Fragmented runtime environments
Multi-cloud, multi-cluster, and edge environments lack consistent standards, management, and operational visibility.
Cloud-Native & AI Runtime Systems
A stable, elastic, and secure container runtime foundation for enterprise applications and AI workloads.
This system owns the Kubernetes, GPU scheduling, container, and infrastructure runtime foundation for application and AI workloads.
Customer challenges
Platform programs are moving from cluster delivery to a long-term operating system
New demands from AI workloads
GPU scheduling, heterogeneous resources, isolation, and cost governance must become part of the common runtime system.
Disconnected security, continuity, and upgrades
Image, admission, runtime, backup, recovery, upgrade, and migration capabilities are often built by separate teams.
Platform delivery is not sustainable operations
Long-term governance for health, capacity, versions, incidents, cost, and service levels remains incomplete.
Value
Make the runtime foundation a shared capability for continuous business and AI evolution
Business continuity
Run critical business and AI scenarios on a stable, scalable, and rehearsable foundation.
Platform standardization
Turn environment, cluster, compute, security, and operations requirements into reusable platform standards.
Compute efficiency
Improve GPU availability and utilization through pools, scheduling, quotas, metering, and capacity governance.
Controlled security
Embed supply-chain, admission, network, runtime, and audit controls across the platform lifecycle.
Sustainable operations
Use SLO, capacity, version, incident, and cost evidence to evolve platform capability continuously.
System boundary
Connect onboarding, runtime, governance, assurance, and continuous optimization around containers and heterogeneous compute
Service work packages
From platform strategy and compute scheduling to continuity, migration, and long-term operations
01
Container platform strategy and implementation
Define architecture, implement the platform, integrate core services, and verify delivery.
- Outcome
- A standardized and extensible container runtime foundation.
- Key deliverables
- Target architecture · Platform delivery and operations guide
02
Unified multi-cloud and multi-cluster management
Implement cluster enrollment, configuration, policy, visibility, and lifecycle management.
- Outcome
- Consistent multi-cluster governance and operational visibility.
- Key deliverables
- Multi-cluster management design · Policy and operations baseline
03
Container scheduling for GPU and AI compute
Build resource pools, scheduling, quotas, isolation, capacity, and metering.
- Outcome
- More available, transparent, and efficiently used AI compute.
- Key deliverables
- GPU resource-pool design · Scheduling and quota policies
04
Integrated cloud, edge, and device operations
Unify node management, application distribution, offline operation, state reporting, and version governance.
- Outcome
- Consistent delivery and autonomous operation for distributed workloads.
- Key deliverables
- Cloud-edge-device architecture · Distribution and operations standards
05
Container security and compliance governance
Establish image, admission, secret, network, runtime, and audit controls.
- Outcome
- A security baseline spanning build, delivery, and runtime.
- Key deliverables
- Container security architecture · Compliance control catalog
06
Cloud-native disaster recovery and business continuity
Build application-consistent backup, cross-cluster recovery, orchestration, and drills.
- Outcome
- A business-continuity capability that can be rehearsed and verified.
- Key deliverables
- Disaster recovery system design · Recovery drill report
07
Platform operations assurance and continuous optimization
Establish monitoring, health checks, incident response, capacity, and version management.
- Outcome
- Platform delivery becomes a sustainable operating capability.
- Key deliverables
- Operations assurance model · Health and optimization reports
08
Container platform upgrades and migration
Assess, verify compatibility, migrate in waves, design cutover, and prepare rollback.
- Outcome
- Platform evolution with business continuity protected.
- Key deliverables
- Upgrade and migration plan · Verification, cutover, and rollback plan
Target system
Connect workload onboarding, compute runtime, cross-environment management, and operations governance
- 01
Application and AI workload onboarding
Onboard applications, data, and AI workloads through standard templates, artifacts, pipelines, and self-service.
- Self-service
- CI/CD integration
- Images and artifacts
- Workload templates
- 02
Container and heterogeneous compute runtime
Use Kubernetes to host general applications, data services, and GPU-intensive AI workloads.
- Container runtime
- GPU resource pools
- Scheduling and isolation
- Storage and networking
- 03
Multi-cloud, multi-cluster, and edge management
Manage central cloud, private cloud, factory edge, and field nodes through consistent enrollment and distribution.
- Cluster lifecycle
- Policy distribution
- Cloud-edge collaboration
- Offline autonomy
- 04
Security, observability, and continuity
Unify supply-chain security, admission, runtime protection, observability, backup, disaster recovery, and drills.
- Security and compliance
- Runtime observability
- Backup and DR
- Recovery drills
- 05
Operations governance and continuous optimization
Manage health, capacity, versions, incidents, service levels, cost, and capability roadmaps as a platform product.
- Platform SLOs
- Capacity and cost
- Version and change
- Continuous improvement
Delivery path
Five stages from strategy to sustainable operations
- 01ASSESS
Assess
Assess application, infrastructure, team, and governance baselines.
- 02DESIGN
Design
Define target architecture, standards, boundaries, and the evolution roadmap.
- 03BUILD
Build and integrate
Implement the core platform in stages and integrate identity, engineering, security, and operations systems.
- 04MIGRATE
Migrate and verify
Onboard applications, data, and AI workloads in waves and verify compatibility, performance, security, and recovery.
- 05OPTIMIZE
Optimize and transfer
Improve with operating evidence and transfer processes, tools, knowledge, and platform-team capability.
Deliverables
Deliver the platform together with standards, mechanisms, and sustainable operating capability
Key scenarios
Programs for enterprise applications, multi-cluster estates, AI compute, and distributed operations
Enterprise containerization and unified runtime
Standardize application assessment, modernization, delivery, operations, and service levels for scaled onboarding.
Multi-cloud and multi-cluster governance
Unify cluster lifecycle, policy, quota, change, and operations while preserving necessary environmental differences.
GPU and AI compute platform
Build heterogeneous GPU pools, queues, quotas, isolation, metering, and capacity planning for training and inference.
Cloud-edge operations and business continuity
Support controlled distribution, offline autonomy, state reporting, disaster recovery, and drills across field environments.