Managing IT NOC operations autonomously requires Two Pillars
Visualize a world where humans and artificial intelligence coexist. In most cases, automation performs the heavy lifting, while humans supervise. We are already on our way to this future with AIOps, despite sounding like science fiction. Even though actual AI-augmented teams are still a long way away, AI/ML tools are becoming more commonplace within IT Ops, NOCs, DevOps, and SRE teams and leading us towards this not-so-distant future.
The number of internet users has doubled in the past five years. The past two years have generated more data than any other time in history. The amount of data produced by smart devices each day has reached five quintillion bytes (with 18 zeros). These massive volumes of data are having a significant impact on digital operations.
As we see more and more tools generating more data in the enterprise, there is also a consequent increase in complexity. The adoption of new technologies and methodologies such as CI/CD, microservice, and more contributes to this trend. Some of the factors contributing to this phenomenon include migration to the cloud, M&A activity that brings new tools and data into existing organizations, and migration to the cloud. It eventually becomes impossible for IT Ops teams to manually manage the data generated due to the amount of data and complexity involved.
Then, when this point is reached, outages and service disruptions become the norm, as IT teams are pulled away from their business-critical, strategic projects to deal with what is essentially a firefighting issue.
Scaling teams will only increase the problem while implementing more monitoring and observability tools will not be cost-effective over time. It is automated and, more importantly, autonomous systems that offer the most efficient and innovative solutions.
How are NOC engineers/technicians monitoring a typical NOC and IT infrastructure?
Monitoring and maintaining equipment, such as servers, network connections, and telecommunication systems, is the responsibility of NOC technicians. Monitoring is done by a centralized location to identify issues with data centers, servers, and computer networks.
The ability to operate without human intervention is what defines autonomy in IT Operations. The concept relies on automated incident management, just like in a modern, highly complex robotic car assembly line, so that humans only need to supervise.
Shortly, what are the chances of IT operations becoming autonomous? Below are three proposed pillars that provide a guide:
· Pillar One: Democratization
· Pillar Two: Proactively Automate processes
Democratization, one of the pillars
The democratization of AI is the last pillar we need to achieve full autonomy in IT operations. AIOps should be accessible to all organizations, not only to advanced, mature organizations staffed with data scientists. Any organization should be able to adopt AIOps and reap its benefits just as anyone can accomplish almost anything online nowadays with just a few clicks!
Democratization is about making relevant information accessible to anyone who needs it, when they need it, and in a way that’s easily applicable and actionable. AI must be accessible to the general public through simple user interfaces, without the need for coding; that is, people that do not (need to) understand machine learning models should be able to take advantage of machine learning innovations.
Demonstrating democratization will require that AIOps platforms become simple and accessible for all – from administrators to users. Using a platform for AIOps, for example, administrators can automatically suggest how operators should prioritize incidents, simplifying the triage process for them and providing instruction that even a non-expert can understand. AIOps will become more democratized as more such features are integrated into the platforms.
It will eventually be necessary to reach IT Ops autonomy to conquer the tyranny of overwhelming data, as data volumes will continue to grow exponentially in the coming years.
For your customers to progress towards autonomy, keep those three pillars in mind if you provide AIOps services. AIOps vendors’ platforms and tools should be designed with these pillars and their principles in mind if IT Ops autonomy is part of your vision as an IT Ops leader or tooling architect.
Automation is the second pillar
Automation of incident management is essential to its success. Eventually, it will be possible thanks to AI and machine learning.
Think of autonomous driving as an analogy.
A few years ago, cars were mostly manual, with little or no automation. Afterward, vehicles have moved to partial autonomy, paving the way for today’s conditional autonomy, when a car can drive itself in ideal circumstances. When we begin accepting automation, we do so cautiously or enthusiastically, depending on who we are. In addition, progress is being made in the direction of greater levels of autonomy, which will eventually allow vehicles to drive themselves under most conditions. Perhaps one day we will reach the holy grail of full autonomy when vehicles will be able to drive themselves–maybe without steering wheels!
Edge computing for autonomous vehicles: Each autonomous vehicle needs its server to function, so the best place would be near the vehicle.
Edge computing, for instance, can identify pedestrians more quickly and stop them more quickly. The technology makes it impossible for vehicles to directly access third parties such as cloud servers.
Similar to IT Ops, automation requires time to build technology capabilities and trust so it can become widely adopted. Human-driven automation typically begins with adding “if-then-else” types of automation manually. Upon the raising of an alert, a ticket is automatically assigned to the person who raised it. As our confidence grows, we may be able to offer suggestions based on AI, using what AI has learned from previous human interactions, and suggest what should be automated, but the expert decides whether the suggestion should be accepted.
Eventually, AI will be able to perform some tasks partially – perhaps less risky ones like collecting diagnostic data and including it in alerts, so that an incident response receives both together. Maybe we can automate certain parts of our system in a staging environment that isn’t production-critical and is, therefore, a good place to test whether the automation is working as expected. AI may eventually be able to fully automate a process and require no human intervention – detecting a problem and resolving it will be handled by AI. We may eventually be able to automate this level of life as AI and machine learning capabilities advance and our confidence grows.
Finally, where are we on the road to autonomy? It is only a matter of time. However, one thing is for sure: we will get there, and we might be seeing machine-augmented teams soon if the pandemic of COVID-19 is any indication.
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