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Sercompe Business Technology provides basic cloud services to approximately 60 enterprise customers, supporting approximately 50,000 users in total. It is therefore crucial that Joinville, Brazil, the company’s core IT infrastructure, provides reliable service with predictable high performance. However, with a complex IT environment with more than 2,000 virtual machines and 1 petabyte (equivalent to one million gigabytes) of managed data, it was very difficult for network administrators to sort through all the data and alerts to understand what was happening when there were problems. cropped. It was also difficult to ensure that network and storage capacity would be where they were supposed to be or when the next upgrade would be made.
To solve complexity and increase the productivity of support engineers, Sercompe has invested in an artificial intelligence operations (AIOps) platform that uses AI to get to the root cause of problems and alert IT managers before small problems become big problems. Now, according to Rafael Cardoso, cloud product manager, the AIOps system does most of the work of managing the IT infrastructure – a huge boon over the old manual methods.
“Understanding when I needed more space or capacity – it was a mess before. We needed to get information from many different points when planning. We never got the number right,” says Cardoso. I own it.” AIOps provides visibility over the entire environment.
Before implementing the technology, Cardoso was where countless other organizations found themselves: battered in a complex network of IT systems with interdependencies between hardware, virtualization, middleware, and finally application layers. Any downtime or downtime can lead to tedious manual troubleshooting and ultimately a negative impact on the business: for example, a website that is not working and pissing off customers.
AIOps platforms help IT administrators master the task of automating IT operations using AI to provide quick insights into how infrastructure is working – buzz-buzzing areas with places in danger of triggering an outage event. Gartner is credited with coining the term AIOps in 2016: a broad category of tools designed to overcome the limitations of traditional monitoring tools. Platforms use self-learning algorithms to automate routine tasks and understand the behavior of the systems they monitor. They gain insights from performance data to identify and monitor erratic behavior on IT infrastructure and applications.
Market research firm BCC Research predicts that the global AIOps market will grow from $3 billion in 2021 to $9.4 billion by 2026, with a compound annual growth rate of 26%.1 Gartner analysts published in April “AIOps Platforms They write in the Market Guide for the Marketplace: The increasing rate of AIOps adoption is driven by digital business transformation and the need to move from reactive responses to proactive action to infrastructure challenges.
“With data volumes reaching or exceeding gigabytes per minute in a dozen or more different domains, it is no longer possible for a human to analyze data manually,” Gartner analysts write. Applying AI systematically accelerates insights and enables proactivity.
According to Mark Esposito, chief learning officer of automation technology company Nexus FrontierTech, the term “AIOps” derives from “DevOps”, a software engineering culture and practice aimed at integrating software development and operations. “The idea is to advocate automation and monitoring at all stages, from software construction to infrastructure management,” says Esposito. Recent innovations in the field include using predictive analytics to predict and resolve issues before they impact IT operations.
AIOps helps infrastructure fade into the background
Saurabh Kulkarni, head of engineering and product management at Hewlett Packard Enterprise, says this help can be used by network and IT administrators who are frustrated with increasing data volumes and increasing complexity. Kulkarni is working on HPE InfoSight, a cloud-based AIOps platform to proactively manage data center systems.
“IT managers spend tons and tons of time planning their work, planning deployments, adding new nodes, computing, storage and all the other work. And when something goes wrong with the infrastructure, it’s extremely difficult to manually debug those issues,” Kulkarni says. “AIOps uses machine learning algorithms to look for patterns, examine repeated behaviors, and learn from them to provide a quick recommendation to the user.” Beyond the storage nodes, each part of the IT infrastructure sends a separate alert so that issues can be resolved quickly.
The InfoSight system collects data from all devices in a customer’s environment and then correlates it with data from HPE customers with similar IT environments. The system can detect a potential problem so it can be fixed quickly; If the problem reoccurs, the fix can be applied automatically. Alternatively, the system sends an alert so IT teams can quickly resolve the issue, Kulkarni adds. Consider a storage controller that fails because it has no power. Rather than assuming the problem is just storage related, the AIOps platform examines the entire infrastructure stack down to the application layer to determine the root cause.
“The system monitors performance and can see anomalies. “We have algorithms that are constantly running in the background to detect anomalous behavior and alert customers before problems occur,” Kulkarni says. The philosophy behind InfoSight is to “eliminate infrastructure” by consolidating IT systems and all telemetry data into a single panel. By looking at a huge dataset, administrators can quickly tell what went wrong with the infrastructure.
Kulkarni recalls the challenge of managing a large IT environment from past jobs. “I had to manage a large dataset and stand by for hours calling many different vendors and trying to resolve issues,” he says. “Sometimes it took us days to realize what was really going on.”
By automating data collection and tapping into large amounts of data to understand root causes, AIOps allows companies to reallocate key personnel including IT administrators, storage administrators and network administrators, consolidate roles as infrastructure is simplified, and spend more time ensuring application performance. “Previously, companies had multiple roles and different departments that handled different things. So even deploying a new storage space required five different managers each to do their own job,” says Kulkarni. But with AIOps, the AI does most of the work automatically, so IT and support staff can devote their time to more strategic initiatives, increase efficiency, and deliver to their customers. can improve profit margins when it comes to a business providing technical support.For example, Sercompe’s Cardoso has reduced the average time support engineers spend on customer calls, increasing efficiency while reflecting better customer experience.
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This content is produced by Insights, the exclusive content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review.
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