Tiered Storage Guide: Tiers Types and Solutions

Organizations today have to use storage rationally as large amounts of data can increase storage costs and lead to data proliferation. Data storage media differ in terms of cost, write/read speed, etc., and different types of data should be stored on the most efficient media so as to save costs and resources.

For example, storing backups on high-speed SSDs (solid-state drives) devices is unnecessarily costly as the high speed of SSDs is not required for this type of secondary data. In contrast, storing production virtual machines (VMs) on hard disk drives (HDDs) with low RPM (revolutions per minute) may be budget-friendly but will not meet the performance requirements for primary systems.

For this reason, storage types should be categorized for using each storage type to store the appropriate data by using storage tiering.

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What Is Storage Tiering?

Storage tiering is a data storage management strategy used to optimize the performance and cost efficiency of a storage system by categorizing data into different tiers based on its characteristics and access patterns. The main goal of storage tiering is to ensure that the most frequently accessed and critical data is stored on high-performance storage media while less frequently accessed data or less critical data is stored on lower-cost storage.

This approach allows organizations to store their data on various types of storage media, such as fast and expensive solid-state drives (SSDs) or slower but more cost-effective hard disk drives (HDDs), depending on the data’s value and usage patterns.

Storage tiering begins with classifying data into different categories or tiers based on criteria like frequency of access, importance, and performance requirements. This classification can change over time if the working process requires that. The number and types of storage tiers can vary – from 3 to 7 – depending on the storage infrastructure.

A tiered storage architecture helps organizations reduce storage costs by allocating high-cost storage resources only to the data that requires it. This ensures that expensive resources are not wasted on data that doesn’t benefit from them. By placing hot (frequently accessed) data on high-performance storage media and cold (less frequently accessed) data on lower-performance media, storage tiering optimizes overall system performance.

Data Classes for Tiered Storage

Data classes in a tiered storage architecture refer to the categorization or classification of data based on specific attributes or characteristics. These classes create a data hierarchy and help determine where data should be stored within a tiered storage system. This approach ensures that data is placed on the most appropriate storage tier to balance performance, cost, and accessibility. The specifics of data classes can vary depending on the organization’s needs and storage infrastructure. The common attributes used for data classification are:

• Access frequency. One of the primary criteria for data classification is how frequently it is accessed by users and applications. Data that is regularly and actively used (hot data) should be stored on high-performance storage tiers, such as SSDs or NVMe drives, to ensure quick access times. In contrast, infrequently accessed data (cold data) can be placed on lower-cost storage tiers like HDDs or cloud storage.
• Criticality or importance. Some data is more critical to an organization’s operations or compliance requirements than others. Critical data may need to be stored on more reliable and resilient storage tiers, such as RAID (redundant array of independent disks) or cloud storage with redundancy, to minimize the risk of data loss.
• Data type. Different types of data, such as database files, multimedia content, application logs, or archival documents, may have varying storage requirements. For instance, multimedia files may require high throughput and capacity, while logs can be stored on slower storage as long as they are retained for compliance purposes.
• Retention period. Data with specific retention or compliance requirements may need to be stored on tiers that can guarantee data integrity and availability for the required duration. Compliance data often requires long-term retention and therefore, it may be stored on more reliable tiers of storage.
• Size. Large data objects may benefit from being stored on storage tiers optimized for capacity, while small, frequently accessed data may require storage with low latency and high I/O performance.
• Data lifecycle. Data goes through various stages in its lifecycle, from creation and active use to archival or deletion. Data classes should account for these stages and move data between tiers as needed. For example, newly created data may start on a high-performance tier but gradually be moved to lower-cost tiers as it becomes less active.
• Cost sensitivity. Organizations often have budget constraints. Data classes can help align data storage costs with budgetary considerations by ensuring that more expensive storage resources are reserved for data that justifies the cost.
• User or application requirements. Different users or applications may have specific storage needs. Data classes can take into account these requirements to ensure that each group receives the necessary storage performance and capacity.

Once data is classified into these classes, policies and algorithms are used to manage data placement and movement within the tiered storage infrastructure. This ensures that data is continuously optimized for performance and cost efficiency while meeting organizational needs and access patterns.

