(Source: (RT Insights): By embracing durability as a strategic priority, organizations can mitigate risks, protect their investments, and enable innovation in an increasingly data-reliant world.

In today’s data-driven landscape, high-performance computing (HPC) and AI environments demand more than just speed. According to Hyperion Research, durability—ensuring that data remains unchanged and free from corruption or loss—is emerging as a critical requirement alongside traditional performance metrics. Organizations can no longer afford to overlook the potential costs of data loss, corruption, or downtime, which include financial penalties, project delays, and wasted resources.

While durability has long been prioritized in the enterprise market, HPC and AI environments face unique challenges due to the sheer scale of infrastructure, where “everything breaks at scale.” Addressing these challenges requires moving beyond traditional storage architectures and learning from Hyperscalers who have adopted more sophisticated levels of data protection. These strategies offer improved durability and availability compared to traditional high-availability (HA) pair models that have historically dominated parallel file system deployments.

Traditional Data Protection Is No Longer Enough

Traditional data protection strategies, such as high-availability (HA) pairs with local redundancy (e.g., RAID configurations), were designed to prioritize uptime in traditional enterprise storage environments but were not designed for the scale-out needs of HPC or AI data infrastructure. While effective in the past, these methods fail to address the performance, availability, and durability challenges posed by modern AI and HPC workloads, including:

• Hardware Failures Across Multiple Nodes: Failures impacting more than a single system can lead to cascading data losses.
• Corruption During Recovery: Rebuilding data from localized failures increases the risk of corrupted datasets.
• Data Volume Explosion: The rapid growth of data and the increasing complexity of AI workloads have outpaced traditional protection methods.

Learning from Hyperscalers

To mitigate these risks, hyperscalers have adopted advanced techniques with layers of protection to ensure availability and durability in large-scale environments. By distributing data and parity across multiple nodes, these systems achieve higher levels of performance, fault tolerance, and efficiency compared to traditional RAID or HA pair models. This approach demonstrates the importance of scalability and redundancy for protecting against failures at the node, rack, or even data center level.

For HPC and AI environments, taking inspiration from hyperscaler architectures provides a path forward. Hyperion emphasizes that durability mechanisms should scale seamlessly with workload demands, ensuring consistent protection regardless of data volume or complexity.

Why AI Workloads Require a New Approach

AI infrastructure investments run into millions annually, spanning GPU clusters, compute time, and energy costs, raising the stakes for data protection. Experiencing downtime or losing critical datasets—whether due to corruption, node failure, or recovery delays—can have devastating consequences:

• Expensive: Restarting AI models may require millions of dollars in GPU server time and energy consumption.
• Missed Milestones: Delays caused by lost data can disrupt timelines and erode competitive advantage.
• Wasted Resources: Computational efforts and insights are lost without the right durability safeguards in place.

These risks demand a durability strategy capable of protecting data at multiple levels.

A Modern Approach: Multi-Level Erasure Coding (MLEC)

To address the growing durability challenges in AI and HPC, organizations must adopt advanced strategies like multi-level erasure coding (MLEC). Unlike traditional methods, MLEC provides protection at multiple levels of the storage architecture:

1. Client-Side Network Erasure Coding: At the point of data entry, client-side encoding ensures resilience against network outages and system-wide failures.
2. Node-Level Erasure Coding: Within storage nodes, localized protection safeguards data against hardware failures, disk corruption, and rebuild errors.

The dual-layered approach delivers unmatched durability, ensuring data integrity even in the face of multiple concurrent failures. By combining client-side and node-level safeguards, MLEC provides:

• End-to-End Data Protection: Ensuring data remains intact and recoverable across system and hardware failures.
• Scalable Resilience: Protection mechanisms that grow with expanding workloads and data volumes.
• Optimized Performance: Advanced erasure coding minimizes latency, ensuring HPC and AI systems can perform at peak capacity.

Durability: A Strategic Imperative

AI and HPC infrastructures are among the most significant investments organizations make. In this context, durability is not just an operational concern; it is a strategic imperative.

Organizations that value their data must start by assessing their risk and determining how much durability they need. This means asking key questions about downtime tolerance, data loss, and business continuity:

• What is the acceptable risk of data loss?
• How much downtime can your organization afford?
• What are the financial or operational costs of delayed data availability or corrupted datasets?

Quantifying Durability: The “Nines” of Data Protection

Durability is often measured in terms of “nines,” reflecting the probability of data remaining intact over time. Understanding these metrics helps organizations align durability requirements with their business needs:

• 1 Nine (90%): Allows for one object in 10 to experience data loss. This level may only be suitable for non-critical or test workloads with minimal impact from interruptions.
• 3 Nines (99.9%): Reduces the risk to one object in 1,000. Appropriate for workloads with moderate reliability requirements where some data loss is tolerable.
• 5 Nines (99.999%): Ensures that only one object in 100,000 may experience data loss. This level is ideal for most critical applications where interruptions can result in significant costs.
• 7 Nines (99.99999%): Offers extremely high durability, with a risk of one object in 10 million being lost. This level is suited for sensitive workloads, such as AI model training, where data loss would cause catastrophic setbacks.
• 9 Nines (99.9999999%): Represents near-perfect durability, allowing for one object in a billion to experience data loss. This is essential for the most valuable AI and HPC environments where even the slightest data loss is unacceptable.
• 11 Nines (99.99999999999%): Represents the highest level of durability achievable today—only one object in 100 trillion may experience data loss. This level of protection is for environments that demand maximum assurance. It offers enterprise and research organizations a new gold standard in data resilience, making it ideal for mission-critical AI and HPC workloads where any data loss is unacceptable.

A Call to Action for Organizations

Companies must define their durability thresholds based on the criticality of their workloads and the cost of failure. For AI and HPC, where infrastructure investments are substantial, prioritizing advanced architectures like multi-level erasure coding (MLEC) is essential. MLEC provides scalable, end-to-end protection that aligns with modern durability demands, ensuring that data remains secure, accessible, and resilient across all workloads.

By embracing durability as a strategic priority, organizations can mitigate risks, protect their investments, and enable innovation in an increasingly data-reliant world.