As data centers become ever-more complex, the need for intelligent operations and maintenance systems becomes ever-greater.
The role of the data center is now more crucial than ever and integrating Artificial Intelligence will help enterprises move towards the next phase of their digital transformation journey. Vital facilities for hosting IT infrastructure and data storage repositories of strategic importance, data centers that are well-run represent a competitive advantage as industries embrace and accelerate digitalization.
In response to this evolving need, tech giant Huawei says its Data Management Engine (DME) is an intelligent O&M platform for modern data infrastructure. According to the company, the DME enables full data storage lifecycle management and automation that covers planning, construction, optimization, and O&M. And to deliver the versatility, integration, and autonomous O&M for advanced data centers, DME incorporates various built-in AI capabilities that are tightly integrated across various aspects of its operation to support Artificial Intelligence for IT Operations (AIOps).
First proposed by Gartner, AIOps envisions the use of big data analytics and machine learning to automate IT operations processes. AIOps helps IT O&M personnel process massive volumes of data to determine the root causes of errors and proactively predict the risks from existing systems.
With its ability to enhance decision-making by contextualizing large volumes of operational data, Gartner says AIOps adoption is growing rapidly across enterprises1.
AIOps systems also automate mundane software maintenance activities and orchestrate the many layers of IT systems, enabling them to become increasingly autonomous and self-regulating. Indeed, IDC predicts that AIOps will become an important capability for O&M with an adoption rate of at least 50% by large enterprises by 2024 and become the new normal for IT operations2. Enabling AIOps
And since organizations increasingly seek to extract insights from data, data center operations and maintenance (O&M) systems can no longer stay the same. Typically seen as immutable systems that run unchanged for years, O&M must evolve to incorporate greater agility to meet fluid, demanding new requirements without compromising their innate reliability.
For a start, O&M systems are no longer passive support systems, but seen as part of core
production systems. New capabilities and a process of integration are required to bring new value to services, which may be a bridge too far with traditional systems.
Another transition happening is the shift from human-centric processes to autonomous ones. Where O&M processes used to revolve around machines assisting humans, the new paradigm sees AI-based machines reconstructing the service process and even autonomously driving IT O&M.
Manual interventions, if required at all, are specific and centered on novel challenges. In a sense, it is humans who will be assisting the machines.
With Huawei DME, service change requests are performed on a single unified management
interface Huawei DME uses AI technologies to enable various capabilities, from predicting performance and capacity, to quickly identifying or even predicting impending faults. By delivering continuous insights and optimization of IT infrastructure and services, IT O&M personnel are hence able to identify system exceptions and quickly locate root causes, proactively predicts system running risks, and generate alarms.
AIOps envisions the use of big data analytics and machine learning to automate IT operations processes Some of the AI-powered capabilities in Huawei DME include performance warning, load imbalance detection, fault warning and root cause analysis.
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