Cloud And High Performance Computing Strategy

The following discussion illustrates a project that is well suited to the capabilities of an independent consultant in the Umbrex Shipbuilding & marine systems Practice. This is an illustrative example. Umbrex consultants adapt their methodology, timeline, and deliverables to the specific needs of each client.

1) Client Situation

The client operated a multi-yard shipbuilding enterprise within the Aerospace & Defense sector, requiring a Cloud And High Performance Computing Strategy: Define hybrid cloud and high-performance computing architecture for CAD, CAE, and digital twins; optimize cost, resilience, and latency across multi-yard operations. The engineering organization relied on model-based enterprise processes spanning MBSE, CAD (e.g., CATIA/3DEXPERIENCE, Siemens NX), CAE (e.g., Ansys, Abaqus, Nastran, STAR-CCM+, OpenFOAM), and an evolving digital twin stack for hull structures, propulsion, hydrodynamics, acoustics, and shipboard systems. Several pain points had emerged at the intersection of Information Technology and shipyard operations:

• Fragmented compute and storage footprint: Each yard maintained separate HPC clusters and file systems with inconsistent scheduler configurations (SLURM vs. PBS Pro), resulting in low average utilization during off-peak phases and queue bottlenecks during design gates. KPI underperformance included core-hour utilization below target and CAE queue wait times exceeding acceptable thresholds.
• Latency-sensitive CAD/PLM access: Engineering teams at satellite yards and afloat maintenance sites experienced high latency over MPLS/SD-WAN links to centralized PLM/PDM vaults (Teamcenter and 3DEXPERIENCE), leading to slow model open/save times and degraded VDI session performance (NICE DCV/Teradici PCoIP). KPIs such as CAD model open time and VDI frame rate consistently lagged benchmarks for interactive design.
• Inconsistent digital thread: Data handoffs between MBSE (SysML), requirements (DOORS), CAD/CAE, PLM, MES (DELMIA/Opcenter), and ERP (SAP/IFS) introduced duplication and version drift, undermining the digital twin. Change order lead times and drawing release cycle times exceeded governance thresholds, with limited traceability coverage across the lifecycle.
• Security and compliance exposure: HPC and cloud workloads lacked a unified enclave strategy aligned to ITAR, DFARS, CMMC, and NIST 800-171. The Authority to Operate (ATO) boundary definitions, logging/monitoring, and enclave segmentation were inconsistent across yards. CMMC readiness assessments flagged gaps in access control, key management, and vulnerability remediation cadence.
• Rising compute cost and constrained capacity: Capital cycles struggled to keep pace with GPU/CPU demand curves driven by CFD/FEA workloads and emerging ML models for weld quality and anomaly detection. Cloud usage was ad hoc, with opaque cost allocation, egress fees, and underutilized reserved capacity. Cost per simulation and cost per core-hour exceeded planning assumptions.
• Storage and data movement friction: Parallel file systems (Lustre, GPFS/Spectrum Scale, BeeGFS) showed performance hotspots and metadata contention. Replication across yards created lengthy synchronization windows and inconsistent RPO/RTO. Data movement into/out of cloud lacked policy-driven tiering and WAN acceleration, inflating cycle times.
• License inefficiency: FlexLM token pools for CAD/CAE were over-provisioned at some yards and starved at others. License checkout failures, denials, and idle license time reduced engineering throughput. License utilization KPIs were not consolidated across sites for proactive rebalancing.
• Operational resilience gaps: Backup, DR, and cyber recovery capabilities varied by site. Some clusters lacked immutable snapshots and air-gapped backups. RTO/RPO targets for PLM and CAE environments were undefined or inconsistent with program criticality.
• Toolchain sprawl and version drift: Heterogeneous tool versions and plugin stacks (CAD, CAE solvers, meshing, pre-/post-processing) created support burden and rework. Validation and verification workflows were inconsistent, complicating configuration management for naval programs.
• Talent and operating model constraints: Engineering IT, HPC admins, and yard IT teams operated in siloes with unclear RACI for cloud landing zones, scheduler policies, and data governance. Change management for new workflows, including containerized solvers and spot-instance pilots, lacked sponsorship and training pathways.

