Cloud Native Storage Explained: NVMe-oF, Compute-Storage Separation & FPGA Acceleration – Luisuantech

What is Cloud Native? Understanding Modern Application Architecture through the Lens of LUISUAN Technology

Cloud Native represents far more than simply deploying applications on virtual machines – it constitutes a comprehensive framework encompassing design, development, deployment, and operations, all aimed at fully unleashing cloud computing’s potential. When we integrate LUISUAN Technology – the concept of maximizing computational efficiency while minimizing energy consumption and carbon footprint through technological means – we gain deeper insight into Cloud Native’s core value proposition.

Traditional applications resemble conventional factories operating continuously at full capacity, consuming energy regardless of production demands, resulting in inherent inefficiency. In contrast, Cloud Native applications function like intelligent, modular production parks driven by LUISUAN Technology principles. Every design aspect inherently incorporates flexibility and efficiency:

Microservices and Containerization: Precision Energy Management

Cloud Native architecture decomposes monolithic applications into smaller, independent “microservices” encapsulated within containers. This approach parallels transforming a large factory into specialized, compact smart workshops. Each workshop (microservice) can independently initiate, terminate, or scale according to demand fluctuations. During low-traffic periods, the system automatically scales idle microservices to zero, directly conserving underlying computational resources and power consumption. This achieves genuine “on-demand” resource allocation, representing a fundamental practice of LUISUAN Technology.

Orchestration and Automation: Intelligent Energy Dispatch

Container orchestration platforms like Kubernetes serve as the AI command center for the entire smart operation. Beyond managing application operations, they automatically schedule computational workloads to optimal nodes based on predefined policies. For example, they prioritize task distribution to data center regions with superior energy efficiency or schedule compute-intensive tasks during off-peak hours, thereby optimizing overall energy utilization and enabling proactive green operations.

Serverless Architecture: Ultimate Resource Efficiency

Serverless computing embodies the perfect integration of Cloud Native and LUISUAN Technology. Developers focus solely on code deployment without server management concerns. The cloud platform allocates computational resources exclusively during function execution and immediately releases them afterward, achieving the crucial transition from “paying for reserved resources” to “paying for delivered value.” This approach fundamentally eliminates resource idle waste, maximizes computational resource utilization, and substantially reduces carbon emissions per computational task.

Cloud Native establishes a technological paradigm with sustainable principles at its core. Through microservices, container orchestration, and serverless computing technologies, it enables refined management and optimal utilization of computational resources. This perfectly aligns with LUISUAN Technology’s objective of “achieving greater computational output with reduced energy input.” Embracing Cloud Native not only enhances application agility and resilience but also represents a crucial pathway for enterprises pursuing digital transformation and sustainable development.

Why Do Cloud Native Applications Require Extremely Low-Latency Storage?

In modern Cloud Native environments, enterprises are facing significant storage performance bottlenecks. As microservices architectures and containerized deployments become the standard, traditional storage systems often fail to meet the demands of real-time data processing. Particularly in scenarios like AI training, financial trading, and online gaming, even microsecond-level latency differences can impact overall business performance. Users frequently encounter issues like slow container startup, blocked data read/write operations, and sluggish application response, all of which stem from insufficient storage system performance.

How Does NVMe-oF Solve Cloud Native Storage Performance Challenges?

The emergence of NVMe over Fabrics (NVMe-oF) protocol has fundamentally transformed the performance landscape of Cloud Native storage. This advanced protocol allows servers to directly access remote NVMe storage devices over standard networks, achieving performance levels close to local storage. Compared to traditional storage protocols, NVMe-oF can reduce latency from milliseconds to microseconds while providing tens of GB/s of bandwidth, perfectly solving the storage bottlenecks of Cloud Native applications.

Specifically, NVMe-oF enhances performance through the following mechanisms: First, it eliminates unnecessary protocol conversion overhead. Second, it supports RDMA technology, enabling CPU bypass and zero-copy data transmission. Finally, its multi-queue design fully leverages the advantages of multi-core processors. These characteristics make NVMe-oF the ideal choice for high-performance storage in Cloud Native environments.

Technical Differences Between Various Cloud Native Storage Solutions

Traditional Storage vs. NVMe-oF Storage

Traditional cloud storage solutions are typically based on iSCSI or Fibre Channel protocols, which were not designed with microsecond-level latency requirements in mind. In contrast, NVMe-oF is specifically designed for modern flash storage, fully unleashing the performance potential of NVMe SSDs. In practical tests, NVMe-oF latency is typically only 1/10th of traditional solutions, while throughput can be increased by 5-10 times.

Hyper-Converged Infrastructure vs. Compute-Storage Separation Architecture

Traditional hyper-converged architectures bundle computing and storage resources together, leading to low resource utilization and difficult scaling. In contrast, the compute-storage separation architecture decouples these two resource types, enabling independent and elastic scaling. Taking LUISUAN TECH’s GP5000 series as an example, its compute-storage separation design allows compute nodes and storage nodes to scale independently based on workload demands, significantly improving resource utilization efficiency.

