High Availability (HA): Architecture, Principles, and Real-World Use Cases

Hardware fails. Power supplies burn out, fiber gets cut, and hard drives die. High Availability (HA) masks these physical failures from the end-user.

It is a system design focused on automated failover. When a crash occurs, traffic instantly diverts to a healthy component, allowing business processes to continue despite infrastructure failures. Unlike Disaster Recovery, which implies a stop and a restart, HA aims to make the failure invisible to the application layer.

High Availability Architecture

HA requires a complete dependency chain. A redundant application cluster is useless if it sits behind a single firewall. The high availability architecture relies on two core concepts:

1. Redundancy: Every critical part of the system has more than one copy. These copies work together so that if one component fails, another one is already available. This removes single points of failure and helps keep systems running without interruption.
2. Automated failover: The system can detect a failure and switch to a healthy component automatically. This process does not require manual action from IT staff. As a result, recovery happens quickly, and users may not even notice that a failure occurred.

If a human has to press a button, it is not High Availability. It is Disaster Recovery.

Figure 1: Simple HA scheme

The Layers of Redundancy

Compute clustering is ineffective without Storage HA. If the data sits on a single SAN and that SAN dies, the compute cluster has nothing to run. This makes shared storage or synchronous mirroring (like StarWind Virtual SAN or VMware vSAN) mandatory. Similarly, the Network Level requires multipathing. A single cable or switch failure should result in a dropped packet, not a dropped connection.

So, for proper, redundant system design, High Availability must be implemented across multiple levels of the IT environment:

Compute layer

At this level, High Availability is achieved by clustering multiple servers together. These servers share workloads and monitor each other’s health. If one server fails, workloads are automatically moved to another server in the cluster, allowing applications to continue running.

Storage layer

Here, High Availability is achieved by distributing data between storage nodes. This ensures that data remains accessible even if one storage device or node fails. Since applications depend on data availability, storage HA is a critical part of the overall architecture.

Networking ayer

At the networking level, high availability is achieved by using multiple network paths. This is done with redundant switches, firewalls, routers, and network links. If one network path fails, traffic is automatically redirected through another path, preventing connectivity issues.

The Cost of “Nines”. Uptime vs. Budget

Availability is measured in “nines,” representing the percentage of time a system is operational over a year. Often, executives demand “Five Nines” (99.999%) without actually approving the budget required to achieve it.

The figures below show how availability percentages translate into real downtime over one year:

“Nines” vs Downtime:

• 99% (Two Nines): ~3.6 days of downtime/year. Acceptable for non-critical batch processing.
• 99.9% (Three Nines): ~8.7 hours of downtime/year. The standard for most SMB infrastructures.
• 99.99% (Four Nines): ~52 minutes of downtime/year. Required for medium businesses and enterprise production environments.
• 99.999% (Five Nines): ~5 minutes of downtime/year. Required for banking, healthcare, and telecommunications.

The hard truth is moving from 99.9% to 99.99% often involves an exponential increase in cost, requiring a shift from simple redundancy (Active/Passive) to “proper” Active/Active clusters and geographically stretched sites. The same rule works for transitioning from four nines to five – such environments require serious planning, substantial investments, and complete fault tolerance on all infrastructure layers.

The “Complexity Paradox”

A common system design oversight is ignoring the complexity tax. A complex HA system, such as a stretched cluster across multiple sites with automated load balancing, introduces new failure modes.

If the logic controlling the failover malfunctions, it can cause an outage even when the hardware is fine. This often occurs in “Split-Brain” scenarios where communication between nodes breaks and both nodes try to take ownership of the same data, leading to corruption.

To prevent this, robust HA typically requires a “Witness” or Quorum mechanism to act as a referee during network partitions.

Some solutions like StarWind Virtual SAN handle this specific complexity by offering multiple heartbeat strategies. It can operate in a “witness-less” 2-node configuration using specific safeguards (like heartbeat over separate physical links) or allow for a traditional Witness implementation using additional lightweight cluster instance or a simple file share to arbitrate the connection. This significantly reduces the infrastructure footprint required to maintain a safe quorum.

HA is Not Disaster Recovery (And It’s Not a Backup)

Even if High Availability redundancy is implemented on all levels, a working (and tested) backup strategy must still be in place. This is mandatory because HA protects only from infrastructure failure, but it does not guarantee protection from software corruption, human errors, or cyberattacks.

HA does not replace backups.

• HA protects against infrastructure failure. If a RAID array or one of the servers dies, HA keeps the data (your VMs, and containers) live.
• Backups protect against data corruption. If you delete a critical database table, the HA system instantly replicates that deletion to the mirror. You now have highly available, corrupted data.