These issues manifested in measurable underperformance of critical KPIs, including: higher-than-target CAE queue wait percentage relative to wall-clock time; core-hour utilization variability across yards; CAD/PLM open/save latency breaches; VDI session performance below vendor-recommended thresholds for interactive CAD; elevated cost per simulation run; extended change order cycle times; incomplete digital thread traceability; and unaddressed compliance findings in CMMC readiness scorecards.

2) Project Objective

Primary objective: We defined and validated a hybrid cloud and high-performance computing strategy and architecture for CAD, CAE, and digital twins that optimized cost, resilience, and latency across multi-yard operations under Aerospace & Defense security and compliance constraints.

• Secondary objectives:
  • Reduce time-to-engineering throughput by right-sizing and orchestrating HPC resources across on-prem clusters and IL5/IL6 cloud environments with policy-based bursting.
  • Improve cost control via workload profiling, chargeback/showback, license optimization, and cloud pricing constructs (reserved/spot/committed use) aligned to program schedules.
  • Strengthen cyber posture and compliance with a standardized landing zone, enclave segregation, zero-trust principles, and audit-ready logging aligned to CMMC and ITAR.
  • Enhance user experience for latency-sensitive CAD/PLM/VDI through network path optimization, edge caching, and proximity-based rendering strategies.
  • Enable the digital thread by establishing data architecture patterns for model lineage, metadata, and governed movement between PLM, HPC, MES, ERP, and analytics platforms.
  • Increase operational resilience with harmonized backup/DR patterns, immutable storage tiers, and tested recovery runbooks for PLM and CAE environments.
  • Build internal capability through a federated operating model, upskilling, and a migration factory playbook that scaled beyond initial pilots.

3) Methodology and Approach

We organized the engagement into integrated workstreams that addressed architecture, workloads, data, security, and change management. Each workstream employed targeted analyses and design activities to enable risk reduction, KPI improvement, and acceleration of time-to-value, without asserting final results.

Workstream 1: Current-State Architecture and KPI Baseline

We developed an end-to-end view of the Information Technology landscape supporting engineering and yard operations. Activities included:

• Inventory of compute, storage, and network assets across yards: HPC clusters (CPU/GPU generations, node counts), storage (Lustre, GPFS, NetApp ONTAP, Pure), interconnects (InfiniBand, Ethernet, RDMA), and WAN design (MPLS, SD-WAN, QoS policies).
• Application and data mapping: CAD/CAE tools, pre/post processors, PLM/PDM vault topology, license servers (FlexLM), VDI brokers (NICE DCV, Teradici), and data flows to MES (DELMIA/Opcenter), ERP (SAP/IFS), and analytics/data lake platforms.
• KPI and telemetry baseline: SLURM/PBS job logs, queue wait distributions, wall-clock vs. run-time ratios, utilization heatmaps, storage IOPS/throughput/metadata rates, VDI latency and frame metrics, PLM transaction timings, and license denial rates.
• Compliance and security posture review: ATO boundary documentation, SSP and POA&M review, identity and access management (IdP, MFA, privileged access), vulnerability management cadence, and logging coverage against NIST 800-171/CMMC controls.
• Tools and techniques: CMDB analysis, NetFlow/SNMP telemetry, synthetic transaction testing for PLM/VDI, packet captures for WAN path analysis, and a consistent KPI taxonomy for comparison across sites.

This workstream enabled a shared fact base to prioritize gaps impacting cost, resilience, and latency, forming the basis for subsequent design decisions.

Workstream 2: Workload Profiling and Economic Modeling

We profiled engineering workloads to quantify the resource shape, performance sensitivity, and cost drivers. Activities included:

