What is storage architecture? Storage architecture suggestions (2023)

Your system's storage architecture is a critical component for transferring data and accessing important information. It forms the basis for data access in a company. Depending on your operations and the needs of your business, specific storage architectures may be required to enable employees to reach their full potential.

So what is an IT storage architecture and how does it fit into the day-to-day tasks you need to do? To help you understand storage optimization, we outline the details of storage architecture and what you need to know to make informed decisions about the design and maintenance of one of the most critical components of your business.

What is network storage architecture?

Network storage architecture refers to the physical and conceptual organization of a network that enables data transfer between storage devices and servers. It provides the backend for most enterprise-level operations and allows users to get what they need.

The configuration of a storage architecture can determine which aspects are prioritized, such as cost, speed, scalability, or security. Since different companies have different requirements, equipping an IT storage architecture can be a big factor in the success and ease of use of day-to-day operations.

The two main types of storage systems offer similar functionality but differ greatly in implementation. These storage types include network attached storage (NAS) and a storage area network (SAN).

1. Network Storage (NAS)

A NAS system connects a computer to a network to deliver file-based data to other devices. Files are typically kept across multiple storage units arranged in a redundant array of independent disks (RAID), which helps improve performance and data security. This user-friendly approach appears as a network-mounted volume. Security, management and access are relatively easy to control.

NAS is popular for smaller businesses because it allows for local and remote file sharing, data redundancy, 24/7 access, and easy upgrades. In addition, it is not too expensive and very flexible. The downside of NAS is that it may require server upgrades to keep up with increasing demand. There can also be latency issues with large files. It probably wouldn't be noticeable with small file sizes, but when you're working with large files like videos, this latency can interrupt many processes and slow you down significantly.

2. Storage Area Network (SAN)

SAN creates a storage system that works with consolidated block data. It bypasses many of the limitations caused by TCP/IP protocols and LAN congestion and offers faster access speeds than a NAS system. Part of the reason for this speed improvement is the way files are served. The NAS uses Ethernet to access files, which are then delivered over a high-speed Fiber Channel, allowing for fast access. NAS improves accessibility and appears like external hard drives to users.

Because of its complexity, the SAN is often reserved for large companies that have the capital and IT department to manage it. For businesses with high-demand files like video, the SAN's low latency and high speeds are a key benefit. It also distributes and prioritizes bandwidth fairly across the network, which is ideal for businesses with high-speed traffic, such as B. E-commerce websites. Other SAN bonuses include extensibility and block-level file access. The main disadvantage of SAN is the cost and maintenance challenges, which is why it is typically used by large companies.

Ideas

Within these storage systems you will find a variety of configurations. Different structures can affect the performance of each storage system. The components of these settings include:

• A front-end interface:This interface is typically connected to the access layer of the server infrastructure and allows users to interact with the data.
• Main node:A master node is a node that communicates with compute nodes using information from outside the system. It manages the compute nodes and takes care of monitoring functions and node states. They are often hosted on a more powerful server than the compute nodes.
• Compute Nodes:A compute node helps perform a variety of operations such as calculations, file manipulation, and rendering.
• A consistent file system:With a shared parallel file system on the server cluster, compute nodes can easily access file types and provide better performance.
• A high-speed fabric:Communication between nodes requires a fabric that offers low latency and high bandwidth. Gigabit Ethernet and Infiniband technologies are the main options.

Below are some of the architectural styles you can find.

1. Layered model

With a tiered data center, HTTP-based applications leverage separate tiers for web, application, and database servers. It allows for a clear separation between tiers, which improves security and redundancy. If one layer is compromised, the others are usually safe with the help of firewalls between them. If a server goes down or needs maintenance, other servers in the same tier can keep things running.

2. Cluster architecture

In a clustered system, the data resides behind a single compute node. They don't share memories with each other. The input-output (I/O) path is short and direct, and the system connection has extremely low latency. This simple approach is actually the most feature-rich because it's so easy to add data services.

One approach to the cluster architecture model is to “federation models' on it to enlarge it a little. This causes the I/O to bounce until it hits the node that contains the data. These federated layers require additional code to redirect data, slowing down the whole process.

3. Tightly coupled architectures

These architectures distribute data across multiple nodes running in parallel and use a grid of multiple controllers for high availability. You have a significant amount of inter-node communication and work with many types of operations, but the master node organizes the input processing. /O operations.

