ITOps (IT Operations) was never easy. Many environments grew organically, with new equipment added over the years. Most enterprises have infrastructure from multiple vendors, each of whom requires companies to update to the latest releases and patches.  These factors have made ITOps ever more complex. And the trend to a hybrid cloud strategy that many companies are adopting now makes ITOps’ job even more challenging. The issue is not the cloud itself, but the rapid rate of adoption and difficulty in getting operational cloud know-how.

Spending on cloud computing infrastructure continues to grow at a furious pace. Global cloud infrastructure services market grew 42 percent year-on-year in the first quarter of 2019 with Amazon Web Services (AWS) making the biggest gain in dollar terms with sales up by $2.3 billion (41%) on Q1 2018, according to data from tech analyst firm Canalys.  That performance put AWS further ahead of second-placed Microsoft, even though it grew sales by $1.5 billion or 75 percent. Google was the fastest growing of the top three in percentage terms, up 83 percent from $1.2 billion to $2.3 billion.

A market growing at 42 percent year on year (although slightly slower than the 46% growth in Q4 of last year) is pretty remarkable. But according to Canalys, the battle for enterprise customers will intensify this year as the big cloud vendors seek to maintain that growth.

Many businesses have already finished moving easy-to-shift applications to the cloud, and are maturing their approach to cloud computing. This involves moving to multi-cloud and hybrid-IT strategies that leverage the strengths of different cloud service providers and deployment models to meet a variety of application, compliance, cost and performance requirements.

In an effort to gain a competitive edge and expand their market shares, some cloud service providers are now looking at ways to enter customers’ existing data centers. For example, AWS will start shipping its first appliance, Outposts, later this year, which will see AWS hardware on customer premises, largely to deal with the issue of latency.

Other vendors are looking at how to integrate across multiple clouds, like Google Anthos, an application management platform that supports multiple clouds. Adding new partners or making cloud part of a broader business transformation strategy are other ways that cloud vendors will try to boost sales this year. Most companies will end up using a combination of in-house data centers, plus cloud-computing technologies across a number of vendors. Few will choose just one vendor for every service.

All these factors will put the ITOps team under even greater pressure when an application performance problem surfaces.  ITOps teams also suffer from alert information overload. Too many false positives and too little information can make root cause investigations a never-ending search.  The answer lies in artificial intelligence (AI). Many ITOps tasks are routine and alert-based, so why not train a bot or an algorithm to build a machine learning model that can reduce root cause MTTR by 95% and enable the team to be proactive?

This is the value proposition of AIOps or Artificial Intelligence for IT Operations. It speeds MTTR, informs ITOps on possible issues before they turn into problems, and changes operational modes from reactive to proactive.

There are significant differences between AIOps products. One way Pepperdata differs is in the number of metrics and amount of data being collected for analysis. You need a consistent flow of data to turn an AI algorithm into a viable model. The more meaningful and complete the metrics are, the more accurate the model will be. Pepperdata captures more than 350 application and infrastructure performance metrics every 5 seconds.  This substantial data set enables the Pepperdata AI model to be much more robust and accurate than alternative approaches. Constantly capturing and analyzing this massive amount of data and metrics is one of our biggest advantages, resulting in major benefits to our customers.

The Pepperdata AI value proposition is simple. We use an AI model to automatically diagnose existing problems faster, identify potential problems, and offer actionable insights for ITOps team.  The Pepperdata AIOps approach also enables ITOps to scale infrastructure to match actual use and eliminate wasted resources that result from over-provisioning. This is extremely important in cloud environments that charge for every bit of CPU, memory, and storage that is being used.  The Pepperdata Capacity Optimizer can automatically help ITOps reduce cloud (and on-prem) infrastructure requirements and associated costs by 30 to 50 percent, eliminating the need for costly and time-consuming manual tuning!

Learn more about Capacity Optimizer and how it improves capacity utilization and saves ITOps time and expense.