AIOps: How to Use AI for Storage Management

Enterprise Storage Forum content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.

Enterprises are currently facing daunting challenges related to exploding data volumes—not just in properly managing and storing this data, but in extracting meaningful information from it even as it grows more complex. This is where artificial intelligence for IT operations (AIOps) comes into play. AIOps is an emerging paradigm that uses artificial intelligence and machine learning (ML) to operationalize and transform data into actionable insights and optimize the storage of that data.

This article explores AIOps and how it can be used to effectively manage data storage.

Current Storage Management Challenges

As data volumes across the world continue to increase exponentially, so do complexity and diversification. IDG reported that data volumes are increasing on average 63 percent per month, with most enterprises integrating over 400 data sources. On the high end, 20 percent of businesses are incorporating over 1,000 data sources. Enterprise data will increase more than double the rate of consumer data over the next five years, as the world’s data is expected to increase to 175 zettabytes by 2025, with total data anticipated to more than double in size between 2022 and 2026.

This continued hyper-expansion of enterprise data—both in usage levels and diversification—has created key challenges in terms of management overhead, cost, compliance, and security, to name a few. More complexity invariably results in less initial visibility and a broader cyber attack surface, resulting in issues that make proper storage management difficult.

What is AIOps?

IT teams that rely on manual processes for managing storage resources face an uphill battle as their organization’s data consumption patterns expand and evolve. AIOps combines AI and advanced analytics with traditional IT operations to automate and enhance various aspects of data storage management, doing away with error-prone manual processes.

Chiefly, AIops merges machine learning algorithms, big data analytics, and natural language processing (NLP) to deliver actionable insights and optimize storage infrastructure.

Key AIOps Components

Though implementations may differ across organizations, AIOps workflows fundamentally consist of the following components:

  1. Data Collection: AIOps platforms collect a wide array of data from various sources, including logs, metrics, events, and performance data from storage systems. This data is then analyzed to identify patterns, anomalies, and trends.
  2. Machine Learning: AIOps leverages ML algorithms to process and analyze large volumes of data. These algorithms can detect and predict potential issues, identify patterns, and provide intelligent recommendations for storage management.
  3. Automation: A critical aspect of AIOps is automating routine tasks such as data monitoring, capacity planning, performance optimization, and incident management. This enables IT teams to focus on strategic activities and problem-solving rather than mundane and repetitive tasks.

AI and Storage: A Brief History

In the data storage management arena, AI has played a transformative role in optimizing operations, improving efficiency, and ensuring the integrity and security of stored information. The following are crucial developments in the history of AI for data storage management, highlighting key milestones and innovations that have shaped the landscape of storage technology.

Learn more about Data Storage Management.

Early Applications of AI in Data Storage Management

The origins of AI in data storage management can be traced back to the 1980s with the emergence of knowledge-based systems, also known as expert systems. These expert systems employed rule-based reasoning to capture and automate knowledge from storage experts. They were employed for tasks such as storage configuration, troubleshooting, and capacity planning, providing valuable insights to IT teams and simplifying management processes.

Emergence of ML and Data Analytics

With the advent of ML algorithms and advanced data analytics techniques in the late 1990s and early 2000s, IT professionals were introduced to ML models for analyzing storage performance data, detect anomalies, and predict potential failures. This new era of AI in data storage management brought greater accuracy and efficiency to storage management by enabling proactive actions and reducing downtime.

AIOps and Intelligent Automation

The rise of AIOps marked another new era in storage management. AIOps saw the integration of AI capabilities (e.g., ML, big data analytics, and NLP) for automating and enhancing various aspects of storage operations, and introduced intelligent automation to tasks like capacity planning, performance optimization, incident management, and security analysis.

Learn more about Storage Automation.

Predictive Analytics and Proactive Management

In recent years, AI in data storage management has focused on predictive analytics and proactive management. By leveraging historical data, ML algorithms can identify patterns and trends, enabling organizations to better anticipate storage capacity requirements across heterogeneous environments, optimize resource allocation, and mitigate potential issues before they impact operations. Predictive analytics has become a crucial tool for optimizing storage infrastructure and improving overall efficiency.

Cognitive Storage Management and Data Classification

AI’s cognitive capabilities have been harnessed to enhance storage management further. Cognitive storage management systems use natural language processing and machine learning to analyze unstructured data, categorize it, and assign metadata automatically. This approach simplifies data classification, improves searchability, and enables more efficient data retrieval and access control.