For a more complex design, a tightly coupled architecture requires a lot more code. This aspect limits the availability of data services and makes them rarer in the main code stack. However, the more tightly coupled a memory architecture is, the better it can provide predictably low latency. As tight coupling improves performance, it can be difficult to add nodes and scale up, inevitably adding complexity to the entire system and creating room for error.

4. Loosely coupled architectures

This type of system does not share memory between nodes. The data is shared between them with a significant inter-node communication overhead in the writes, which can be expensive to execute given the cycles. The transmitted data is transactional. Low latency is sometimes hidden in write storage locations that are also low latency, like SSDs or NVRAM, but there will still be more movement in a loosely coupled architecture, creating additional I/O.

Similar to the tightly coupled architecture, this one can also follow a "federation" pattern and scale out. It usually involves grouping nodes into subgroups using special nodes called mappers.

This architecture is relatively easy to use and works well for distributed reads, where data can reside in multiple places. Since the data is in more than one point, multiple nodes can store it and speed up access. This factor makes this architecture particularly well-suited for server and storage software and hyper-converged transactional workloads.

Just as each node does not share memory, neither do they share code that is separate from other nodes. This design has some effects. If data is heavily distributed across writes, you'll see higher latency and less efficient I/O operations per second (IOPS). If you have less spread, you might get lower latency, but you won't see as much concurrency on reads as you otherwise would. Ultimately, the loosely coupled architecture can offer all three options - low write latency, high concurrency, and high scale - when the data is substrateed are and you do not write a large number of copies.

5. Distributed Architectures

While this approach may resemble a loosely coupled architecture, it works with non-transactional data. It does not share memory between nodes and data is distributed across nodes. Data is grouped in a node and occasionally distributed as a security measure. This type of system uses object and non-POSIX file systems.

This type of architecture is less common than many others, but is used by extremely large enterprises because it can easily handle petabytes of storage. Its parallel processing model and speed make it a great choice for search engines. It is incredibly scalable due to its grouping methods and its independence from transactional data. Because of its simplicity, a distributed rather than shared architecture is typically software only and not hardware dependent.

What are the elements of memory architecture?

Designing a storage architecture is often a balance between different functions. Improve one aspect and you can make another worse. You need to figure out which features are most important for your type of work and how to get the most out of them. You also need to balance the cost against the needs of the organization. Here are some of the most common aspects of memory architecture design.

1. Data standard

Depending on the nature of your work, you may have a random or sequential pattern of I/O requests. The type of pattern you work with most affects how the hard drive components reach the area that contains the data.

• Arbitrarily:Data is written to and read from multiple locations on the platter in a random pattern, which can affect the effectiveness of a RAID system. The controller cache uses patterns to predict which blocks of data it needs to access next for reading or writing. If the data is random, there is no pattern to work with. Another problem with a random pattern is the increase in search time. Because data is distributed across data blocks, the disk head must move each time information is requested. The platter arm and head must physically move, which can increase seek times and affect performance.
• Sequentially:The sequential pattern works in an orderly fashion, as you can imagine. It is more structured and offers predictable data access. With this type of layout, the RAID controller can more accurately estimate which data blocks will need to be accessed next and cache that information. It increases power and prevents the arm from moving too much. These sequential applications are often built with throughput in mind. You'll see sequential patterns with large file types like videos and backups being written to the drive in continuous chunks.

Under random workloads, hard drive performance depends on how fast it is spinning and how long it takes to access data. When the disk moves faster, it delivers more IOPS. When performing sequential operations, all three major hard drive types—SATA, SAS, and SSD—offer similar levels of performance. However, in general, sequential patterns are common with large or streaming media files, which are better suited to SATA drives. Random patterns occur with small files or inconsistent storage requirements, such as virtual desktops. SAS and SSD are generally the best choices for random patterns.

In terms of spin speeds and access times, see how the drives stack up.

• SATA:SATA drives have relatively large hard disk platters that can handle random workloads due to their slow speed. A large disk size can result in longer seek times.
• SAS:These units have smaller platters with higher speeds. You can significantly reduce the search time.
• SSD:The SSD drive is ideal for extremely high-performance workloads. It has no moving parts, so there is almost no seek time.

