This article was originally published on INFORMATION WEEK by Jason Chien from Silicon Motion.

Optimizing enterprise storage for AI with low latency, high throughput, and improved QoS using dynamic resource management and advanced controllers.

As artificial intelligence (AI) reshapes enterprise IT environments, the need for robust, predictable storage performance has never been more pressing - maximizing storage Quality of Service (QoS) is crucial for optimizing AI pipelines.

Existing techniques and capabilities go a long way toward safeguarding performance, maintaining reliability and enabling seamless scaling in modern data centers, but the unique demands that AI places on storage infrastructure require another layer of QoS functionality.

Silicon Motion's PerformaShape leverages advanced algorithms to help enterprise storage meet current AI pipeline needs while also providing the flexibility and control required for future innovation.

AI Ups the Ante for QoS

QoS has been table stakes for enterprise storage long before the onslaught of AI workloads.

It ensures reliability in shared and virtualized settings and reduces resource conflicts - strong QoS stops less important apps from affecting essential workloads, keeps systems stable, and prevents "noisy neighbor" issues.

Maintaining QoS in an AI pipeline is even more complex and dynamic. Mission critical AI workloads such as fraud detection and medical diagnostics need priority access to vital storage resources so they can deliver reliable, timely results.

As data-intensive workloads like AI grow, enterprises tend to overprovision storage, which leads to higher costs and underutilization and further cements the need for smarter, automated QoS.

Intelligent QoS ensures real-time inference and autonomous systems consistently meet performance goals for various workloads throughout the AI pipeline.

Table Stakes Techniques for Managing QoS

There are many ways to manage enterprise storage QoS - all of them should be considered as part of a broader toolkit for meeting AI storage performance goals:

• Policy-based storage QoS management sets IOPS and throughput limits for storage objects, sharing or dedicating resources as needed.
• Rate limiting and minimum guarantees restrict the throughput and IOPS a workload can consume to ensure fair resource allocation and set baseline performance. This is typically used for live storage migration in major AI platforms; strict performance control is critical for smooth live migration of GPU/CPUs and namespace storage attached to virtual machines.
• Service classes or tiering can give critical workloads higher priority; isolating high-demand workloads prevents them from affecting important or latency-sensitive processes.
• Throughput ceilings/floors can be scaled dynamically using adaptive QoS based on the size of the workload.
• The I/O rate can be managed with traffic shaping to prevent congestion; priority and weighted fair queueing ensure high-priority workloads are serviced first.
• QoS Monitoring systems track IOPS, throughput, and latency for each workload, adjusting enforcement as limits are reached.
• In shared, multi-tenant environments, fine-grained QoS controls can be applied to a specific tenant or volume to meet SLA requirements.
• Software-defined queue-based I/O classification and performance shaping can enforce policies.
• Automated tools can enable bulk QoS policy updates, scaling, and self-service provisioning for large-scale deployments.

These techniques provide the necessary performance stability, resource fairness, SLA compliance, and cost efficiency for enterprise AI storage environments, which are further enhanced by the storage media and firmware.

QoS Features in SSDs and NVMe

Common QoS techniques are complemented by features built into SSDs to keep them efficient, reliable, and suitable for applications where performance consistency is vital.

• The latency predictability of SSDs guarantees fast completion of I/O operations by specifying the percentage of I/Os finished within a target time to maintain performance during heavy workloads.
• Advanced QoS minimizes the impact of internal background flash tasks which can disrupt I/Os and affect latency, such as garbage collection and wear leveling.
• Some enterprise SSDs offer per-tenant QoS controls, enabling providers to guarantee performance and minimize noisy neighbor issues through rate limiting and prioritization.
• Enterprise SSDs use advanced firmware and unused capacity to ensure sustained performance and stable QoS as they fill up or age.

The Non-Volatile Memory Express (NVMe) protocol further advances QoS in SSD storage through hardware and software mechanisms It also promotes parallelism and multi-queuing, which allows multiple I/O streams to be processed simultaneously and helps maintain consistent delivery of IOPS and throughput across AI pipelines. Software integration further enhances QoS by mapping user priorities and Service Level Agreements (SLAs) to NVMe queue configurations.

Data placement is essential for QoS in AI workloads because storing data in slow or remote systems leads to delays and decreased performance.

NVMe's data placement features now let hosts guide how SSDs write data, aiming to reduce write amplification and boost endurance. The Flexible Data Placement (FDP) feature allows hosts to provide hints for intelligent media alignment, balancing host influence with SSD controller management.

QoS can be further improved by prioritizing critical AI data flows with performance shaping. By dynamically migrating or replicating data based on workload demands and QoS monitoring, these systems adapt to changes, ensuring consistent QoS for modern AI applications in distributed environments.

While QoS techniques, SSDs features, and the NVMe protocol all help to improve QoS management and optimize data placement for AI pipeline storage, the pace of innovation demands an additional layer of capabilities that deliver intelligent, automated data orchestration.

PerformaShape Amps Up AI Storage QoS

Silicon Motion's PerformaShape adds another layer of QoS capabilities for AI storage.

Its firmware defined and hardware accelerated performance shaping engine supports a large number QoS groups. Each group can be associated to a host tenant - an application, a virtual machine, a container, or a namespace - and each QoS group can be programmed to shape a specific IO workload, including sequential read/write throughputs and random read/write IOPS.

Silicon Motion's PerformaShape has several features that optimize enterprise storage for AI, including workload isolation, maximized resource utilization and support for advanced NAND and AI workloads:

• Multi-dimension and multi-stage performance shaping algorithms are configurable via firmware, so QoS can be tailored for specific workloads at different stages in AI pipelines
• By managing read/write performance and QoS for each namespace or tenant, PerformaShape reduces performance fluctuations caused by resource contention and noise from neighboring workloads by isolating and guaranteeing performance and response for each virtual machine or container.
• PerformaShape helps SSDs fully utilize available resource by smoothing out I/O fluctuations and optimizing per-tenant resource usage to improve overall storage performance and reduce bottlenecks.
• Silicon Motion has integrated PerformaShape with our latest controllers supporting PCIe Gen5 and NVMe 2.0, high-capacity QLC NAND, and advanced features like FDP.

The QoS requirements of AI storage will continue to increase - traditional management techniques and even the advanced features offered by today's NVMe SSDs are insufficient in complex and dynamic AI data center environments.

Enterprises adopting SSDs powered by PerformaShape benefit from higher throughput, predictable QoS, and lower latency, all of which are critical for data-driven, AI-focused applications, workloads and pipelines.