Persistent Storage Latency: Impact on Stateful App Scaling

Persistent storage latency directly affects the scalability and performance of stateful applications. Delays in reading or writing data, even in milliseconds, can disrupt applications like e-commerce platforms, financial systems, or databases, especially during traffic spikes. For UK businesses, this can result in slower transactions, reduced user satisfaction, and revenue loss.

Key Takeaways:

• What is Storage Latency?
  It's the time delay in accessing data from persistent storage, measured in microseconds (µs) or milliseconds (ms). High latency slows down read/write operations, impacting system performance.

• Causes of Latency:

  • Storage type: HDDs are slower than SSDs or NVMe drives.
  • Protocols: Modern ones like NVMe over TCP outperform older standards like iSCSI.
  • Network congestion and distance amplify delays, especially in distributed systems.

• Impact on Scaling:
  Stateful applications, unlike stateless ones, rely heavily on storage. Increased latency disrupts data synchronisation, slows transactions, and complicates scaling.

• Solutions to Reduce Latency:

  • Use faster storage options (e.g., NVMe SSDs).
  • Optimise network infrastructure.
  • Implement caching strategies (local, distributed, or edge).
  • Align storage classes with workload demands (e.g., Azure Premium SSD v2 for production).

• Compliance Concerns:
  UK businesses must meet GDPR and data localisation requirements. Choosing compliant storage solutions and robust disaster recovery plans is essential.

Quick Tip:

Reducing latency and optimising scaling practices can cut costs by up to 30% and improve performance by 25%. For UK businesses, this ensures competitiveness while navigating strict regulatory frameworks.

Efficiently managing storage latency isn't just about speed - it's about ensuring your systems scale effectively, stay compliant, and deliver consistent performance.

What Is Persistent Storage Latency

Persistent Storage Latency Explained

Persistent storage latency refers to the time it takes for a system to retrieve data after a request has been made from persistent storage [2]. Essentially, it’s the delay experienced when an application accesses data from a database or file system. This metric is crucial for understanding the performance of any system that relies on stored data, as it directly impacts the speed of both read and write operations.

Unlike network or compute latency, which measure delays in data transmission or processing, storage latency focuses solely on the time it takes to access data from storage devices. While all forms of latency can slow down a system, storage latency often becomes the limiting factor for stateful applications that depend heavily on frequent read and write operations.

When storage latency is high, it can disrupt transaction speeds in databases, reduce the responsiveness of cloud applications, and impair the efficiency of virtualised environments [2]. For UK businesses, especially those in sectors like e-commerce or financial services, such delays can negatively impact user experience and even result in revenue loss.

Let’s take a closer look at what causes these delays, particularly in cloud-based systems.

Main Causes of Latency in Cloud Environments

Several factors contribute to storage latency in cloud and hybrid setups. At the core is the type of storage medium. Traditional hard disk drives (HDDs), with their mechanical components, inherently introduce delays. In contrast, solid-state drives (SSDs) and NVMe drives, which rely on electronic components, provide much faster data access times [2].

The protocol used to interface with storage also plays a significant role. Modern protocols like NVMe over TCP can significantly reduce latency compared to older standards such as iSCSI or SATA [2]. This is particularly important for UK businesses that operate hybrid cloud environments, where choosing the right protocol can make or break system performance.

Another contributing factor is network congestion, especially in distributed storage systems. When data travels over longer distances - such as internationally, where delays can reach up to 202ms [3] - latency increases. Additionally, infrastructure components like routers and switches, combined with high queue depths, can create bottlenecks that further slow down storage operations [1][2].

How to Measure Latency

Measuring latency accurately is a key step in improving the performance of stateful applications. Storage latency is typically measured in microseconds (µs) or milliseconds (ms), depending on the technology being used [2]. To ensure accurate results, measurements should ideally be conducted from outside the system being tested, simulating real-world conditions [7].

Latency is closely tied to metrics like IOPS (input/output operations per second) and throughput. Lower latency allows more operations to be completed in a given timeframe, directly influencing overall system performance [5].

For reference, a ping rate under 100ms is generally acceptable for most applications. However, for optimal performance, latency often needs to be within the 30–40ms range [1]. In high-performance environments, such as those supporting London’s financial sector, even slight improvements in microseconds can deliver a competitive edge.

