October 2022): Page 1

under "Benefits

" it highlights

"Avoid large upfront costs and align cash flow to usage with a flexible

pay-per-use model." This confirms the consumption-based nature of the service.

3. HPE Official Website

"HPE GreenLake for High Performance Computing": The overview section frequently describes the service as delivering "the cloud experience on-premises" and enabling customers to "pay only for what you use." This reinforces the core concept of an on-premises

cloud-like consumption model. (Refer to the main product page for HPE GreenLake for HPC).

The HPE + NVIDIA AI Enterprise solution stack includes NVIDIA's Data Center GPU Manager (DCGM) for comprehensive, real-time health and performance monitoring of GPUs. DCGM exposes hundreds of metrics that are crucial for observing AI workloads. These metrics are typically scraped by a time-series database like Prometheus and then visualized using Grafana dashboards. This combination provides the necessary observability into GPU utilization, memory usage, power consumption, and other key performance indicators essential for managing and optimizing AI infrastructure.

B. MS Word charts: MS Word is a word processing application and is not a tool for real-time monitoring or observability of IT workloads.

C. Windows Event Viewer: This is a component of the Windows OS for viewing system logs; it is not a specialized tool for observing GPU-centric AI workload metrics.

D. Citrix Workspace: This is a digital workspace solution for delivering applications and desktops to end-users, unrelated to infrastructure or AI workload monitoring.

1. NVIDIA AI Enterprise Deployment Guide (v5.0): In the section "Monitoring

" it states

"NVIDIA AI Enterprise includes features for monitoring the health and performance of GPUs... These metrics can be integrated with popular monitoring and alerting solutions like Prometheus and Grafana." This directly confirms the use of Grafana for observability. (Source: NVIDIA AI Enterprise Documentation).

2. NVIDIA DCGM Documentation - Integration with Prometheus and Grafana: This official documentation provides a dedicated section on how to use the dcgm-exporter to feed GPU metrics into Prometheus and visualize them with pre-built or custom Grafana dashboards. This is the standard

documented method for achieving observability in this environment. (Source: NVIDIA DCGM Documentation).

3. HPE Reference Configuration for NVIDIA AI Enterprise on HPE ProLiant Servers: These documents detail the validated software stack. In the "Software Overview" or "Management and Operations" sections

they describe the inclusion of the NVIDIA AI Enterprise suite

which contains the necessary monitoring tools (like DCGM) that integrate with platforms like Grafana for a complete observability solution. (e.g.

Document ID: a50002213enw).

HPE OneView provides a unified API that enables infrastructure-as-code, allowing for the automated provisioning and lifecycle management of compute, storage, and networking resources. This is crucial for deploying and scaling third-party container platforms like Kubernetes or Red Hat OpenShift.

The HPE iLO RESTful API, which is based on the Redfish standard, offers direct, agentless, and secure programmatic control over individual HPE servers. This allows for the automation of bare-metal server configuration, firmware updates, and monitoring, which are foundational tasks for preparing nodes for a container cluster.

C. HPE StoreOnce: This is a data protection and backup solution. While it may integrate with container environments for backup, it is not a primary tool for infrastructure automation.

D. VMware Horizon: This is a virtual desktop and application platform developed by VMware, not an HPE solution for container automation.

1. HPE White Paper: Automating and orchestrating HPE infrastructure at scale with Red Hat Ansible Automation Platform. (Document ID: a00098169enw

Published: April 2020).

Page 4

"HPE and Red Hat Ansible Automation Platform" section: "The HPE OneView and HPE iLO Ansible modules enable IT to treat ‘infrastructure as code’ to automate the provisioning of bare-metal infrastructure for virtualization

containers (for example

Red Hat OpenShift)

and enterprise workloads." This statement directly supports that both HPE OneView and HPE iLO are used for API-driven automation for container platforms.

2. HPE Developer Portal: HPE OneView API.

Overview Section: "HPE OneView allows you to control your infrastructure programmatically with a single

unified API... It is the foundation for a software-defined infrastructure and the enabler for infrastructure-as-code." This confirms OneView's role as the primary API for infrastructure automation. The portal provides extensive documentation on integrating with tools used for container orchestration.

