Self-healing IT infrastructures have taken shape due to machine learning and data analytics innovations, as well as wider AI adoptions. IT teams use this approach to detect operational anomalies, predict hardware interruptions and resolve performance issues without hands-on intervention.
Let's explore the key components of self-healing IT systems and identify the steps for successful deployment as the technology advances closer to maturity.
Elements of self-healing data centers
The importance of autonomous IT processes has become apparent as data centers grow more complex, operational costs rise, and organizations face IT staffing and maintenance challenges.
For example, the increase in cloud service adoptions demonstrates the value that organizations place on accessible, dynamic and externally managed resources. For self-healing systems, IT leaders use automation to ensure operational consistency and balance performance levels across the compute infrastructure. Through automation and the integration of machine learning and AI into common business flows, self-healing IT strives to resolve data center limitations before they become chronic problems.
Administrators use virtualization to dynamically distribute resources to meet compute and application demands, treating the data center as a single, unified machine. Self-healing IT reallocates resources efficiently via pooled CPU, memory, storage and network bandwidth. Additionally, it automates daily operational tasks, such as VM provisioning and real-time workload distribution.
Another key aspect of self-healing IT systems is the use of network data and algorithms for persistent monitoring. For example, processing and transferring business information as batch data commonly results in errors. However, self-healing IT infrastructures can monitor and repair these types of failures; they assess software and hardware performance and add resources if an application or server shows signs of possible failure.
For physical facilities, many IT teams already deploy a combination of AI, real-time analytics and IoT sensors to monitor and self-correct data center HVAC systems, for example. The goal is to apply the same level of self-monitoring to ensure consistent high availability for compute and networking processes. This is particularly important as data centers move closer to the edge with IoT deployments and more expansive mobile wireless networks. Edge processing poses new challenges as network traffic expands. Fortunately, self-organizing networks are already an integral part of cellular communications and offer automated optimization and healing.
Along with virtualization and monitoring, self-healing IT infrastructures adapt to variations according to guidelines administrators set. AI-fueled operations (AIOps) use rule-based, automated responses to infrastructure changes to enable tools to self-correct as needed. For example, should a CPU fail, the compute load shifts automatically to another CPU. Or, with health monitoring and telemetry data, AIOps can help prevent configuration drift, as well as diagnose and repair component or equipment anomalies that signal possible failures.
Key steps to adopt self-healing architecture
Administrators need a comprehensive evaluation of data center functionality to initiate the creation of a self-healing IT system.
First, apply defined metrics and automate log checks and alerts to track key operations on a continual basis, including CPU usage, hard disk capacity and storage limitations. Through performance insights and benchmarks, administrators can establish an operational baseline to diagnose problems and determine the potential for compute or network failure.
Step two is to adopt data analytics to accrue information to make accurate predictions, gain insights into system-wide weaknesses and identify problem areas. Analytics synthesizes a vast number of system events to inform predictions and present remedial choices. Analytics also uses clustering and correlation to streamline the data gathering process, which generates metrics for AI and machine learning algorithms. IT teams then apply these algorithms to train models for problem detection and commence the self-healing process.
The third step is to create a proactive AIOps approach that combines big data with machine learning to automate data center processes. AIOps monitors hardware performance, extends usability and compensates for failures that lead to service outages. It also reduces IT burden and frees up resources for other tasks.
Administrators define and establish rules that ensure high data center performance and business continuity. Then, to achieve self-healing, AIOps codifies, orchestrates and automates those operational rules. The process extends to every aspect of the data center, from coordinating alert logs to classifying infrastructure types to determine the remediation approach.
Self-healing IT challenges and benefits
Self-healing IT systems also have staffing implications. IT leaders face a variety of hiring challenges, including a lack of high-level expertise. Additionally, 16% of respondents to a survey from Vertiv said they expect to retire by 2025, which could cut organizations' IT workforces.
As the number of responsibilities IT teams shoulder continues to increase, a self-healing IT environment compensates for over-extended staff and strained resources. It enables data centers to handle the next wave of technology expansion and innovation, including 5G networks, edge computing and microservice architectures.