It’s no secret that Artificial Intelligence (AI) is impacting virtually every industry and showcasing unprecedented use cases.
While it has been controversial since the beginning, its ability to increase productivity and efficiency is undeniable. Global GDP is expected to grow by $15.7 billion through AI.
AIOps is the latest trend in IT Operations (Ops) trend that is helping enterprises reap the many benefits of AI. It’s a new concept for many, especially for businesses like data centers that have long relied on physical infrastructure. However, its implementation may eventually become a necessity.
In this article, we will discuss the following:
- AIOps and its capabilities.
- How it factors into the context of data centers.
- What it means for hardware infrastructure.
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What Is AIOps?
AIOps, or Artificial Intelligence for IT Operations, refers to using AI and its capabilities to automate and improve operations. This may include natural language processing (NLP), machine learning (ML), and big data to streamline day-to-day IT operations in organizations.
The term AIOps was introduced by Gartner. The main application of AI in operations is to aggregate large amounts of data produced by different infrastructure segments, components, tools, and applications and make sense of it all.
With the learning abilities of AI, IT or DevOps teams can diagnose the root causes of problems in infrastructure and resolve them proactively. Most importantly, AI automates many of the tasks that operations managers or administrators used to do manually, streamlining workflows.
AIOps brings separate operations into a unified and intelligent platform, giving a bird's eye view of the entire operations, whether distributed geographically or logically.
AI in operations helps deal with the complexities at different layers and/or stages of IT operations.
Large enterprises with large infrastructures and advanced architectures are readily adopting AIOps to improve efficiency and eliminate complexity in operations. As a result, the AIOps market has crossed $3 billion in 2022, according to Global Market Insights.
How Does AIOps Work?
Much like DevOps, AIOps is essentially a theory you can implement with the help of the right tools. There’s no one size fits all approach to embracing AIOps.
That said, the underlying working mechanism of AIOps is pretty much the same regardless of its implementation.
It involves using an independent AI-powered tool that works with different operational components. Here’s how that would work:
- The tool takes the vast amount of redundant IT data generated by the infrastructure components (for example, network equipment monitoring or traffic control system). It selects the data elements that are important (such as data that indicates problems in operations).
- It employs correlation to find links between random data sets, identifying meaningful data elements and presenting them digestibly for further analysis.
- Again, using the extracted data, it detects the problem's root cause and identifies possible fixes.
- The AIOps tool interacts and collaborates with other tools and teams that may not even be located in the same location.
- It also preserves data to learn from it and proactively report incidents should such problems return. For known and documented issues, it automates the response sequence to take care of the issues without requiring human interference.
5 Advantages of AIOps
The advanced capabilities of AI make it incredibly beneficial for today’s increasingly complex IT ecosystems. Here are the key advantages of adopting AIOps:
- Noise Reduction: Noise in IT operations indicates useless data and irrelevant alerts that distract engineers. AIOps cuts that noise, making it easier for engineers or DevOps team members to recognize more significant issues just in time while also improving the overall efficiency of the workloads.
- Increased Observability: With the increasing use of microservices and containers, AI has become essential for dealing with visibility issues. Thanks to integrations with different parts of the infrastructure, including third-party tools and applications, AIOps tools provide transparency for the entire ecosystem.
- Proactive Anomaly Detection: By aggregating and analyzing data from the entire IT ecosystem, AIOps is better at detecting even the slightest deviation from normal operations. Its ability to correlate data and trigger the appropriate alerts and responses ensures near-perfect network uptime and availability.
- Automated Remediation: AIOps relies on knowledge and uses it to automate certain workflows and issue resolutions, saving managers and team members considerable time that they would otherwise spend figuring and solving trivial issues. It can also help enterprises move towards a self-resolving, ticketless IT support system.
- Cost Reductions: Automatic detection and resolution of operational issues result in little to no downtime, saving enterprises money they can invest in other areas, such as CapEx.
Where Does AIOps Fit With Existing Data Center Technologies?
Data centers today face increased performance requirements from data-driven technologies like IoT. Data centers are witnessing more complex digital environments to keep up with the increasing production of data and additional computing needs.
AIOps can benefit data center operations just how it can help improve any other IT enterprise. It can be challenging to see precisely how AIOps fits into the data center ecosystem.
The beauty of AIOps is that it doesn’t replace existing technologies used for IT operations like log management, monitoring, or support desk. It essentially fits at the crossroads of all these existing technologies, integrating with them and improving the overall operations system.
The OpsRamp State of AIOps Report in 2019 found that 87 percent of AIOps implementations were successful. For data centers, AIOps present an opportunity to get more out of their infrastructure and use the data generated to make beneficial business decisions.
AIOps can also address the complexity issues with data center virtualization. Although virtualization improves data center performance, it also creates management challenges. Even though the hardware is being utilized more efficiently, there’s much more data. That’s because the proliferation of VMs produces more data points than bare hardware.
Furthermore, an AIOps tool can also be instrumental in threat detection, as it can work parallel with the security tools and on top of them. By detecting problems and abnormalities, operations managers can prevent downtime due to security incidents while ensuring stricter compliance with regulations and industry standards.
How to Get Started With AIOps?
Investing in AIOps can be daunting at first, but if done right, it has the potential to transform your entire operation. That said, it’s essential to research and analyze which KPIs or performance issues you want to tackle by bringing AI into the mix.
There are domain-specific options, and then there are domain-agnostic options. For data centers, the former may be more suitable. Keep your goals and budget in mind when evaluating available solutions.
Don’t Forget Hardware Infrastructure
Virtually every aspect of the IT ecosystem is leaning more toward software — for instance, software-defined wide area network (SD-WAN) or software-defined storage (SDS). AIOps is just another brick in that wall. However, it’s essential to understand that the base of this wall is hardware.
The reliability of hardware limits software. If your hardware can’t handle the increasing workloads of tools like AIOps, you might as well not invest in more software-defined technology.
In other words, it’s vital to keep your hardware up to date and maintain it, which is where PivIT comes in with its hardware procurement and maintenance services tailored for data centers. Learn more about how PivIT and OneCall can help you improve your infrastructure!
