IBM is acquiring Turbonomic to advance AIOps agenda

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IBM announced this week that it is acquiring Turbonomic, provider of application resource management (ARM) and network performance management (NPM) software infused with machine learning algorithms. Terms of the acquisition, which is expected to close this quarter, were not disclosed.

The two companies have a long-standing relationship under which IBM has been reselling Turbonomic’s ARM platform. Cisco also resells tools developed by the company. Turbonomic, which is privately held, claims revenues were up 41% for fiscal 2021 and counts Avon, HauteLook, and Litehouse Foods among its customers.

Applications and systems management

The decision to acquire Turbonomic comes after IBM began revamping its application and systems management portfolio last fall. This push began in earnest with the acquisition of Instana, provider of an application performance management (APM) platform for monitoring and observing applications.

IBM now plans to further integrate the ARM software Turbonomic developed with the APM software from Instana and an IBM Cloud Pak for Watson AIOps platform that employs machine learning algorithms to identify anomalies in real time.

“Turbonomic provides actionable observability,” IBM Automation GM Dinesh Nirmal told VentureBeat in an interview.

IBM is further extending its IT management portfolio via the recent acquisition of WDG Automation, provider of a robotic process automation (RPA) platform, and MyInvenio, which offers process mining tools, he noted.

As IT environments become more complex, Nirmal said it won’t be feasible to manage these environments without augmenting IT staff with capabilities enabled by AI platforms. It’s not likely AI platforms will replace the need for human IT administrators, but the job functions themselves will continue to evolve as lower-level manual tasks become automated, Nirmal added.

IT challenges

Now that companies are becoming more cognizant of the scope of IT management challenges, IT teams are increasingly embracing AI platforms. Organizations are now deploying a new generation of microservices-based applications that are more difficult to manage than the existing monolithic legacy applications, which are not likely to be retired anytime soon, Nirmal said. Those applications make use of cloud-native technologies such as containers, Kubernetes, and serverless computing frameworks that all need to be managed alongside virtual machines. At the same time, the IT environment has become more distributed than ever, thanks to the rise of both cloud and edge computing platforms.

The only way to contain the total cost of managing that extended enterprise is to rely more on automation enabled by AIOps platforms, Nirmal said.

IT teams need to come to terms with the fact that it takes time for machine learning algorithms to learn IT environments that are unique and subject to change. Implementing AI requires patience, Nirmal said, adding, “IT teams need to accept that AI comes with an upfront cost.”

But the return on investment in AIOps becomes apparent as rote tasks are eliminated and more potential issues are addressed before they impact an application, Nirmal noted. IT teams, for example, will be able to predict the impact new code is likely to have on the overall IT environment before it’s deployed.

IBM’s investments in AIOps are a natural extension of the capabilities IBM has developed to automate a wide range of business processes using AI technologies, Nirmal added. IT leaders can’t make a credible case for applying AI to automate business processes if the IT team isn’t using the same technologies to automate IT operations, he noted.

At this juncture, AI is about to become a mainstream component of IT operations. The issue now is determining to what degree. In some cases, AI capabilities will be slipstreamed into existing platforms, while in others, IT teams will decide to move to a new platform. Either way, machine learning algorithms will be present in one form or another.

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