Flying High With AI
How Machine Learning Powers AIOps, Correlation & Analytics
By Bishnu Nayak
Digital disrupters have accelerated the pace of business. They’ve prompted digital transformation across organizations and industries. That has led to more departments within more businesses adopting more connected applications.
That has created greater reliance on underlying enterprise networks on which these critical business applications run. The infrastructure is becoming more distributed, heterogeneous, intelligent, open, and virtualized to support the growth, agility, and scale of the business applications.
Application architecture is changing to adopt newer technologies such as containers. That allows deployment of the application entities in multiple data center and cloud environments, which can scale dynamically. (In October, DockerCon Europe reported that 24 billion containers have been downloaded.)
Business network elements frequently come from a wide variety of hardware and software suppliers. And these networks are only becoming more diverse given the movement by business networking professionals to avoid vendor lock-in, embrace open architectures, and use best-of-breed solutions.
The changes in the dynamic application environment happen very abruptly. So it’s impossible to track the changes and correlate the events using legacy techniques. Manual processing of massive amounts of data – which is dynamic across the stacks – to identify patterns, anomaly scenarios, and predict capacity requirements is almost impossible. That, in turn, poses tremendous business risks and hinders business innovation
So, what’s the solution?
A solution that combines the power of machine learning with the ability to auto-discover and correlate entities across critical layers of digital business – business, application, and infrastructure.
Artificial intelligence and machine learning are not a replacement for people in this scenario. Rather, they help humans perform day to day IT operational tasks such as troubleshooting, capacity management, migration, and planning. There are companies that use AI in event driven architecture, if you are interested in finding out more you might be interested in visiting somewhere like https://vantiq.com/resources/why-businesses-are-moving-to-an-event-driven-architecture/.
“Most recent advances in AI have been achieved by applying machine learning to very large data sets,”
notes McKinsey & Co. “Machine-learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences, rather than by receiving explicit programming instruction. The algorithms also adapt in response to new data and experiences to improve efficacy over time.”
Machine learning can correlate and analyze data, (using something similar to data lake) from multiple enterprise application and infrastructure domains, dealing with the volume, velocity, and varieties of data generated. It can uncover patterns to show what has occurred. It can use current conditions and past learning to spot exceptions and predict the future. Machine learning can even offer suggestions on what to do in various scenarios.
AIOps platforms leverage machine learning to deliver AI capabilities for IT operations. Here are some interesting use cases.
- Multivariate anomaly detection can identify anomaly scenarios across various dependent entities. Such anomalies may signal that a planned or unplanned business event has taken place. For example, a multivariate anomaly group may represent an unplanned event like a DDoS cyberattack or a planned business effort such as Black Friday event.
- A time-series sequential pattern detection algorithm can predict business outages triggered by events anywhere in the stack business functions are deployed.
- It’s also possible to use AI and machine learning to predict when you’ll run out of capacity. For example, it could signal a potential lack of storage disk volume and excessive network bandwidth use of a router. Such information helps IT experts do proactive capacity planning to better meet business needs.
Machine learning automates IT operations and can notify operations teams of potential business outages before they happen. It also can detect security issues, identify infrastructure performance bottlenecks, and recommend capacity augmentation and optimization.
IT teams can then set systems to trigger actions for remediation. Executing remediation scripts or integrating with other orchestration and automation tools to take actions minimizes human tasks.
Proactively detecting issues and fixing such issues enables business continuity and assures customer satisfaction. In the age of digital transformation, such capabilities and AIOps solutions are an absolute must.
With machine learning, IT staff can continually and completely look for traffic exceptions. So IT experts can be far more effective in preventing and quickly responding to cyberattacks. So businesses can stay up and running, and stay out of the headlines.
These are just a few reasons why AI and machine learning have become key components of digital transformation. And that’s only going to accelerate moving forward.
“During the next few years, the technologies associated with this [digital transformation] wave — including artificial intelligence, cloud computing, online interface design, the Internet of Things, Industry 4.0, cyberwarfare, robotics, and data analytics — will advance and amplify one another’s impact,”
note PwC analysts Leslie H. Moeller, Nicholas Hodson, and Martina Sangin.
Forrester Research says more than half of organizations already have implemented some form of an AI project. And it says another 20 percent are planning AI projects in the near future.
Your business and its IT staff should be thinking about how you can benefit from AI and machine learning too.
If you’re still on the fence, think of it like this. Machine learning is to network operations as air traffic control is to airline operations.
There are about 5,000 airplanes in the sky every hour in the U.S.
So you can’t use manual processes to track planes as they move around. It would be near impossible, and just plain dangerous.
So we use air traffic control to manage the chaos. The air traffic control system helps experts keep track of all the traffic (airplanes) among the different domains (various airports and airlines).
By bringing together the various data points and presenting a complete view of what’s happening, air traffic control helps avoid crashes and enables smoother traffic flow.
Machine learning likewise enables data correlation and analytics. That way, IT experts can keep the network and its applications running safely and on time. And that allows organizations to deliver better and safer customer experiences, make better use of their human and technological resources, and keep their applications and businesses moving forward.
That’s why artificial intelligence and machine learning are key technology enablers of the FixStream AIOps solution. They’re the AI in AIOps.
On behalf of FixStream and the entire crew, I’d like to thank you for joining us on this trip. We look forward to seeing you on board again in the near future. Have a nice day. (Sorry, I couldn’t resist!)
In my next blog, I’ll talk about automation.
Bishnu Nayak is the CTO for FixStream.