Classification with hot, warm, and cold storage

The common type of data classification in tiered storage systems is classifying data as mission-critical, hot, warm, and cold. These classes help determine how data is stored, managed, and accessed within the storage infrastructure. In this case, the data classes used in tiered storage strategies include:

• Mission-critical data. This data class pertains to data that is absolutely essential for an organization’s core operations. Mission-critical data requires the highest level of performance, reliability, and availability. It is typically stored on the most resilient and high-performance storage media available, such as redundant SSD arrays or fault-tolerant storage systems.
• Hot data. Hot data refers to data that is actively and frequently accessed. This data is typically of high importance to the organization and requires fast response times and high-performance storage. Hot data is often stored on the highest-tier storage media, such as solid-state drives (SSDs) or NVMe drives, to ensure low latency and rapid access.
• Warm data. Warm data represents data that is accessed less frequently than hot data but is still actively used. This data class typically resides on a tier below hot data in terms of performance, such as high-performance hard disk drives (HDDs) or hybrid storage solutions. While warm data may not require the fastest storage, it still needs to be readily available for efficient access.
• Cold data. Cold data includes data that is rarely accessed, historical, or archived. This data is often considered less critical and is stored on lower-cost storage tiers, which could be traditional, slower HDDs, or even archival storage options like tape or cloud-based cold storage. The emphasis for cold data is on long-term retention and cost savings.

The number of data classes can depend on the number of storage tiers in the storage classification model. Organizations can classify data in a more complex way by using the following data classes additionally to the classes explained above:

• Backup and disaster recovery data. Data used for backup and disaster recovery purposes is often categorized separately. These data classes focus on ensuring that data can be reliably and quickly restored in case of data loss or system failure. Backup data may be stored on disk-based systems, while long-term retention copies could be stored on tape or in the cloud.
• Compliance data. Data that needs to adhere to regulatory compliance requirements, such as financial records or healthcare data, may have specific storage needs. Compliance data classes ensure that this data is stored securely, with features like encryption and strict access controls, and retained for the required duration.
• User or departmental data. Some organizations classify data based on its source, such as data generated by specific departments or users. This approach can help allocate storage resources based on the needs of different organizational units.
• Temporary or cache data. Data classes for temporary or cache data may include data that is short-lived and can be stored on high-speed storage tiers for fast access, with the understanding that it can be discarded or replaced when no longer needed.
• Tier migration data. In some cases, data classes are used to identify data that is actively moving between storage tiers based on access patterns. For example, data that starts as hot but becomes less frequently accessed over time may migrate to warmer or colder storage tiers.

These data classes can serve as guidelines for storage administrators and automated storage management systems to make informed decisions about where to place data within a tiered storage infrastructure.

Multi-Tiered Storage Types

Multi-tiered storage refers to a storage architecture where data is categorized into different tiers based on its performance and accessibility requirements. Each tier represents a specific level of storage performance and cost. The goal is to ensure that data is stored on the most appropriate tier to optimize both performance and cost efficiency. Below, you can see common storage tiers, starting with Tier 0:

• Tier 0 represents the highest-performing storage tier in a multi-tiered storage system. It is often composed of the fastest and most expensive storage media available, such as enterprise-grade solid-state drives (SSDs) or NVMe (Non-Volatile Memory Express) SSDs. Data stored in Tier 0 is typically mission-critical and requires extremely low latency, high I/O performance, and rapid data access. It is used for applications and data that demand the highest levels of performance.
• Tier 1 is the next level down in terms of performance and cost. It typically consists of high-performance hard disk drives (HDDs), hybrid storage arrays (combining SSDs and HDDs), or faster SSDs, which are not as costly as those in Tier 0. Data in Tier 1 is important but may not require the absolute fastest storage available. This tier is suitable for applications and data that need good performance but can tolerate slightly higher latency compared to Tier 0 data.
• Tier 2 represents a lower-cost storage tier with somewhat slower performance compared to Tier 1. It often includes traditional HDDs or cloud-based storage solutions. Data in Tier 2 is typically less frequently accessed or less critical to real-time operations. This tier is suitable for archival data, backups, and data that can tolerate longer access times.
• Tier 3 is the lowest-cost storage tier in a multi-tiered storage system. It typically includes archival storage solutions, such as tape libraries and cloud-based cold storage. Data in Tier 3 is rarely accessed and is primarily retained for compliance, regulatory, or long-term archival purposes. It offers the lowest performance but the most cost-effective storage.