• Job characterization: Classification of solver types (CFD, FEA, acoustics, hydrodynamics, thermal, electromagnetics), parallelization patterns (MPI/OpenMP), GPU utilization, typical core counts, memory footprints, I/O patterns (checkpointing, scratch, post-processing), and license token consumption profiles.
• Performance sensitivity analysis: Benchmarking against representative meshes and model sizes to understand CPU/GPU scaling curves, interconnect requirements (InfiniBand vs. Ethernet), and storage throughput/metadata bottlenecks (e.g., STAR-CCM+ checkpoint I/O).
• Economic model: Total cost of ownership for on-prem clusters (capex, support, power/cooling, floor space, admin FTEs) vs. cloud HPC (compute types, committed use, spot, storage tiers, egress, data transfer, license model impacts). We built a time-phased model aligned to program gates and yard demand cycles.
• License optimization: Analysis of FlexLM logs, token pools, denials, idle time, and global sharing policies, with scenarios for license server consolidation and token-based budgeting by program.
• Tools and frameworks: TEA (techno-economic analysis), queue simulation models, capacity planning calculators, and unit economics (cost per core-hour, cost per simulation, cost per design loop).

This workstream created the economic rationale and workload placement logic necessary to optimize cost without compromising schedule, quality, or compliance.

Workstream 3: Target-State Hybrid Cloud and HPC Reference Architecture

We designed a target-state architecture aligned to Information Technology strategy and the multi-yard operating model. Activities included:

• Reference patterns: On-prem HPC clusters for steady-state throughput, cloud HPC bursting for peak demand and specialized GPU workloads, and enclave segmentation for ITAR/CUI with IL5/IL6 landing zones (e.g., Azure Government, AWS GovCloud).
• Compute and scheduling: Standardized SLURM configuration across clusters, fair-share policies, QoS tiers by program criticality, and federation mechanisms to enable transparent bursting (e.g., Slurm Federation, CycleCloud/ParallelCluster integration).
• Storage architecture: High-performance parallel file systems for scratch and solver working sets; enterprise NAS for PLM workspaces; object storage for cold/archive (S3/Blob with lifecycle policies); and data movers (HPC Cache, DataSync, ISILON SmartSync) with checksum validation.
• Network and latency: SD-WAN with application-aware routing, QoS prioritization for VDI/PLM, WAN acceleration for PLM transactions, regional edge nodes for rendering and edge cache of CAD assemblies, and proximity-based brokering of VDI sessions.
• Virtual Desktop and visualization: NICE DCV/PCoIP session placement near data, GPU-backed visualization nodes both on-prem and in cloud, and brokers that considered MFA and enclave boundaries.
• Resilience and DR: Immutable backups for PLM/HPC metadata, cross-yard replication with snapshot orchestration, RPO/RTO targets by system tier, and DR runbooks validated through tabletop exercises.
• Toolchain and containerization: Containerized CAE solvers where permitted (Singularity/Apptainer), module environments for reproducibility, golden machine images for CAD/PLM/VDI, and CI/CD for HPC applications with compliance gates.

The reference design aligned stakeholders on the end-state blueprint and guided subsequent build-out and pilots.

Workstream 4: Security, Compliance, and Landing Zone Engineering

We implemented a compliant cloud foundation and hardened on-prem segments consistent with defense-grade requirements. Activities included:

• Landing zone design: Guardrails for identity (federated IdP, conditional access, MFA), network segmentation (hub-spoke, private endpoints), key management (HSM-backed CMKs), logging (SIEM integration), and policy-as-code to enforce tagging and encryption.
• CMMC/NIST alignment: Control mapping for access control, configuration management, vulnerability remediation SLAs, incident response runbooks, and audit trail retention; updated SSP and POA&M content.
• Data classification and handling: Marking and routing rules for ITAR/CUI, cross-domain solutions planning where applicable, and encryption-in-transit/at-rest standards across on-prem and cloud.
• Supply chain and vendor posture: Due diligence on cloud/HPC partners, software SBOM expectations, and export control considerations for GPU procurement and solver libraries.
• Zero-trust measures: Micro-segmentation for HPC nodes, just-in-time privileged access, bastion patterns, and continuous compliance scanning for hardened images.

This workstream reduced authorization risk and established a reusable security architecture compatible with multi-yard operations and federal oversight.