In virtualised environments, reducing latency not only improves application performance but also ensures more efficient use of processor and memory resources [6]. This makes precise latency measurement an essential part of capacity planning and system optimisation efforts.

Storage Performance in 5 mins - IOPS, Latency & Throughput

How Storage Latency Impacts Stateful Application Scaling

Storage latency poses a significant challenge to scaling stateful applications, often causing a ripple effect of performance issues that stateless systems can avoid. When latency increases, it can disrupt critical scaling events and lead to system inefficiencies.

Take, for example, a PostgreSQL instance running on AWS EBS gp3 storage. This setup typically experiences a latency of 2–4 milliseconds for read-write operations, which limits it to fewer than 100 consistent transactions per second. With each transaction taking about 12 milliseconds, the maximum throughput is reduced to roughly 83 transactions per second. To make matters worse, a standard gp3 volume only provides a baseline of 3,000 IOPS[4].

In distributed databases, higher storage latency not only slows down transaction times but also leads to issues like connection pool exhaustion and reduced buffer cache efficiency. Complex configurations - such as data partitioning, session affinity, and backup mechanisms - can further amplify these latency-related problems during scaling events[4][8]. This creates a unique set of challenges for stateful systems trying to scale efficiently.

Scaling Problems for Stateful Applications

Stateful applications face distinct scaling challenges due to their reliance on persistent storage. Unlike their stateless counterparts, these systems must ensure data consistency across multiple instances, handle session information, and maintain ordered operations - all of which become more complex as they scale.

In Kubernetes environments, for instance, StatefulSets require stable network identities and persistent storage access. However, when storage latency increases, pod readiness is delayed during scaling events. This delay complicates data synchronisation across the cluster, as replication lags prevent new instances from serving requests effectively until the synchronisation process is complete.

Stateless vs Stateful Scaling Performance

The performance gap between stateless and stateful systems becomes evident when comparing their scaling approaches. The table below highlights key differences:

Stateless applications can respond to traffic spikes by simply adding more instances. This approach relies on tools like load balancers, auto-scaling groups, health checks, and caching mechanisms. On the other hand, stateful applications require more intricate infrastructure, including distributed caching, session management, data replication, and failover systems - all of which are directly affected by storage latency[8].

The financial implications are considerable. Organisations that use mixed architectures - combining stateless and stateful components - can reduce their operational costs by up to 30% while improving application performance by 25%[8]. However, stateful services often demand higher CPU and memory resources to handle session and synchronisation tasks, which means scaling these systems requires more powerful (and expensive) instances.

The difference becomes even more stark when modern storage solutions are introduced. NVMe storage, for instance, offers sub-200μs latency and millions of IOPS. By reducing overall latency to around 0.8 milliseconds, throughput can increase to approximately 1,250 transactions per second[4]. For UK organisations, adopting NVMe storage can significantly close the performance gap, making stateful applications more competitive in terms of scalability.

For businesses in industries like financial services or e-commerce - where both performance and data consistency are essential - understanding these distinctions is key to making informed architectural decisions about when to use stateful versus stateless approaches.

How to Reduce Persistent Storage Latency

Reducing persistent storage latency requires carefully choosing storage options that strike a balance between performance and cost - a crucial consideration for UK businesses navigating the 20% VAT rate. Here’s how optimising storage classes, improving network infrastructure, and leveraging caching can work together to minimise latency.

Choose the Right Storage Classes and Disk Types

Selecting the right storage class goes beyond simply opting for SSDs. For cloud-based environments, Azure Premium SSD v2 outperforms its predecessor, Premium SSD, while often being more cost-effective. It delivers up to 80,000 IOPS and 1,200 MB/s throughput, making it an excellent choice for production workloads that require consistent performance, especially during scaling events.

If you need extreme performance, Ultra Disk offers unparalleled specifications, providing up to 400,000 IOPS and 10,000 MB/s throughput. However, keep in mind that Ultra Disk is designed for IO-intensive tasks and cannot be used as an OS disk.

For AWS users, storage class selection should align with data access needs. For example, S3 Standard-IA is ideal for infrequently accessed data that still requires quick retrieval, while S3 Glacier Instant Retrieval provides cost-effective storage with millisecond access for archived data. When VAT is factored in, options like Premium SSD v2 often deliver better value compared to older alternatives.