Some organizations using primarily on-premises storage dedicate additional special tiers for storage in the public cloud and backup storage:

• Cloud Tier. In some multi-tiered storage architectures, a separate Cloud Tier is used to store data in a cloud storage service like Amazon S3 or Azure Blob Storage. This allows organizations to take advantage of scalable and cost-effective cloud storage for data that may not fit neatly into on-premises tiers. Data in the cloud tier can be accessed over the internet when needed.
• Backup Tier. While not always considered a primary storage tier, backup storage is a critical part of the storage hierarchy. Backup data is stored on disk-based systems or tape libraries, depending on the organization’s backup strategy. The focus is on data protection and rapid recovery in case of data loss or disasters.

How many tiers are usually used by organizations?

The number of tiers used by organizations in their storage architectures can vary widely depending on their specific needs, budget constraints, and the complexity of their data management requirements. However, in practice, many organizations commonly implement a three-tier storage hierarchy as a starting point (Tier 0, Tier 1, Tier 2).

Many organizations start with these three tiers as a foundation and then customize their storage infrastructure to meet their specific needs. They may add additional tiers or adopt specialized storage classes as their data requirements evolve. For example:

• Some organizations might add a Tier 4 or Tier 5 for long-term, deep archival storage, which could involve technologies like tape libraries or very low-cost cloud storage.
• Others might implement a Cloud Tier for offsite backups and disaster recovery purposes, using cloud storage services like Amazon S3 or Azure Blob Storage.
• Hybrid cloud strategies can also introduce more tiers, including cloud-based tiers for data that needs to be seamlessly moved between on-premises and cloud storage.

The key is to design a storage architecture that aligns with the organization’s data access patterns, performance requirements, and budgetary considerations. It’s also important to implement effective data management and tiering policies to ensure that data is stored on the appropriate tier based on the changing needs of this data over time. As data storage technologies continue to evolve, organizations may adjust their tiered storage strategies to take advantage of new innovations and cost-efficient solutions.

These common storage tiers can be summarized in a table with brief explanations and typical use cases:

Please note that the names and characteristics of storage tiers can vary between organizations and storage vendors. The above table provides a general overview of common storage tiers and their typical use cases, but specific implementations may differ based on the organization’s needs and available technologies.

Where Storage Tiers Are Used

Storage tiering is a storage management strategy that can be used both on-premises (within an organization’s own data centers or private cloud environments) and in the public cloud. It is a flexible approach that can be applied to various storage architectures to optimize data placement and access patterns.

Storage tiering on-premises

Storage tiering is used in the following environments focused on the on-premises (local) infrastructure:

• Traditional data centers. In traditional on-premises data centers, storage tiering is commonly used to manage data stored on different types of storage media, such as SSD, HDD, and tape libraries. Organizations implement storage tiering to optimize performance, cost, and data availability within their own infrastructure.
• Private clouds. Many private cloud environments incorporate storage tiering to efficiently manage data across different types of storage resources. This is especially important in private cloud setups where resources need to be allocated dynamically to support various workloads.
• Hybrid clouds. In a hybrid cloud environment, which combines on-premises infrastructure with public cloud resources, storage tiering can be used to optimize data placement across both environments. Organizations may use tiering policies to determine which data should reside on-premises and which should be moved to the public cloud for cost efficiency or scalability.

Storage tiering in the public cloud

As for the public cloud, storage tiering is used in the following environments:

• Public cloud storage services. Public cloud providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offer their own cloud storage tiering options as part of their cloud storage services. For example, AWS offers S3 storage classes (Standard, Intelligent-Tiering, Glacier, etc.), each tailored for different performance and cost requirements.
• Object storage. Object storage services in the public cloud often support storage tiering to allow customers to choose the most appropriate storage class for their data. This is beneficial for optimizing costs and access times.

Automated Storage Tiering

Automated storage tiering and storage tier optimization are techniques used in modern data storage management to ensure that data is placed on the most suitable storage tier effectively and in the proper time.

Automated storage tiering is a data management technique that involves automatically and dynamically moving data between different storage tiers based on specific policies and criteria. These policies are typically defined by storage administrators or set by intelligent storage management software. The primary goal of automated storage tiering is to optimize the use of storage resources by ensuring that data is stored on the most appropriate tier at any given time.

Automated storage tiering empowers you to dynamically optimize your storage tiering, continuously monitoring data usage and access to determine data priorities and the required tiering levels. When you use automated storage, you set your preferred thresholds and the automation takes care of the rest.

As data usage reaches predefined thresholds, it is relocated accordingly. If data access frequency increases, it moves to a lower latency tier. When data goes unused, it is shifted to a lower-cost, higher-latency tier. This approach optimizes both your costs and performance with minimal effort and no need for ongoing maintenance.