Workstream 5: Data Architecture for Digital Thread and Digital Twin

We addressed the data layer required to sustain model lineage and analytics across CAD, CAE, and the digital twin. Activities included:

• Metadata and lineage model: Canonical identifiers for assemblies, meshes, solver runs, post-processing artifacts, and change objects; linkage between requirements (DOORS), MBSE (SysML), CAD/PLM objects, and CAE results.
• Data lakehouse integration: Ingest patterns for solver outputs (HDF5, CSV, VTK), telemetry from integration rigs and sea trials, and PLM metadata; schema and governance in a secure enclave with role-based access.
• Data movement orchestration: Policy-driven pipelines between PLM workspaces, HPC scratch, object tiers, and analytics environments; checksum verification and automated retention policies.
• APIs and interoperability: Connectors for Teamcenter and 3DEXPERIENCE, event-driven subscriptions for change notices, and standardized result catalogs to enable ML feature stores and dashboards.

This data architecture enabled consistent traceability and faster retrieval of historical analyses to accelerate future design loops and certification packages.

Workstream 6: Network, Latency, and User Experience Optimization

We optimized the end-to-end path for latency-sensitive workflows. Activities included:

• WAN path diagnostics: Hop-by-hop measurement of latency, jitter, and packet loss for VDI and PLM transactions; identification of congestion points and suboptimal routing.
• VDI tuning: Codec selection, frame pacing, and GPU profile tuning; placement of brokers and session hosts near data; session pre-launch for heavy users at shift start; and endpoint QoS policies.
• Edge caching and file acceleration: Deployment of PLM-aware caching proxies, SMB/NFS tuning (MTU, credits, TCP windows), and HTTP/2 acceleration for API-heavy PLM operations.
• SD-WAN policies: Application identification, traffic steering based on real-time performance, and brownout mitigation via secondary links.

We linked these changes to measurable improvements in CAD model open/save times and interactive responsiveness, enabling stable productivity across yards.

Workstream 7: License, Capacity, and Workload Orchestration

We harmonized license pools and scheduling policies to increase throughput and predictability. Activities included:

• Global license service: Consolidated FlexLM services with resilient topology, usage dashboards, and policy-based reservation of tokens for critical programs.
• Scheduler policy alignment: QoS classes and fair-share weights aligned to program milestones; preemption for best-effort jobs; and backfill strategies to maximize utilization.
• Bursting orchestration: Placement policies based on queues, license availability, and data locality; job wrappers to select optimal compute targets; and automated data staging and cleanup.
• Change windows and maintenance: Coordinated patching/firmware cycles to avoid quarter-end crunch periods; configuration drift control via infrastructure-as-code.

This workstream supported predictable engineering schedules while controlling cost and minimizing idle capacity.

Workstream 8: Pilot, Migration Factory, and Operating Model

We executed pilots and established a repeatable migration factory with clear roles and responsibilities. Activities included:

• Pilot selection: Representative CFD and FEA workloads with known performance signatures, including GPU-accelerated solvers and IO-intensive post-processing.
• Runbook development: Standardized patterns for containerized and VM-based solver execution, data staging, checkpointing, and failure handling.
• Operating model: RACI for Engineering IT, central IT, HPC admins, and yard IT; service catalog for bursting requests; change management and training assets for engineers and admins.
• Governance: Architecture review board cadence, KPI dashboards for executive oversight, and investment stage-gates tied to performance, cost, and compliance validation.

These activities established a scalable path to expand adoption across programs and sites.

4) Data Request

To execute the project effectively, we requested detailed data across commercial, operational, financial, technical, regulatory, people, and systems domains. We specified time horizons, granularity, and common pitfalls.

• Commercial and program:
  • Program schedules and gate calendars (12–36 months), including critical design reviews, drawing release milestones, and trial windows.
  • Projected engineering workload by program and discipline (monthly granularity), including anticipated CFD/FEA runs and mesh sizes.
• Operational and engineering:
  • HPC job logs (SLURM/PBS) with job IDs, user, queue, core/GPU count, runtime, wait time, memory usage, IO metrics (per job) for 12–24 months.
  • VDI telemetry (NICE DCV/PCoIP): session latency, FPS, bandwidth, disconnections (daily/hourly).
  • PLM transaction logs: open/save times, check-in/out, replication status (per transaction).
  • License server logs (FlexLM): token usage, denials, checkout duration (per feature, hourly granularity).
  • Network telemetry (NetFlow, SNMP): link utilization, latency, jitter across inter-yard paths (5-minute intervals).
  • Storage metrics: IOPS, throughput, latency, metadata ops, capacity, and snapshot schedules for parallel FS and NAS (hourly/daily).
• Financial and cost:
  • On-prem TCO: asset inventory, depreciation, support contracts, power/cooling, floor space, admin FTEs (annual).
  • Cloud billing and usage: compute/storage/network cost by account/subscription (daily granularity), including reserved/spot commitments and egress fees.
  • License costs and maintenance by tool/vendor (annual) with contract terms and true-up schedules.
• Technical/engineering:
  • Solver portfolios and versions, benchmark input decks, typical mesh sizes, and scaling characteristics.
  • Image/OS baselines for CAD/CAE/PLM/VDI; patch cadence and hardening standards.
  • Interconnect specs (InfiniBand bandwidth, Ethernet speeds), node configurations, and GPU profiles.
• Regulatory/ESG:
  • SSP, POA&M, recent audit findings (CMMC/NIST 800-171), ATO boundary documents.
  • Data classification policies (CUI/ITAR markings), data retention requirements, and incident response runbooks.
• People/governance:
  • Organization charts and RACI for Engineering IT, central IT, HPC admins, security, and yard IT.
  • Change advisory board schedules, release calendars, and training plans.
• Systems and integrations:
  • ERP (SAP/IFS), MES (DELMIA/Apriso, Siemens Opcenter), PLM (Teamcenter, 3DEXPERIENCE), PDM vault configurations, requirements (DOORS), MBSE tooling, CMDB exports.
  • Data lake/datalakehouse architecture, pipeline definitions, and catalog/metadata tools.

Common data quality pitfalls we flagged included: inconsistent time zones and clock drift across log sources, missing job-level IO metrics, incomplete tagging on cloud resources, duplicate user IDs across yards, gaps in license log retention, and PLM replication logs lacking transaction identifiers for correlation.

5) Questions for Client

• What is the target ambition for cloud adoption across CAD/CAE/digital twin (e.g., cloud-eligible workloads, on-prem steady-state, bursting guardrails), and what are non-negotiable constraints?
• Which compliance posture is required for engineering compute (CMMC level, ITAR boundary), and what is the current ATO status for prospective cloud environments?
• What latency thresholds are acceptable for interactive CAD/PLM and VDI sessions by user cohort and yard, and where are the most critical hotspots today?
• How should capacity be prioritized across programs during peak demand—by milestone criticality, contractual penalties, or engineering throughput metrics?
• What are the capital vs. operating expenditure preferences over the next 3–5 years, and how do they interact with budget cycles and program recoverability?
• Which solver/tool vendors are strategic, and are there constraints or preferences around containerization, version cadence, or license pooling?
• What RTO/RPO targets are mandated for PLM, license servers, and HPC metadata, and which systems are mission-critical for shipyard continuity?
• How is cost accountability structured today (chargeback/showback), and what financial transparency is expected by program managers?
• Which data classification and cross-domain constraints apply to digital twin data (test/sea-trial telemetry, supplier models), and what sharing is required with partners?
• What is the current tolerance for spot/preemptible compute for non-critical runs, and how should risk be managed for long-duration simulations?
• Which network modernization initiatives (SD-WAN, private 5G, fiber upgrades) are in-flight, and what integration windows are available?
• What talent bandwidth exists in Engineering IT and HPC administration for operating a hybrid environment, and where is managed service support appropriate?
• Which decision gates and governance bodies must review the target architecture (e.g., architecture review board, cybersecurity council, program leadership)?
• What are the key external dependencies (supplier models, classification authority sign-offs, vendor roadmap changes) that could affect the rollout?
• How should success be measured over the first year—what KPIs matter most to executives and program leadership?

6) Interview Guide for Subject Matter Experts

Role: Head of Engineering IT / PLM Leader

• How are CAD/PLM workspaces organized and replicated across yards, and where do you see the highest latency pain points?
• What version governance processes exist for CAD plugins and macro libraries, and where do version mismatches create rework?
• Which engineering workflows are most sensitive to VDI performance versus local workstation use, and what is the plan for standardization?
• How are change notices propagated through PLM to CAE workflows, and where does data lineage break?
• What is the current PLM/MBSE integration state (DOORS/SysML), and how should CAD/CAE results be linked for traceability?
• What replication/backup strategy is in place for PLM vaults and metadata, and how are RPO/RTO targets enforced?

