Deploying Private Clouds for E-Learning and Remote Labs

Running e-learning at scale is a feat given the tremendous appetite for AI- and lab-based learning. Here's a practical build guide if you want to deploy private clouds for GPU labs, low-latency access, and predictable costs.

Universities are no longer in competition with just each other. Students now have the option to choose between faculty and ChatGPT. 

In a world reshaped by generative AI, attention has shifted from lecture halls to learning that happens on-demand, asynchronously, and across devices. The institutions that thrive in this new era will be those that mirror these real-world shifts and scale with remote-first needs. 

When done well, this approach supports equitable access, deeper skill development, and stronger outcomes, whether you're running a data science class, AI workload, or a hybrid cybersecurity bootcamp. 

That's Why Remote-Ready Infrastructure Matters 

During the pandemic, e-learning became necessary.  

In fact, one major European university documented over 600 virtual classes per day and served 16,000 students concurrently on its own network after shifting online. 

However, post-pandemic, it is expected.  

But that kind of scale and concurrency pushes edge‑networking, storage and compute systems harder than typical lecture‑based e‑learning platforms.  

In engineering, science and cybersecurity curricula, students require hands‑on interaction with lab environments and virtual machines. 

Why? 

Because virtualized environments also promise better learning through improved engagement and accessibility. According to a 2024 study, 73% of educators said a virtualized lab drove strong student engagement, particularly when paired with structured scaffolding and on-demand access to compute-backed exercises.  

And while many institutions already provide online courses, traditional IT architectures were never built for this level of flexibility.  

The Driver: Labs, Traffic and Unpredictability 

If you’re trying to support GPU-backed notebooks, ephemeral sandbox environments, or fast resource spin-ups without manual provisioning, you need a new foundation. Because these environments require flexible resource allocation, secure identity integration, and cost-aware scheduling. 

Using public cloud services alone created unpredictable cost models, latency challenges (when learners connected from multiple geographies), and regional data‑sovereignty or network‑egress constraints, especially for labs requiring high I/O, GPU access or tight control over hardware sets. 

The Case for Campus Private Clouds 

Instead of confining infrastructure to local labs or outsourcing to hyperscalers with opaque pricing, universities are now deploying on-premise or hybrid private clouds built on open-source platforms like OpenStack.  

A private campus cloud brings three key advantages: 

1. Low latency and controlled networking 

When lab VM instances, storage systems and instrumentation are on‑campus (or within a regional data centre tightly coupled via low-latency links), students experience real‑time responsiveness akin to physical labs. 

2. Dedicated resources and hardware specialization 

Many remote labs require GPU passthrough, SR‑IOV virtualization or specific instrumentation (e.g., oscilloscopes or FPGA boards). A private cloud lets you allocate, partition and manage these resources explicitly rather than relying on general-purpose public cloud instances. 

3. Predictable cost and utilisation models 

Rather than unpredictable hour‑based billing or data‑egress fees, a campus private cloud offers fixed‑cost or consumption‑based internal metering. 

What the Architecture Looks Like 

From a design perspective, the architecture typically involves a cluster of compute nodes (some general‑purpose, others hardware‑accelerated for labs), a shared object/block storage backend (Ceph is a common choice), network segmentation supporting VLANs or tenant‑networks per lab/class and an orchestration layer (OpenStack, Kubernetes) to let instructors spin up lab environments on‑demand.

In one documented case study, a university deployed an OpenStack + Ceph environment consisting of 11 nodes, 96 vCPUs, 312 GB of RAM, and 27 TB of Ceph storage to power their remote education initiative. 

Use Case: A Remote Lab  

Consider a semester on embedded systems with remote access to boards and virtual machines. On the private cloud: 

• Instructors define a lab blueprint (via Heat/Ansible or Terraform) that includes a VM with attached storage, a virtual network, and an FPGA board exposed via PCI‑passthrough or PCI‑hotplug. 
• The orchestration engine instantiates that blueprint when the student logs in; storage is dynamically allocated from a Ceph RBD pool; network isolation ensures students’ traffic is segmented; GPU/FPGA scheduling ensures sharing without interference. 
• Duration and quotas are enforced via a usage metering layer idle instances are cleaned up automatically, freeing resources for the next student wave. 
• Overstamp the environment with monitoring: Prometheus/Grafana dashboards track VM start‑latency, network performance, storage IOPS, and end‑to‑end latency from student endpoint to lab VM. 

This model enhances reproducibility (labs spin up identical), scalability (hundreds of students simultaneously) and manageability (usage is tracked, hardware is dedicated but pooled). 

Use Case: Federated Cybersecurity Labs 

A national cybersecurity bootcamp wants to create identical lab environments for students in six different regions, with access governed by a national identity system. Their requirements: sandboxed VMs, region-specific data boundaries, and centralized control. 

Using Heat templates and Keystone federation, the platform team deploys lab environments that reflect each region’s compliance zone, with Neutron security groups enforcing east-west isolation.