AI and Security in Data Storage

AI has also been instrumental in bolstering data storage security controls. ML algorithms can analyze access patterns, user behavior, and data anomalies to detect potential security threats. AI-powered security platforms provide real-time alerts, identify suspicious activities, and respond rapidly to mitigate risks, thus improving the organization’s overall data security posture.

Cloud-based AI in Storage Management

As cloud computing gained prominence, AI found its way into cloud-based storage management. Cloud storage providers integrated AI capabilities into their platforms, offering advanced analytics, intelligent tiering, and automated data placement to optimize storage resources and provide cost-effective solutions.

AI for Storage Management Key Benefits

The history of AI for storage management has been characterized by ongoing improvements and a continued focus on automation, optimization, and security from the early days of expert systems to the emergence of AIOps. The following are the key advantages that today’s AIOps workflows offer when it comes to managing storage resources effectively.

Proactive Problem Identification and Resolution

Traditional data storage management approaches are often reactive, relying on human intervention to identify and resolve issues. AIOps, on the other hand, enables proactive problem identification by analyzing historical data and identifying patterns that could lead to potential problems. It can predict storage capacity issues, performance bottlenecks, or failures before they occur, allowing IT teams to take preemptive actions and minimize downtime.

Optimal Resource Allocation and Capacity Planning

AIOps platforms analyze storage usage patterns, performance metrics, and workload requirements to provide recommendations for optimal resource allocation and capacity planning. By analyzing historical and real-time data, AIOps can identify under- or over-utilized storage resources, enabling organizations to optimize their storage infrastructure, reduce costs, and improve overall efficiency.

Intelligent Incident Management

AIOps platforms can analyze incident data and identify the root causes of storage issues with greater accuracy and speed. By correlating events, logs, and performance metrics, AIOps can quickly pinpoint the source of a problem and provide actionable insights to resolve it. This reduces mean time to repair (MTTR) and enhances the overall incident management process.

Enhanced Security and Compliance

Data storage is a critical component of an organization’s security and compliance posture. AIOps can help in identifying potential security threats by analyzing log data, user behavior patterns, and access logs. It can detect anomalies and trigger alerts for suspicious activities, enabling security teams to respond swiftly and mitigate risks. Additionally, AIOps can assist in compliance management by automating data classification, retention, and access control processes.

Building an AIOps Storage Management Workflow

AIOps implementations will vary across organizations, as there is no “one size fits all” AIOPs architecture. However, to implement AIOps effectively for data storage management, organizations should consider the following steps:

1. Data Integration and Preparation

Integrate data sources such as storage systems, monitoring tools, and log files into a centralized data repository. Clean and normalize the data to ensure consistency and accuracy.

2. Machine Learning Model Development

Develop machine learning models tailored to the organization’s storage environment. Train the models using historical data to enable accurate predictions and recommendations.

3. Automation and Orchestration

Leverage automation and orchestration capabilities to automate routine tasks such as capacity planning, performance optimization, and incident management. Integrate AIOps platforms with existing storage management tools and workflows for seamless operations.

4. Continuous Improvement

AIOps is an iterative process that requires continuous monitoring, evaluation, and refinement. Regularly assess the performance of the AIOps system, identify areas for improvement, and incorporate feedback from IT teams and end-users.

Bottom Line: Using AI for Storage Management

In an era of exponential data growth, managing data storage has become an increasingly complex and difficult affair. AIOps provides organizations with a transformative approach to storage management by harnessing the power of AI/ML to automate and optimize storage management processes. With AIOps, organizations can gain proactive insights, optimize resource allocation, enhance incident management, and improve security and compliance. And as AIOps continues to evolve, it will no doubt unveil new opportunities for organizations to unlock their data assets’ true value.

Get the Free Newsletter!

Subscribe to Cloud Insider for top news, trends, and analysis.

Latest Articles

15 Software Defined Storage Best Practices

Software Defined Storage (SDS) enables the use of commodity storage hardware. Learn 15 best practices for SDS implementation.

What is Fibre Channel over Ethernet (FCoE)?

Fibre Channel Over Ethernet (FCoE) is the encapsulation and transmission of Fibre Channel (FC) frames over enhanced Ethernet networks, combining the advantages of Ethernet...

9 Types of Computer Memory Defined (With Use Cases)

Computer memory is a term for all of the types of data storage technology that a computer may use. Learn more about the X types of computer memory.