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Maxim is the co-founder and CEO of Zesty. With over 13 years of experience in the tech industry, including a prior career in homeland security, Maxim thrives on solving complex problems and disrupting previously established norms.
When we think DevOps, we think speed, agility, innovation and technical excellence. But productivity is another driving force that has led to its massive popularity. IT organizations were tired of the silos, and sometimes even power struggles, between developers and operations. They just resulted in wasted time, lack of ownership and subpar technology.
But while DevOps has led to increased innovation as well as smoother, less interrupted workflows, one thing still stands in the way of experiencing the full potential of DevOps productivity: cloud management.
Now that DevOps and the cloud have practically become synonymous, DevOps is running into challenges that impair productivity and agility. We’ll delve into some of these below.
Cloud Monitoring and Optimization Challenges
Simply put, continuously monitoring and optimizing cloud environments is impossible to do manually. It would mean DevOps teams constantly performing menial tasks and babysitting their cloud infrastructure to predict future scale, analyze usage in real time and manage dynamic environments. Some of the problems they run into with that approach include:
Too Many Data Points to Analyze
Managing the cloud requires DevOps engineers to leverage real-time data on current usage patterns, as well as future predictions, to account for scale. These predictions need to be 100% accurate far in advance. Needless to say, 100% accurate predictions are impossible when dealing with dynamic environments, a fluctuating customer base and the implementation of new technologies.
In addition, to take action in real time, DevOps engineers constantly need to monitor their current cloud usage to accommodate changes in capacity needs. This means they need to be on watch 24/7 to receive alerts from their monitoring tools. As a result, it’s easy for alert fatigue to set in, which can make them filter out these notifications altogether. Lastly, all of this data is overwhelming and tedious to manage, requiring constant analysis on spreadsheets and endless calculations.
Too Many Decisions to Make in Real Time
Let’s say your DevOps engineers are able to collect this data easily and accurately. Now they have to make split-second decisions about what the data means and the best way to move forward. For example: Say your application scales up significantly. What are the best instances to accommodate this scale? Figuring this out takes time, effort and manual calculation. That time can surely be better spent on implementing new technologies, developing new features or improving processes.
Too Many Menial and Repetitive Tasks
What do most DevOps engineers love about their job? The ability to innovate — to be a part of building valuable technologies, to contribute to their favorite open source projects, and of course, the opportunity to solve complex problems. What do they hate? Wasting time on repetitive tasks, fixing broken systems and doing grunt work that could easily be automated. Unfortunately, managing the cloud involves way too much of the latter.
For example, say your EBS disk is about to reach its limit. An alert goes out in the middle of the night and whichever DevOps engineer is on call will have to get out of bed and manually expand your EBS disk in order to prevent application failure. It’d be the same process with EC2. If you want to optimize costs as you scale up and down, DevOps engineers will need to continuously buy and sell commitments or configure their application to run on a spot instance. There’s simply no way to win.
Powering DevOps Productivity with AI Automation
As we have established, DevOps engineers are not babysitters. They are highly qualified and talented engineers who thrive by building new and innovative technologies. The grunt work of cloud management, therefore, is often seen as an obstacle to DevOps productivity as it requires constant monitoring, configuration and adjustments. It doesn’t help that much of this work is impossible to do 100% effectively.
Thankfully, there is a better way.
AI automation is perfectly suited to handle repetitive, routine tasks such as analyzing real-time data, predicting future scale, adjusting infrastructure to accommodate changes in requirements and more. Plus, it can do all of this with perfect accuracy.
DevOps teams cannot be as productive as they want if they are constantly putting out fires in their cloud infrastructure. By automating the tasks they don’t like doing anyway, your cloud stays fully optimized while your DevOps engineers are able to work more efficiently on what they enjoy most.
InApps is a wholly owned subsidiary of Insight Partners, an investor in the following companies mentioned in this article: Real.
Photo by Aleksandar Pasaric from Pexels.
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