Labs can be provisioned, torn down, and replicated with near-zero overhead, and telemetry pipelines provide feedback to instructors about resource consumption and suspicious activity. 

Tech Design: Best Practices and Pitfalls 

Hardware Configuration

Compute hosts should be synchronised with uniform CPU feature‑sets (important if live‑migration is used), and support required extensions (e.g., VT‑d for passthrough, SR‑IOV for network). Storage clusters need to support high IOPS bursts (for VM boot storms at class start) and high throughput (for video/virtualisation). 

Network Architecture

Neutron (OpenStack) or Calico/Flannel (K8s) should support multi‑tenant routing, security‑groups and micro‑segmentation. For remote labs, north‑south connectivity must consider student WAN latency, but east‑west between compute/storage must stay low. Enabling jumbo frames (MTU 9000) across the storage and tenant networks improves throughput for lab‑VMs transferring large datasets. 

Scalability and Burst Handling

Many e‑learning patterns follow “class start” waves wherein hundreds of VMs are booting within minutes. The orchestration engine must handle that boot storm: ensure compute node resources are pre‑warmed, storage backends (for example Ceph OSDs) are healthy and metadata caches are warm. Without it, student login latency kills experience. 

Compliance and Privacy

Especially for government or national‑education contexts, data locality, encryption at rest, audit logs and role‑based access control matter. Studies recognised that complexity, institution size and technology readiness are significant predictors for cloud adoption in academic institutions. 

How to Think about Implementation 

1. Discovery and sizing - Profile lab workloads (VM counts, GPU/FPGA usage, network peaks) and plan for peak concurrency plus 10‑15% headroom. 
2. Platform selection - Choose an open‑source cloud control plane (e.g., OpenStack) with integrated Kubernetes support if container labs are needed. 
3. Storage & network build‑out - Design Ceph pools for block/object, implement tenant / project quotas, enable thin‑provisioning and snapshots to support student experiment checkpoints. 
4. Blueprinting labs - Define infrastructure templates per course or lab type, with variables for number of VMs, attached devices (GPU/FPGA), network segments, duration limits. 
5. Monitoring and automation - Deploy telemetry. This could be Prometheus for metrics, Grafana dashboards, alerting for latency/IOPS, usage metering to auto‑clean idle devices. 
6. Governance andaccess control - Implement role‑based access (faculty, TA, student), integrate with institutional SSO (LDAP/SAML), log every session for audit/compliance. 
7. Pilot and then scale - Start with one course, measure boot‑latency, resource utilisation and student experience. Tune infrastructure before scaling across faculties or geographies. 

Operational and Budget Advantages 

Faculty can want agility but IT will needs guardrails. 

OpenStack’s native role-based access control (RBAC), project scoping, and usage telemetry (via Telemetry, Prometheus, or 3rd-party exporters) give campus operators visibility without bottlenecks. Students can provision VMs for coursework, but only within assigned quotas. Researchers can request GPU-backed instances, but only through an approval workflow. And with automated billing scripts, teams can assign cloud usage to grant codes or departmental budgets. 

Other operational perks include: 

• Usage metering: Projects and departments can be billed or budgeted based on actual usage via tools like Stratometrics. 
• Self-service provisioning: Instructors can pre-provision lab templates, avoiding last-minute IT tickets. 
• Software-defined isolation: Projects are separated by Neutron networks and Keystone roles, reducing the risk of lateral movement or student interference. 

The benefit is two‑fold: deliver a responsive, modern student‑experience platform while shifting from reactive hardware refresh‑cycles to a stable, software‑driven cloud platform. Additionally, by adopting open‑source platforms (OpenStack, Ceph) you avoid vendor‑lock‑in, reduce licence costs and can repurpose nodes across semesters (for research, HPC, labs etc.). 
 
On the cost end, a 2024 report noted that implementing a private‑cloud platform using open‑source stacks (for teaching resources) can reduce “service interruption time to less than 5 minutes per year” and help with a 20% reduction in resource‑waste. 

A Note on Implementation 

While OpenStack and Ceph provide the foundational components, getting everything running smoothly requires experience in architecture design, networking, and identity integration.

Platforms like Atmosphere, VEXXHOST’s OpenStack distribution, help by providing pre-integrated deployment tooling, observability pipelines, and optional professional services for educational deployments. When deployed with Atmosphere, teams also benefit from: 

• Pre-configured security defaults (like security groups, default quotas, and project isolation) 
• Grafana dashboards for real-time visibility into lab usage, student consumption, and system health 
• Day-2 tooling for patching, upgrades, and high availability, minimizing admin time and student downtime 

With Hosted and On-Premise editions, institutions retain full control while offloading day-to-day operations. 

Ready to Modernize? 

If your institution is looking to bring compute, storage, and orchestration in-house to support remote learning, AI/ML courses, or modern research, schedule a free consultation with one of our experts.