3. HPE Developer Portal: iLO RESTful API.

Overview Section: "The iLO RESTful API is a management interface that server management tools can use to perform server configuration

inventory

and monitoring tasks... The iLO RESTful API is Redfish API conformant." This establishes the iLO API as the tool for programmatic

bare-metal server automation

a prerequisite for deploying container hosts.

HPE OneView Global Dashboard is designed to provide a unified, aggregated view of infrastructure health, inventory, and alerts from multiple HPE OneView and HPE Synergy instances, potentially across different data centers. Since HPE OneView itself integrates with third-party virtualization managers (like VMware vCenter, Microsoft SCVMM) and container platforms, the Global Dashboard consolidates this information. This creates the "single-pane-of-glass" management experience described in the question, spanning both the physical HPE compute infrastructure and the virtual/container layers it supports.

B. vSphere Client: This tool is specific to managing VMware vSphere environments and does not provide a unified view of HPE hardware and other non-VMware platforms.

C. Red Hat Satellite: This is a lifecycle management tool specifically for Red Hat Enterprise Linux systems, not a general-purpose infrastructure or multi-vendor virtualization management console.

D. Docker CLI: This is a command-line interface used exclusively for managing Docker containers and their immediate environment, not for broad infrastructure or hypervisor management.

1. HPE OneView Global Dashboard Datasheet: "HPE OneView Global Dashboard provides a unified view of the health and inventory of HPE servers

profiles

enclosures

frames

and racks across multiple HPE OneView and HPE Synergy appliances... This enables IT administrators to manage their HPE infrastructure at scale from a single pane of glass." (Hewlett Packard Enterprise

Document a00027125enw

Published March 2023

Page 1).

2. HPE OneView 8.3 User Guide for VMware vCenter: This document details the integration that forms the basis for the consolidated view. "The HPE OneView for VMware vCenter with Operations Manager and Log Insight is an appliance that integrates the manageability features of HPE ProLiant

HPE BladeSystem

HPE Synergy

and HPE Apollo servers with VMware solutions." (Hewlett Packard Enterprise

Document P11545-017

Published July 2023

Page 7

"Introduction"). The Global Dashboard then aggregates views from multiple such integrated instances.

HPE AI Essentials is a portfolio designed to simplify and accelerate the adoption of Artificial Intelligence. Two key components that directly contribute to accelerating a customer's time-to-value are HPE AI Blueprints and the HPE AI Infrastructure Advisor.

HPE AI Blueprints are pre-validated, full-stack reference architectures that reduce the complexity, risk, and time associated with designing and deploying AI infrastructure.

HPE AI Infrastructure Advisor is a tool that uses data-driven analysis to recommend the optimal infrastructure configuration for specific AI workloads, streamlining the planning and procurement process.

Both components eliminate guesswork and reduce manual effort, directly shortening the project lifecycle from planning to production.

C. GPU clock speed tuning: This is a granular performance optimization task performed post-deployment, not a foundational component of the HPE AI Essentials portfolio for accelerating initial value.

D. BIOS overclocking: This is a low-level hardware modification that is generally unsupported and not recommended for stable, enterprise-grade production environments due to reliability risks.

1. HPE AI Infrastructure Advisor for HPE GreenLake

Solution Brief. (Document ID: a50008913enw

March 2023). Page 1

"Accelerate time to value" section states

"The HPE AI Infrastructure Advisor for HPE GreenLake helps you accelerate time to value by providing a data-driven recommendation for the optimal infrastructure configuration..."

2. HPE Solutions for AI

Solution Brief. (Document ID: a00111927enw

June 2021). Page 2

under the heading "Simplify and accelerate your AI journey..."

the document highlights the value of "Proven enterprise AI blueprints" as part of the full-stack solution.

3. HPE Reference Architecture for Generative AI on HPE Cray Supercomputers. (Document ID: a50009093enw

June 2023). Page 3

Introduction. This document

an example of a blueprint

explains that its purpose is to provide a "prescriptive approach" to "accelerate deployment and simplify operations."