Storage Arrays which are feature-rich RAID arrays that provide highly optimized I/O processing capabilities are generally referred as
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Intelligent Storage Arrays or
Intelligent Storage Systems. These intelligent storage systems have the capability to meet the requirements of today's I/O intensive next generation applications. These applications require high levels of performance, availability, security, and scalability. Therefore, to meet the requirements of the applications many vendors of intelligent storage systems now support SSDs, encryption, compression, deduplication, and scale-out architecture.
The use of SSDs and scale-out architecture enable to service massive number of IOPS. These storage systems also support connectivity to heterogeneous compute systems.
Further, the intelligent storage systems support APIs to enable integration with Software-Defined Data Cneter (SDDC) and cloud environments.
Intelligent Storage Systems Overview
These storage systems have an operating system that intelligently and optimally handles the management, provisioning, and utilization of storage resources. The storage systems are configured with a large amount of memory called cache and multiple I/O paths and use sophisticated algorithms to meet the requirements of performance-sensitive applications.
An intelligent storage system has two key components, controller and storage.
A controller is a compute system that runs a purpose-built operating system that is responsible for performing several key functions for the storage system. Examples of such functions are serving I/Os from the application servers, storage management, RAID protection, local and remote replication, provisioning storage, automated tiering, data compression, data encryption, and intelligent cache management.
An intelligent storage system typically has more than one controller for redundancy. Each controller consists of one or more processors and a certain amount of cache memory to process a large number of I/O requests. These controllers are connected to the servers either directly or via a storage network. The controllers receive I/O requests from the servers that are read or written from/to the storage by the controller.
Based on the type of data access, a storage system can be classified as block-based storage system, file-based storage system, object-based storage system, and unified storage system. A unified storage system provides block-based, file-based, and object-based data access in a single system. These are described in the next posts.
Architecture of Intelligent Storage Systems
An intelligent storage system may be built either based on scale-up or scale-out architecture.
A scale-up storage architecture provides the capability to scale the capacity and performance of a single storage system based on requirements. Scaling up a storage system involves upgrading or adding controllers and storage. These systems have a fixed capacity ceiling, which limits their scalability and the performance also starts degrading when reaching the capacity limit.
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A scale-out storage architecture provides the capability to maximise its capacity by simply adding nodes to the cluster. Nodes can be added quickly to the cluster, when more performance and capacity is needed, without causing any downtime. This provides the flexibility to use many nodes of moderate performance and availability characteristics to produce a total system that has better aggregate performance and availability. Scale-out architecture pools the resources in the cluster and distributes the workload across all the nodes. This results in linear performance improvements as more nodes are added to the cluster.
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Features of an Intelligent Storage Systems
Storage Tiering
Storage tiering is a technique of establishing a hierarchy of different storage types (tiers). This enables storing the right data to the right tier, based on service level requirements, at a minimal cost. Each tier has different levels of protection, performance, and cost.
This technique allows us to place data on the most appropriate tier of storage. It helps to place frequently accessed data on fast media and inactive data on slow media. This can improve the performance of the storage array and bring costs down by not having to fill array with fast disks when most of the data is relatively infrequently accessed.
This movement of data happens based on defined tiering policies. The tiering policy might be based on parameters, such as frequency of access.
For example, high performance solid-state drives (SSDs) or FC drives can be configured as tier 1 storage to keep frequently accessed data and low cost SATA drives as tier 2 storage to keep the less frequently accessed data. Keeping frequently used data in SSD or FC improves application performance. Moving less-frequently accessed data to SATA can free up storage capacity in high performance drives and reduce the cost of storage.
The process of moving the data from one type of tier to another is typically automated. In automated storage tiering, the application workload is proactively monitored; the active data is automatically moved to a higher performance tier and the inactive data is moved to higher capacity, lower performance tier. The data movement between the tiers is performed non-disruptively.
Redundancy
Redundancy feature in a intelligent storage system ensure that failed components do not interrupt the operation of the array. Even at the host level, multiple paths are usually configured between the host and storage in multipath I/O configurations, ensuring that the loss of a path or network link between the host and storage array does not take the system down.
Replication
Storage system based replication makes remote copies of production volumes that can play a vital role in disaster Recovery (DR) and business continuity (BC) planning. Depending on the application and business requirements, remote replicas can either be zero-loss synchronous replicas. Asynchronous replication technologies can have thousands of miles between the source and target volumes but synchronous replication requires the source and target to be not more than 100 miles.
Thin Provisioning
Thin provisioning technologies can be used to more effectively utilise the capacity in the storage systems. Over provisioning of storage space would eventually result in running out of available space.
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Intelligent storage is a data storage system that includes a layer of software for analysis and processing to allow storage to play a more active role in optimizing application, network, and workload performance. With the inclusion of artificial intelligence (AI), intelligent storage can predict how data stored on a device will be accessed, proactively apply software updates and patches, and efficiently manage data across different storage tiers.
In the past, data storage was often a monolithic, device-centric service offering only one kind of access. Today's storage solutions must support more inputs, more types of data, dispersed instances, and of course, more users.
Intelligent storage systems remove the barriers created by traditional storage, offering operational agility, efficiency gains, and the ability to adapt to usage patterns on the fly. For example, intelligent storage can shift infrequently accessed data to cheaper data storage and even identify security threats by tracking anomalous data.
Intelligent storage can help digital enterprises extract more value from data, which is becoming more critical to operations and competitive posture and more complex to collect, store, and access.
Intelligent storage is storage hardware enhanced with compute resources for software and processing. Intelligent storage can also be deployed as a virtual machine or a cloud-based service. Hardware vendors are adding this intelligent storage capability directly to flash modules and building it into data centre storage arrays. Other approaches use emerging technologies in intelligent storage, such as software-defined storage.
AI creates the 'intelligence' in intelligent storage. AI can be built into storage systems in several ways: at device levels, tiered storage and data lifecycle management, and data accessibility support. For each category, AI can analyse the access patterns and performance associated with data and automatically perform actions to keep speed and access at their peak.
However intelligent storage is built, the goal is to offer storage that optimises collection, organisation, caching, delivery, and even costs and power consumption. This might entail unifying data types, extraction from unstructured data, replication and distribution, data deduplication, strategic caching, backups, and security.
Intelligent storage can work via user-defined algorithms, or, as is increasingly the case, the use of artificial intelligence and machine learning to learn behaviors, assess patterns, and optimise storage to meet business goals. Intelligent storage can also flag potential problems, even those outside storage, before they impact other business processes.
Technologies enabling intelligent storage include the following:
In recent years, new approaches for intelligent storage have emerged that can be loosely defined as software-defined storage (SDS). In SDS, everything about storage, from provisioning to optimisation to security, is determined by purpose-built software with artificial intelligence and machine learning at its core.
AI is often at the heart of emerging trends in data storage, such as increased protection against cybersecurity threats through monitoring of anomalous activity or alerts about storage drive failure.
Because intelligent storage automatically manages tasks like performance and resource maximization, you gain time back for other tasks. Intelligent storage also offers:
The explosion of data shows no signs of slowing and the need for more sustainable data centre operations is growing more pressing. Intelligent storage can help organisations meet the computational demands of a data-driven future as well as align with environmental goals.
Pure1 Meta® is the AI-driven, full-stack analytics platform behind the intelligent storage management system from Pure Storage. Pure1 Meta leverages machine learning and the accumulated, real-time, call-home data of a global fleet of cloud-connected arrays to provide predictive analytics at scale. With Pure1 Meta, it's possible to forecast and troubleshoot data storage issues before they occur.
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