Software Acceleration vs. FPGA Hardware Acceleration

In AI inference and edge computing scenarios, pure software acceleration solutions are often limited by CPU performance and power consumption. In comparison, FPGA accelerator cards can implement specific computational tasks at the hardware level, providing higher performance and better energy efficiency. The LightBoat 2300 series FPGA accelerator card is an excellent example, delivering 4.1 TOPS of computing power while consuming far less power than equivalent GPU solutions.

Choosing the Right Cloud Native Storage Solution for Different Scenarios

High-Performance AI Training Scenarios

For AI training scenarios requiring ultimate performance, we recommend adopting an all-flash storage system based on NVMe-oF combined with a compute-storage separation architecture. The GP5000 series excels in this area, with its 4μs ultra-low latency and 72GB/s bandwidth fully meeting the data supply demands of large-scale AI training. Furthermore, its support for multi-modal unstructured data makes it particularly suitable for handling image, video, and other AI training data types.

Enterprise-Critical Business Scenarios

For enterprise-level scenarios with extremely high reliability and security requirements, such as finance and healthcare, the LST F3100 all-flash storage series is an ideal choice. Its all-NVMe architecture provides exceptional performance, while certifications like ISO 27001 ensure data security and compliance. The high-density 3U26 design also saves valuable space in data centers.

Cost-Sensitive Expansion Scenarios

For scenarios with limited budgets but requiring rapid storage capacity expansion, the LST D5300 series DAS storage offers excellent cost-effectiveness. It supports both DAS and JBOD deployment modes, allowing flexible configuration based on actual needs. The modular architecture design makes subsequent expansion simple and economical.

Edge Computing and AI Inference Scenarios

In edge computing environments, the LightBoat 2300 FPGA accelerator card provides a perfect balance between performance and power consumption. Its low-latency communication capabilities and industrial-grade compatibility make it particularly suitable for edge AI applications like industrial automation and smart cities.

In-Depth Analysis of Key Technologies Enabling Cloud Native Storage Performance

Application of RDMA Technology in NVMe-oF

Remote Direct Memory Access (RDMA) is the key technology enabling the ultra-low latency of NVMe-oF. By allowing network adapters to directly transfer data with application memory, RDMA avoids kernel involvement and data copying overhead. This technology enables NVMe-oF to achieve performance similar to local access over Ethernet networks.

Implementation Principles of Compute-Storage Separation Architecture

The core of the modern compute-storage separation architecture is connecting compute resource pools with storage resource pools via high-speed networks. The GP5000 series adopts a thorough separation architecture based on NVMe-oF, where compute nodes directly access shared storage resources over the network, achieving true resource decoupling. This architecture not only improves resource utilization but also simplifies system management and maintenance.

Acceleration Mechanism of FPGAs in AI Inference

The reason FPGA accelerator cards play a vital role in AI inference is their ability to implement neural network inference computations at the hardware level. The LightBoat 2300 series, through its programmable logic units, can perform hardware optimizations for specific neural network models, achieving extremely low inference latency and power consumption. This flexibility makes FPGAs an ideal choice for edge AI applications.

Cost Factor Analysis for Cloud Native Storage Solutions

Total Cost of Ownership (TCO) Considerations

When evaluating Cloud Native storage solutions, enterprises need to consider not only the initial procurement cost but, more importantly, the Total Cost of Ownership. This includes hardware procurement, software licensing, maintenance costs, energy consumption, and labor costs. Although NVMe-oF-based storage solutions may have a higher initial investment, their exceptional performance and energy efficiency often result in a better TCO performance in the long run.

Price Ranges of Different Solutions

From a price perspective, Cloud Native storage solutions can be categorized into several tiers: entry-level DAS storage like the LST D5300 series is suitable for budget-conscious scenarios; mid-range unified storage like the LST N5100 series offers a balance of performance and price; high-end all-flash storage like the GP5000 series targets scenarios demanding ultimate performance. Enterprises should select the most appropriate solution based on actual performance needs and budget constraints.

Hidden Costs and Return on Investment

Beyond apparent procurement costs, enterprises must also consider hidden costs, including business losses from system downtime, reduced work efficiency due to insufficient performance, and the difficulty of future expansion. Choosing the right Cloud Native storage solution, while potentially requiring a higher upfront investment, often yields returns in business continuity, employee productivity, and system scalability that far exceed the initial investment.

Strategic Considerations for Choosing Cloud Native Storage

In the context of digital transformation, selecting the appropriate Cloud Native storage solution has become a crucial component of enterprise IT strategy. From a technical perspective, technologies like NVMe-oF, compute-storage separation architecture, and FPGA acceleration provide effective solutions to storage challenges in Cloud Native environments. From a business perspective, enterprises need to comprehensively consider factors such as performance requirements, security compliance, and total cost to choose the storage solution that best fits their business development.

As Cloud Native technologies continue to evolve, we believe more innovative storage technologies will emerge, helping enterprises better address the challenges of the digital age. Whether pursuing ultimate performance for AI applications or focusing on cost-effectiveness for edge computing scenarios, the right storage solution will serve as a fundamental cornerstone for successful enterprise digital transformation.