Optimise Network Performance for Faster Storage Access

Reducing latency isn’t just about storage - it also depends on a well-optimised network. Upgrading components like NICs, routers, and switches can make a noticeable difference in latency reduction.

Implementing Quality of Service (QoS) and network segmentation can prioritise storage traffic, ensuring smoother performance during peak usage. For stateful applications, separating storage traffic from general application traffic ensures more consistent results, even during scaling.

Other network enhancements, such as DNS optimisation, can cut down lookup times. Choosing faster DNS providers and enabling DNS caching reduces delays. Similarly, adopting protocols like HTTP/2 or HTTP/3 can improve efficiency through header compression and multiplexing, which is especially useful for high-concurrency scenarios where storage APIs are frequently accessed.

Leverage Caching and Data Locality

Caching and data locality are powerful tools for cutting latency by bringing data closer to the application. Caching reduces backend load and speeds up response times by storing frequently accessed data nearby.

• Local caching offers the fastest access since there’s no network overhead. This is great for CPU caches or in-memory stores at the application level. However, its limited size and inability to share across instances can be a drawback for scaling stateful applications.
• Distributed caching provides scalability and shared access across multiple applications, making it ideal for microservices. Solutions like Redis Cluster and Memcached are popular choices, though they can introduce some latency and complexity.
• Edge caching helps reduce latency for geographically dispersed users by storing data closer to end users. Services like Cloudflare can significantly boost performance, though global cache invalidation can be challenging.

For caching to work effectively, strategies around population, eviction, and consistency must be carefully planned. Cache invalidation - deciding when to update or remove cached data - is especially tricky, requiring a balance between speed, memory usage, and data freshness.

Data locality, meanwhile, focuses on storing data closer to the applications that need it, cutting down on network-related delays. This is particularly relevant for UK businesses, where data residency rules and performance requirements often intersect.

Lastly, monitoring and observability are critical for maintaining cache performance over time. Without proper tracking, cache hit rates may decline, eroding the performance gains that caching provides - an issue that can undermine the scalability of stateful applications.

Best Practices for Scaling Stateful Applications

Scaling stateful applications requires careful planning and resource management. For UK businesses, this means juggling performance demands, regulatory compliance, and costs while addressing the challenges of persistent storage latency.

Match Scaling Policies with Storage SLAs

To maintain consistent performance during growth, it's essential to align your scaling policies with your storage SLAs. Autoscaling should dynamically adjust resources based on performance needs and storage agreements, ensuring your infrastructure can handle increased demand without compromising efficiency.

Keep a close eye on metrics like response times, CPU usage, queue lengths, and the time it takes to process messages (critical time). These indicators help fine-tune your scaling strategy, ensuring it reacts to actual workloads rather than relying solely on raw resource usage.

By adopting a mixed architecture, we were able to optimise our resources and significantly enhance our application's performance. - Kevin Lawrence, Strategic Planning Director, Global Technology, Nike [8]

Nike's success story highlights the importance of thoughtful architecture. They cut API response times by 40% using DreamFactory to manage REST APIs within a mixed architecture. This demonstrates how aligning scaling policies with infrastructure can lead to impressive performance gains.

When setting up autoscaling, start with extra capacity and adjust based on monitoring insights. Avoid the costly flapping cycle - where systems repeatedly scale up and down - by setting a reasonable buffer between scale-out and scale-in thresholds. Also, cap the maximum number of instances to prevent runaway costs during unexpected surges.

Meet Compliance and Disaster Recovery Requirements

Scaling stateful applications in the UK means adhering to strict data protection laws. Persistent storage latency can complicate compliance, but neglecting these requirements can lead to severe penalties. GDPR compliance is non-negotiable, with potential fines reaching £17 million or 4% of global turnover for breaches.

The upcoming UK Cyber Resilience Bill, modelled on the EU's NIS 2 Directive, will impose stricter cybersecurity standards on cloud service providers. This makes it vital to choose providers that meet these requirements and implement robust security measures.