Role: HPC Lead / Cluster Administrator

• What are the dominant solver workloads, and how do they scale across CPU/GPU and interconnect types?
• Which scheduler policies (fair-share, QoS, preemption) are in place, and where do they fall short for peak demand?
• What parallel file systems are deployed, and what are the known bottlenecks during checkpointing or post-processing?
• How are images and modules managed for solver consistency, and what compliance hardening steps are applied?
• How are failures handled (node, job, license), and what telemetry is available for root-cause analysis?
• What is your view on cloud bursting maturity, and what blockers (data movement, licensing, security) are most material?

Role: Network Architect

• What is the current inter-yard topology (MPLS/SD-WAN), and how are QoS policies applied for VDI and PLM?
• Where are the observed latency and jitter hotspots, and what options exist to introduce secondary paths or traffic steering?
• How are brokering decisions made for VDI session placement, and what influence do you have over endpoint and gateway configurations?
• What is the plan for edge compute and caching to serve CAD-heavy sites, and how will you validate improvements?
• Which security tools (firewalls, IDS/IPS, ZTNA) sit in-line for HPC/PLM traffic, and what are their performance implications?

Role: CISO / Cyber Compliance Lead

• Which control gaps were identified in recent CMMC/NIST assessments, and what is the remediation roadmap?
• How are identity, MFA, and privileged access managed across on-prem and cloud enclaves?
• What is the logging/monitoring strategy for HPC and PLM environments, and how are alerts triaged?
• What ATO boundary definitions and shared responsibility models are acceptable for cloud HPC?
• How are data classification markings enforced in storage and pipelines, and what cross-domain solutions are in play?
• What is the incident response plan for ransomware or data exfiltration affecting engineering systems?

Role: Program Manager (Naval Program)

• What engineering milestones are most at risk due to compute or toolchain constraints?
• How do you prioritize simulation runs when capacity is limited, and what are the schedule penalties?
• What reporting cadence and metrics do you need to manage cost and throughput for your program?
• What vendor/tool dependencies are critical to your certification and acceptance milestones?
• How would you balance cost savings against risk of delays for critical design reviews?

Role: Finance / Procurement

• How are HPC and license costs allocated across programs today, and what transparency gaps exist?
• What appetite is there for reserved commitments in cloud and multi-year license agreements?
• How do procurement timelines align with hardware lead times (GPUs, interconnects) and program gates?
• What is the policy for chargeback/showback, and how should it evolve with hybrid cloud?
• What are the constraints around supplier security posture and export controls for hardware and software?

Role: Digital Twin / Data Architecture Lead

• What data structures and metadata do you rely on to correlate design models, CAE outputs, and operational telemetry?
• How are data retention and access control policies set for digital twin artifacts across lifecycle phases?
• Which analytics or ML use cases are constrained by current data movement and compute limitations?
• What standards or formats are preferred for solver results to support downstream analytics?
• How would you validate that the digital thread remains intact through migrations and version upgrades?

7) Timeline

We executed a 12-week plan structured around discovery, diagnostics, architecture design, pilots, validation, governance setup, and handoff. Decision gates ensured alignment to the Cloud And High Performance Computing Strategy: Define hybrid cloud and high-performance computing architecture for CAD, CAE, and digital twins; optimize cost, resilience, and latency across multi-yard operations.