Automated storage tiering facilitates policy-driven data transfers between storage tiers, thus aligning with user performance and capacity needs. This feature efficiently works with your existing tiered storage architecture and streamlines data management through automation. Automated storage tiering improves performance optimization and cost efficiency due to real-time adjustment and quick data movement.

Storage tier optimization is a broader concept that encompasses various strategies, including automated storage tiering, to ensure that an organization’s storage infrastructure is efficiently managed and utilized. While automated storage tiering is a key component of storage tier optimization, other techniques and best practices may also be involved.

Tiering vs Caching

Tiering and caching are two distinct techniques used in data storage and management – they serve different purposes. The terms tiering and caching are often used interchangeably by mistake, but they refer to two different storage acceleration techniques. Both involve placing frequently accessed or hot data on high-speed media like flash. However, the similarities mostly end there.

Caching temporarily stores data on a high-performance medium like DRAM or solid-state memory to boost performance. The cache sits between the application and the back-end storage. The same data also resides on a lower storage tier, usually an HDD. Data is copied to the cache, but the original data remains in its initial location. Caching is essentially a one-way transaction and the cache nullifies data after use.

Storage tiering, on the other hand, physically moves data between storage devices. When data is identified as hot, this data is relocated to a high-speed tier, making the standard tier devoid of a copy. When the data cools down, it’s moved back to the standard tier. Storage tiering involves moving data instead of merely copying it, both from slower storage to faster storage and vice versa.

Both storage tiering and caching improve data accessibility, but they differ in how they utilize storage for frequently accessed data. Caching creates copies, whereas storage tiering identifies data and moves it without creating additional copies.

Thus, tiering focuses on optimizing long-term data placement across different tiers of storage to achieve a balance between performance and cost, while caching aims to accelerate data access by temporarily storing frequently accessed data in a high-speed buffer. The choice between tiering and caching depends on the specific requirements of the application or storage system and the nature of data access patterns. In some cases, organizations may use both techniques in combination to achieve the best overall performance and cost efficiency.

Tiered Storage and Hierarchical Storage Management

Tiered storage and hierarchical storage management are both strategies used in data storage management, but they differ in their granularity, data movement mechanisms, and primary objectives. Tiered storage focuses on categorizing data into discrete tiers of storage media based on characteristics, whereas hierarchical storage management focuses on transparently migrating individual files or objects between primary and secondary storage to improve primary storage efficiency and cost savings.

Storage Tiering Benefits

Storage tiering offers several significant benefits for organizations seeking to optimize their data storage infrastructure. The key advantages of implementing storage tiering are:

• Improved performance. By placing frequently accessed or critical data on high-performance storage tiers, such as solid-state drives or NVMe storage, storage tiering can significantly enhance system performance. This results in reduced latency and faster data access times for applications and users, leading to improved productivity and user satisfaction.
• Efficient resource utilization. Storage tiering ensures that each storage tier is used efficiently, avoiding over-provisioning of high-performance storage media and underutilization of lower-cost storage. It maximizes the return on investment (ROI) for storage infrastructure.
• Cost optimization. Storage tiering helps organizations allocate expensive storage resources only to data that requires high performance, while less critical or infrequently accessed data can be stored on lower-cost tiers, such as hard disk drives or cloud storage. This cost optimization leads to potential cost savings in hardware and operational expenses.
• Balanced workloads. Storage tiering can help distribute data and workloads across different tiers, reducing contention for resources. This is particularly valuable in environments with mixed workloads, where some applications require high performance while others have less demanding storage requirements.
• Adaptive data management. Data access patterns can change over time. Storage tiering solutions continually analyze these patterns and automatically move data between tiers as needed. This adaptability ensures that data remains on the most suitable storage tier, even as access requirements evolve.
• Scalability. As data storage needs grow, storage tiering allows organizations to scale their storage infrastructure efficiently. New storage tiers can be added or existing tiers expanded as required to accommodate increasing data volumes and performance demands.
• Simplified data management. Storage tiering solutions often include automated policies and management tools that simplify data management tasks. This reduces the administrative overhead associated with manual data placement and migration.
• Compliance and retention. Organizations with regulatory or compliance requirements benefit from storage tiering by ensuring that data is stored and retained in accordance with legal mandates. Compliance data can be managed on specific storage tiers with the necessary security and retention policies.
• Data protection and disaster recovery. By classifying data based on importance, storage tiering helps organizations prioritize data protection efforts. Critical data can be stored on resilient, redundant tiers, ensuring data availability and recoverability in case of failures or disasters.
• Optimized backup and restore. By segregating data based on its importance and access patterns, storage tiering can help prioritize data for backup and restore operations. Critical data can be backed up with a higher frequency, while less critical data may be subject to longer backup intervals.