Data localisation is another critical factor. Storing data within the UK ensures compliance with both GDPR and UK-specific laws. Neil Cattermull, Director of Cloud Practice at Compare the Cloud, advises:

When in doubt, prioritising data storage within your country of residence, under the protection of GDPR and UK law, offers a significant advantage in terms of compliance, security, and control. - Neil Cattermull, Director of Cloud Practice, Compare the Cloud [9]

Strong disaster recovery plans are essential to protect against data loss or system failures. Regular risk assessments can uncover vulnerabilities in your cloud setup, and appointing a Data Protection Officer (DPO) ensures ongoing compliance oversight.

The risks of non-compliance are stark. In 2018, insurance giant Anthem faced a $16 million fine after a cyberattack exposed sensitive health data for nearly 79 million people [10]. This underscores the importance of robust data protection measures.

Get Expert Help for Optimisation

Once you've established reliable scaling and compliance practices, the next step is optimisation. Managing persistent storage latency while scaling stateful applications demands expertise across cloud architecture, DevOps, compliance, and cost management. This is where consulting specialists can make a difference.

Hokstad Consulting provides tailored solutions to tackle these challenges. Their expertise spans cloud cost engineering, DevOps transformation, and automation, helping businesses address storage latency while maintaining performance and compliance. Their results speak volumes:

• One SaaS company cut costs by £96,000 annually through cloud optimisation.
• An e-commerce site achieved a 50% performance boost while reducing costs by 30%.
• A tech startup slashed deployment times from six hours to just 20 minutes.

Optimisation strategies reduce cloud spending by 30–50% and boost performance through right-sizing and automation. - Hokstad Consulting [11]

Specialist consultants assess workload demands to align performance, compliance, and scalability with the right environment. This comprehensive approach is invaluable for UK businesses navigating the complexities of cloud scaling.

Hokstad Consulting's track record includes up to 75% faster deployments, 90% fewer errors through DevOps transformation, and a 95% reduction in downtime. Their No Savings, No Fee model reflects their confidence in delivering measurable results, with fees capped as a percentage of the savings achieved.

For UK businesses aiming to scale stateful applications effectively while managing storage latency, expert guidance is a critical investment in staying competitive in today's cloud-driven world.

Conclusion: Managing Storage Latency for Better Scaling

Effectively managing persistent storage latency is a cornerstone for scaling stateful applications successfully. A great example of this is Spotify, which adopted Kubernetes with Persistent Volumes back in April 2024. This move allowed the platform to support millions of concurrent users and drove a 30% increase in active users [12]. Similarly, Grab's use of Kubernetes storage classes in 2023 led to a 25% reduction in storage management costs [12].

Key strategies like dynamic volume provisioning, automated tiering, and predictive analytics play a big role in reducing latency [13]. When combined with the right storage classes, improved network performance, and smart caching techniques, these methods provide a solid framework for scaling stateful applications. These approaches align seamlessly with the scaling policies discussed earlier in the article.

Software-defined storage has transformed IT operations by automating storage control and streamlining data services. – DataCore [13]

The financial implications of managing storage are hard to ignore. Storage alone accounts for 20–30% of cloud spending, and many organisations over-provision resources, with average utilisation rates sitting at just 30% [15]. With the global SaaS market projected to grow from around £259 billion in 2025 to over £928 billion by 2032 [14], efficient scaling practices are becoming increasingly vital for UK businesses. The cost-saving opportunities highlighted throughout this article demonstrate why optimising storage strategies is not just a technical necessity but also a financial one.

That said, staying compliant with GDPR while implementing these strategies adds another layer of complexity. This is where specialised expertise becomes crucial, and Hokstad Consulting's proven approach can make a significant difference.

Cloud cost optimisation is not a finance function - it's an engineering discipline. It demands an understanding of how compute, storage, network, and application-level architecture interact under production load. – Evermethod, Inc. [16]

For UK businesses, the key is to combine technical know-how, regulatory compliance, and cost efficiency. Hokstad Consulting’s No Savings, No Fee model, along with their ability to cut infrastructure costs by 30–50% [11], makes expert guidance a worthwhile investment, often paying for itself through the savings achieved.

The next steps are clear: prioritise proactive latency management, adopt proven scaling strategies, and rely on expert guidance to navigate the challenges of both technology and regulation. In today’s competitive market, businesses that master these elements will be well-equipped to scale effectively, control costs, and stay compliant.

FAQs