Similarly, HA is distinct from Disaster Recovery (DR). HA handles daily issues like a failed switch or a rebooting server within a single location. DR is the insurance policy for site-wide catastrophes like floods or long-term power outages. HA is automatic and instant (RTO ≈ 0); DR is often manual and takes time (RTO > 1 hour).

Real-World Use Cases

Here are prominent examples of HA infrastructure use cases:

Healthcare (EHR & PACS)

Hospitals rely on Electronic Health Records (EHR) and Picture Archiving and Communication Systems (PACS). A storage backend failure cuts access to patient histories and scans, endangering safety. HA keeps clinical apps active during host failures, ensuring staff focus on care, not IT troubleshooting.

Manufacturing (SCADA & IIoT)

Factories use Supervisory Control and Data Acquisition (SCADA) to manage assembly lines. A server crash halts production and often ruins the material on the line (e.g., chemical processing). HA clusters prevent this “batch spoilage” by ensuring control software survives hardware glitches in dusty, or, for example, high-vibration environments.

Maritime and Offshore (Disconnected Edge)

Ships and oil rigs operate with limited connectivity, making cloud failover impossible. If navigation systems fail at sea, the crew cannot download backups. Ruggedized 2-node clusters provide self-healing capability, sustaining operations indefinitely until the vessel reaches port.

Retail & ROBO (Distributed Sites)

Retail chains operate “micro clusters” in Remote Office / Branch Office (ROBO) locations. If the local Point of Sale (POS) server fails, the store cannot process payments. Automated 2-node HA clusters allow immediate self-healing, keeping lanes open without requiring emergency IT site visits.

Financial Services (Transactional Integrity)

For banks, uptime ensures regulatory compliance. A database outage pauses operations and creates a backlog of failed transactions requiring expensive manual reconciliation. The cost of regulatory fines often exceeds the investment in HA infrastructure.

Education (VDI & LMS)

Schools rely on Virtual Desktop Infrastructure (VDI) and Learning Management Systems (LMS). A cluster failure during exams affects thousands of students. HA protects active sessions, ensuring a physical host failure doesn’t disconnect students or lose exam data.

Video Surveillance (VMS)

A standalone Network Video Recorder (NVR) is a single point of failure for Video Management Software (VMS). HA storage mirrors recording data between commodity servers, ensuring continuous surveillance without the cost of proprietary enterprise hardware.

All in all, understanding the architecture is only half the battle. The challenge lies in implementation. Historically, achieving “HA level” of resilience required expensive, proprietary SAN arrays. However, the modern approach shifts resilience from the hardware layer to the software layer, allowing you to build High Availability on standard commodity servers.

Achieving Storage HA with StarWind and DataCore

Storage is often the hardest layer to make highly available because data must be consistent across nodes. StarWind and DataCore solve this through software-defined approach that decouples resilience from specific hardware.

StarWind Solutions for SMB and ROBO

• StarWind Virtual SAN (VSAN) – For Edge and SMB environments, StarWind VSAN eliminates the need for expensive physical SANs. It mirrors data synchronously between two servers, presenting them as a single shared storage pool. Crucially, it handles the “Split-Brain” scenario effectively in 2-node setups, ensuring that if one node drops, the other takes over transparently without data corruption.
• StarWind Virtual HCI Appliance – For organizations wanting a “plug-and-play” experience, the HCI appliance pre-integrates the hypervisor, storage, and networking. This approach simplifies deployment and management, providing high availability and pro-active maintenance out of the box.

DataCore Solutions for Enterprise

• DataCore SANsymphony: A hardware-agnostic software-defined storage. It can mirror data between different brands of storage arrays (e.g., mirroring a Dell array to an HP array), ensuring that even a total vendor-specific hardware failure does not stop IO.
• DataCore Puls8 (Container Native Storage): Kubernetes handles application availability by restarting containers, but it doesn’t inherently solve data availability. Puls8 addresses this by pooling local disk capacity into persistent volumes. When a node fails and a pod is rescheduled, Puls8 ensures the volume remains accessible, bridging the gap between stateless orchestration and stateful data requirements.
• DataCore Swarm: For massive unstructured data (backups, archives, video), Swarm provides object storage resilience. Unlike block storage HA, Swarm uses erasure coding and geo-replication to ensure data survives even if multiple disks or entire sites go dark.

Conclusion

High Availability balances the cost of redundancy against the cost of downtime. While modern tools like StarWind and DataCore simplify implementation by removing the need for proprietary hardware, the principles remain rigid: remove single points of failure, automate recovery, and never confuse a cluster with a backup.

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