• Weeks 1–2: Discovery and Baseline
  • Conducted stakeholder interviews, collected architecture artifacts, and initiated data pulls (HPC logs, license usage, PLM telemetry, network metrics).
  • Established KPI baseline dashboards and agreed on target thresholds for latency, utilization, queue wait, and cost unit metrics.
  • Decision gate: Confirmed scope, critical KPIs, and pilot workload candidates.
• Weeks 3–4: Diagnostics and Workload Profiling
  • Completed workload classification, performance sensitivity analysis, and cost driver mapping.
  • Assessed compliance gaps and ATO boundary requirements for cloud bursting.
  • Decision gate: Approved initial placement policy hypotheses and economic model assumptions.
• Weeks 5–7: Target Architecture and Landing Zone Design
  • Produced hybrid HPC reference architecture, storage and data movement patterns, and VDI/PLM latency mitigation designs.
  • Engineered compliant landing zones (identity, network, keys, logging), defined zero-trust patterns, and updated SSP/POA&M drafts.
  • Decision gate: Architecture review board sign-off for pilot build-out.
• Weeks 8–10: Pilot Execution and Migration Factory Setup
  • • Built pilot environments on-prem and in cloud; configured SLURM federation, parallel FS clients, and data movers.
    • Executed representative CFD/FEA runs; validated scheduler policies, license orchestration, and data staging.

  li>Developed migration factory runbooks, service catalog entries, training modules, and operational dashboards.

• Decision gate: Go/no-go for phased rollout and procurement recommendations.
• Week 11: Validation and Business Case
  • Consolidated pilot findings against KPIs; refined economic model (TCO/TEA) and investment phasing.
  • Documented risk mitigations and compliance posture with audit evidence for control coverage.
  • Decision gate: Executive endorsement of roadmap and funding tranches.
• Week 12: Governance, Handoff, and Next Steps
  • Established ongoing governance (performance, cost, security), finalized operating model and RACI.
  • Transferred artifacts to internal teams; scheduled quarterly architecture and KPI reviews; agreed next-wave migrations.

8) Deliverables

• Current-State Architecture Maps
  • End-to-end diagrams of compute, storage, network, PLM/VDI topology, and data flows across yards and cloud. Used to align stakeholders on baseline and dependencies.
• KPI Baseline and Operations Dashboard
  • Dashboards for queue wait, utilization, CAD/PLM latency, VDI performance, storage hot spots, and license denials. Provided the foundation for ongoing governance.
• Workload Profiling and Solver Performance Brief
  • Analysis of solver scaling, IO patterns, GPU/CPU requirements, and placement recommendations. Guided scheduler policies and hardware procurement.
• Techno-Economic Model (TCO/TEA)
  • Multi-year cost scenarios comparing on-prem, cloud bursting, and hybrid strategies; included sensitivities for demand swings, pricing constructs, and license models. Supported funding decisions.
• Hybrid HPC Reference Architecture
  • Blueprint covering compute, storage, network, VDI, data movement, and bursting patterns, with control points for cost, resilience, and performance. Served as the implementation guide.
• Security and Compliance Blueprint
  • Landing zone design, control mappings to CMMC/NIST, zero-trust measures, logging and key management, and updated SSP/POA&M content. Enabled audit-ready execution.
• Data Architecture and Digital Thread Patterns
  • Metadata models, lineage mappings, and integration patterns across PLM, CAE, MES, ERP, and analytics. Ensured traceability for digital twin workflows.
• License and Scheduler Optimization Plan
  • Policy definitions for license pooling, QoS classes, fair-share weights, and preemption. Improved predictability of engineering throughput.
• Network and Latency Optimization Plan
  • WAN/SD-WAN policies, VDI placement and tuning, edge caching recommendations, and validation methods. Addressed user experience at remote yards.
• Migration Factory Playbook
  • Runbooks, automation scripts, image baselines, testing protocols, and acceptance criteria for moving workloads into the hybrid environment. Enabled repeatable scale-up.
• Resilience and DR Runbooks
  • Backup, snapshot, and recovery procedures for PLM, license servers, and HPC metadata, with RTO/RPO targets and test procedures. Increased confidence in continuity.
• Investment Roadmap and Governance Charter
  • Phased roadmap for hardware refresh, cloud commitments, software upgrades, and training; governance cadence, KPIs, and escalation paths. Anchored executive oversight.

9) Industry Insights

Delivering Cloud And High Performance Computing Strategy: Define hybrid cloud and high-performance computing architecture for CAD, CAE, and digital twins; optimize cost, resilience, and latency across multi-yard operations in Shipbuilding & marine systems required a grounded view of industry dynamics and what “good” looks like within Aerospace & Defense.