2. Layers

In a data center storage architecture, you typically see multiple layers of hardware that perform different functions. These layers usually include:

• middle class:This first layer creates the high-speed packet switching required for data transmission. It connects to many aggregation modules and uses a redundant design.
• Aggregation layer:The aggregation layer is where traffic flows through and encounters services like firewall, network analysis, intrusion detection, and more.
• access layer:At this level, the servers and the network physically connect. It includes switches, cables, and adapters to connect everything together and give users access to data.

3. Performance vs Capacity

Drive capabilities are constantly changing. Think about how expensive a 1 terabyte (TB) hard drive was five years ago, and how expensive they areThe first 1 megabyte (MB) hard drive cost $1 million. Disk capacity used to be so small that SAN systems didn't have to worry about the number of disks not generating enough IOPS per gigabyte (GB) - they had enough of it. Currently, SATA drives and SAS drives can offer similar capacities, with the SATA drive using significantly fewer hard drives. Fewer disks reduce the number of IOPS generated per GB.

When your work involves lots of random I/O interactions or extreme demands, using SATA hard drives can quickly become an IOPS bottleneck before capacity is reached. One option here is to add solid-state cache to the hard drives, which can significantly improve random I/O performance.

4. RAID Considerations

When using a RAID system, there is one more factor to consider: the parity penalty. This term refers to the performance cost of protecting data with RAID and only affects write operations. If your job is write-sensitive, the parity penalty may affect you more, since RAID is less stable for write tasks. Different levels of RAID protection can also affect the amount of overhead.

Determining overhead is a complex calculation thatyou can find outwith some information about your future system.

Keep in mind that some types of triggers can benefit from thisdifferent configurations. For example, an SSD can have a RAID1+0 configuration for better performance, while a SATA drive with a RAID6 configuration provides additional recovery security and high capacity.

How is the memory architecture structured?

When designing the storage architecture, we need to look closely at the needs of the business and the environment. It probably goes without saying, but meetings and discussions will help determine your needs. You should also hire professional services to help with the details and do the architecture yourself.

Once you've determined what your data pattern looks like, you can start checking things like:

• capacity requirements
• transfer rate
• IOPS
• Additional functions such as replication or snapshots

If you can't get any data on these aspects, take a close look at the operating system and applications to get started. If you encounter a random data pattern, try matching the capacity to the IOPS requirements. Prioritize capacity and throughput for sequential workloads. Its values ​​in MB per second (MB/s) for sequential data often exceed requirements.

Tips for designing a storage architecture

Of course, we can't fit everything you need to know about storage architecture into one article, but here are a few more of our tips to help you create the ideal storage structure without too much of a headache.

• Evaluate the cost from the start:Keeping costs in mind when designing can help you make realistic decisions that work over the long term. You don't want to end up with an architecture that needs to be reorganized immediately because it's too expensive to maintain or doesn't meet the needs of the business. Be realistic about the cost of a storage architecture to account for the business budget.
• Find areas where you can compromise:You won't be able to prioritize everything. In many cases, focusing on one aspect will degrade the quality of another. A high performance system is expensive and potentially less scalable. A scalable system may require more skillful management and may lose speed. Talk to stakeholders about what aspects of the system are needed and why so you can evaluate possible trade-offs given business needs.
• Work in phases:Your first draft will not match your final one. During the project work you will encounter specific challenges and learn more about the technical details of your system. Try not to stick to a plan and allow the architecture to change organically as you discover more information.
• First identify your needs:While it can be tempting to dive straight into the specific components you plan to use, identifying more abstract requirements is an excellent place to start. Think about the state of your data, what formats to work with, and how to communicate with the server. Try to collect as much information as possible about the tasks required. This approach allows you to work down the chain and find solutions that meet the needs of more than one operation.

Work with an IT professional

As you've probably noticed, enterprise storage architecture is a complicated technology. And it's very important to try to put the pieces together if you don't know what you're doing. This is where IT professionals come into play.

Here at Worldwide Services we know the data and we know the business. Our team of experts can design the software architecture from scratch, with the needs of your business as the top priority. Whether you need a system built for speed, scalability, or anything else, we can help. we can alsopay maintenanceto an existing storage architecture. To learn more about our services,contact usToday.