While the primary purpose of storage tiers is to optimize data placement and storage costs, the benefits they offer can also enhance the organization’s ability to recover from disasters. Redundancy and cost-effective data retention make the chances of successful data recovery higher. It helps organizations maintain business continuity and recover from disasters with minimal data loss and downtime, ultimately improving their overall disaster recovery readiness.

Storage Tiering Best Practices

Storage tiering is a valuable technique for optimizing data storage, but it’s important to follow best practices to ensure its effectiveness and efficiency. The storage tiering best practices are as follows:

• Understand your data. Conduct a thorough analysis of your data to understand its characteristics, access patterns, and importance. Not all data needs to be tiered, for this reason, you should identify which data sets would benefit the most from tiered storage.
• Select the right storage media. Choose storage media for each tier based on your organization’s performance and budgetary requirements. Solid-state drives, hard disk drives, cloud storage, and tape libraries are common options.
• Regularly monitor and adjust. Continuously monitor your storage environment to track data access patterns and tier utilization. Adjust tiering policies as needed to reflect changing requirements. Regularly reviewing and fine-tuning your policies is essential for optimal performance.
• Use data classification and tagging. Use metadata and data tagging to classify data. This metadata can be used by your tiering system to make more informed decisions about data placement.
• Prioritize critical data. Ensure that mission-critical and frequently accessed data is placed on high-performance tiers. This may require different policies or priority levels for different types of data.
• Include redundancy in critical tiers. If you store mission-critical data on high-performance tiers, consider redundancy mechanisms like RAID (Redundant Array of Independent Disks) to protect against data loss due to hardware failures.
• Implement automated tiering policies. Define clear, automated policies for moving data between tiers. These policies should consider factors like access frequency, data age, and performance requirements. Automating data placement and migration helps ensure that data is always on the right tier.
• Provide security and access controls. Implement appropriate security measures and access controls for data on all tiers. Ensure that sensitive data is protected and accessible only to authorized users.
• Backup and disaster recovery. Plan for data protection and disaster recovery. Ensure that backup and recovery strategies are aligned with your storage tiering approach. Critical data should be backed up more frequently and retained securely.
• Scalability. Design your storage tiering strategy to be scalable. As your data storage needs grow, be prepared to add more tiers or expand existing ones.
• Consider hybrid cloud solutions. Depending on your organization’s needs, consider integrating cloud storage as one of your storage tiers. Hybrid cloud solutions can offer scalability and flexibility.
• Regularly evaluate the technology. Stay informed about advancements in storage technology. As technology evolves, new storage media and solutions may become more cost-effective and suitable for your storage tiers.

NAKIVO Backup & Replication and Backup Storage Tiering

NAKIVO Backup & Replication is a modern data protection and disaster recovery solution that can work with different storage tiers, allowing you to optimize your backup and recovery strategies based on their specific needs and available storage infrastructure. The NAKIVO solution supports various storage types, including on-premises storage, cloud storage, and deduplication appliances.

You can configure NAKIVO Backup & Replication to use different storage tiers for backups. For example, critical backups can be stored on high-performance storage (Tier 1) for quick recovery, while less critical backups can be moved to lower-cost storage (Tier 2 or cloud) for long-term retention.

The product offers features like backup copy and replication, which enable the creation of additional copies of backups on different storage tiers. This enhances data redundancy and disaster recovery preparedness by placing backups in multiple locations or storage tiers.

You can define retention policies within NAKIVO Backup & Replication to automatically manage backup data based on your tiering strategy. For instance, backups can be retained on Tier 1 for a shorter period and then transitioned to Tier 2 for longer-term retention.

The NAKIVO solution supports popular cloud storage providers. This means that you can easily incorporate cloud storage as a storage tier for offsite backups, reducing the need for additional on-premises infrastructure.

Conclusion

Multi-tiered storage architectures allow organizations to allocate storage resources depending on the specific needs of their data. By placing data on the most appropriate tier, organizations can optimize both performance and cost, ensuring that critical data receives the necessary performance while less critical or infrequently accessed data is stored cost-effectively. Automated data tiering policies and management tools help ensure that data is moved between tiers as access patterns and requirements change over time.

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