• Market dynamics:
  • Naval programs are increasingly model-based, with MBSE and digital engineering mandates driving integration among requirements, CAD/PLM, CAE, and digital twin environments. Engineering throughput and decision speed have become competitive differentiators.
  • Program schedules feature pronounced peaks around design reviews and trials, creating bursty compute demand. Hybrid architectures that combine on-prem steady-state capacity with secure cloud bursting reduce schedule risk and capital intensity.
• Technology shifts:
  • GPU-accelerated solvers, scalable CFD/FEA codes, and AI-assisted design are reshaping compute profiles. Next-generation interconnects and parallel file systems are essential to unlock performance without cost blowouts.
  • VDI and remote rendering have matured, enabling secure, centralized CAD/PLM access with acceptable user experience when latency budgets are engineered via SD-WAN and edge caching.
  • Containerization (Apptainer/Singularity) for HPC is gaining acceptance, improving reproducibility and portability under compliance constraints, though license models and vendor policies remain gating factors.
• Ecosystem and partnering:
  • Core vendors include Dassault Systèmes (3DEXPERIENCE/CATIA/DELMIA), Siemens (NX/Teamcenter/Opcenter), Hexagon/MSC, Ansys, Cadence (NUMECA), and PLM/MES integrators. Hardware partners span HPE/Cray, Dell, Lenovo, NVIDIA, AMD, and storage providers such as DDN, NetApp, and Pure.
  • Cloud providers’ defense offerings (AWS GovCloud, Azure Government) with IL5/IL6 capabilities and HPC services (ParallelCluster, CycleCloud, FSx for Lustre, Azure NetApp Files) reduce time-to-value when aligned with ATO and enclave patterns.
• Regulations and standards:
  • CMMC and NIST 800-171 compliance is moving from guidance to enforcement for primes and the supplier base. Zero-trust architectures, immutable logging, and documented control coverage are expected.
  • ITAR and export controls affect data locality and GPU procurement. Data classification and marking automation within pipelines reduces compliance risk.
  • Model-based acquisition shifts require demonstrable digital thread traceability from requirements through certification, affecting data architecture choices.
• Buyer behavior:
  • Program managers prioritize schedule assurance and auditability; CFOs emphasize predictable costs; CISOs require consistent control coverage. Executive sponsors favor roadmaps that flex with funding and demand.
  • Buyers increasingly expect visibility into unit economics (cost per core-hour, cost per simulation) and chargeback models that align incentives across programs.
• What “good” looks like (illustrative targets):
  • CAE queue wait time consistently below 10–20% of wall-clock time during peak periods, with sustained cluster utilization in the 70–85% range.
  • Interactive CAD/PLM sessions achieving latency within vendor-recommended budgets for VDI (<50 ms round-trip for most users) and stable frame rates for complex assemblies.
  • Parallel file systems delivering balanced throughput and metadata operations to support high-frequency checkpointing without stalls; object storage with clear lifecycle policies curbing storage sprawl.
  • Chargeback/showback that attributes cost per simulation and per design loop by program, enabling informed trade-offs.
  • RPO/RTO targets for PLM and CAE metadata aligned to program criticality with periodic tabletop and failover tests.
  • Compliance posture with documented control ownership, continuous monitoring, and audit trails for engineering workloads.
• Near-term disruptions and implications:
  • Semiconductor supply constraints and export controls may extend lead times for GPUs; hybrid strategies and cloud commitments provide optionality.
  • License model changes (tokenization, cloud usage rights) affect economics; proactive vendor engagement avoids lock-in and cliff effects.
  • Zero-trust mandates and evolving CMMC levels drive refresh of identity, network micro-segmentation, and logging strategies in engineering environments.
  • Broader adoption of private 5G in yards and edge compute for inspection and AR/VR will influence network design and data movement for digital twins.

In sum, the hybrid cloud and HPC strategy tailored to Shipbuilding & marine systems within Aerospace & Defense required a pragmatic integration of workload economics, compliant architecture, latency engineering, and operating model design. Our approach framed decision rights, investment pacing, and measurable KPIs to guide execution across multi-yard operations while supporting the digital thread and digital twin ambitions.