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According to an IDG Cloud Computing Study, 81 percent of organizations already used applications in the cloud in 2020, and another 12 percent planned to do so within the next twelve months. If IDG's predictions are accurate, 93 percent of enterprises are taking advantage of cloud computing in 2021. 

But the percentage of workloads in the cloud is another matter altogether. More than half of the participants in IDG's study (54 percent) said their IT systems were mainly on-premises, with larger enterprises typically housing more workloads on-prem than smaller organizations. That's not surprising given the wide use of legacy applications, such as ERP, and the sprawling nature of many enterprise IT ecosystems. 

Migrating legacy, mission-critical applications to the cloud can be a daunting prospect. Some of the concerns frequently cited by our clients include:

Application dependencies. ERP applications often have touchpoints to business applications throughout the enterprise. These essential integrations can be broken during a migration, leading to application downtime and loss of productivity.

User experience. IT departments understand how to evaluate and manage application performance in an on-premises environment. But what happens when the application is migrated to the cloud? How will performance be measured, and how will systems analysts know if there's a problem? 

Availability. Minimizing unplanned downtime is vital. If cloud-based applications are unavailable for any reason, how quickly can the problem be diagnosed and service restored?

Which metric is more important: MTTI or MTTR?

One of the fundamental questions we get asked when helping clients implement AIOps is whether MTTI (Mean Time to Identification) or MTTR (Mean Time to Remediation) is the more important metric. Ultimately, it's important to remember that, although we see these as unique metrics in IT, the users we serve do not. For them, the clock starts ticking as soon as they notice an issue and doesn't stop until it's addressed to their satisfaction. 

By automating anomaly detection, AIOps can help identify potential problems before the user becomes aware of them. This can dramatically shorten the users' perception of IT's responsiveness. When AI is used to automate remediation, end users may not even be aware there ever was an issue. 

Mitigate the risks of migrating to the cloud with AIOps

AIOps is the application of Artificial Intelligence (AI) to IT Operations to help your team work smarter and faster. AIOps is an important component of a broader Automation and Orchestration (A&O) strategy designed to help IT operations, development, and infrastructure teams work together to drive digital transformation within the enterprise.

Although AIOps can be applied to many of IT Operations' day-to-day responsibilities, Application Performance Management (APM) is one where AIOps delivers the greatest returns. Specifically, AIOps with APM can help IT Operations address many of the concerns associated with migrating mission-critical legacy applications to the cloud. Note, this is true whether you're refactoring these applications or migrating them via lift and shift. 

At WWT, we create tailored A&O solutions for our clients. The AIOps solution we most frequently deploy as part of the overarching A&O strategy is Cisco's AppDynamics. To help illustrate the benefits of AIOps in a cloud migration, let's look at how AppDynamics addresses some of the most common cloud migration concerns. 

Identify dependencies. AppDynamics can assess your legacy applications to identify cloud and on-premises applications that require access to these systems. We've had clients who were confident they had identified all application dependencies, only to find several overlooked dependencies when they ran AppDynamics against their systems. 

Establish performance metrics. Application performance can be tricky to manage without an APM system. Users may perceive an application as running slower (or faster) in the cloud based on what they expect to see. AppDynamics lets you establish baseline performance before you migrate, which can then be used to assess performance in the cloud.

Manage performance. Once the cloud-based resources are live, AppDynamics looks at every application transaction to identify anomalies then reports these deviations in real time. Often, just decreasing the time it takes to become aware of an issue helps shorten time to remediation and improve overall customer satisfaction.

Shorten remediation time. Of course, it's not AIOps without AI. When performance anomalies are identified, AppDynamics's algorithms also do an automated root-cause analysis to provide AI-driven remediation recommendations.

AIOps: Not just for the cloud

As fast as organizations are adopting cloud computing, on-premises data centers will still be with us for a while. One of the reasons we chose AppDynamics as a cornerstone of our AIOps architecture is the tool's ability to manage application performance in a hybrid cloud that includes both on-prem and cloud-based resources. Even as enterprises migrate more of their legacy applications to the cloud, they can't afford to drop the ball when it comes to managing the performance of their on-prem applications. 

Looking for more information? Here are a few additional resources that may help: