In our last pertinent blog, we talked of the futuristic AIOps and its myriad of possibilities within the DevOps community . To recap, AIOps or Algorithmic IT Ops, refers to solutions that use artificial intelligence and machine learning to automate tasks and processes which would eventually reduce the required human intervention part to a minimum. This blog looks under the hood to explore the relevance of AIOps in the IT world today.
The need for AIOps arises out of the concept of preemptively evading last minute fires by self-learning predictive solutions based on machine learning concepts. The new IT environments are becoming increasingly complex due to popular adoption of IaaS, PaaS and SaaS infrastructure powered by the omnipresent cloud technology . However, this also means that a large portion of time, effort and resources are dedicated towards monitoring and troubleshooting. This is a dangerously reactive position for any company to hold. AIOps advocates using change-tolerant algorithms to fix repetitive problems and using the vast volume of generated operational data to gain profitable insights into the business. This releases the teams from entrapment in mundane tasks and spend more time on proactive relevant tasks.
In general, organizations are recommended to build on the following three concepts:
- Decide the architecture strategy to be employed to accelerate DevOps integration.
- Develop a data strategy that drives digital business transformation.
- Develop cognitive computing strategy based on advanced algorithms to maximize the business value of the accumulated data.
For any organization, a crucial element of migrating to DevOps and more Agile development practices involves taking advantage of application performance data running on live infrastructure. Furthermore, an increasing number of business-critical applications are scaled and automated thusly, there is also a stark escalation in the precedented amount of metric data available to the developer.
The huge volume of information generated with a Devops integration and historic data about the environment are key learning data sets for the AIOps Platform. These platforms use machine learning to understand and identify the normal behaviour of the system over time and are able to identify red flags on any errant system behavior.
A typical AIOps platform:
Proactive monitoring systems keep track of a number of relevant metrics. The criteria for monitoring success is based on the following:
- Identification and collection of key performance indicators.
- Machine learning technology and proactive anomaly detection.
- A consolidated monitoring view that includes performance and exception data.
Intelligent Analytics and Engagement system:
The Analytics component of the AIOps utilizes predictive algorithms, automated solutions and alert mechanisms. As such, the component runs automated scripts for solutions and alerts concerned personnel or system with help from machine learning and artificial intelligence.
Pattern discovery and Anomaly detection:
One way for the early detection of any problems is by identifying irregular behavior based on consistent data monitoring. Instead of being penalized for a malfunction later on, early detection of potential issues aids in timely intervention before it escalates into a crisis. At its core, the premise for this argument is that it is possible to objectively identify and separate normal system behaviour from irregular behaviour. However, as in the case of any classification problem, there is a margin for error- for example, a normal behaviour may be flagged as irregular or an irregular behaviour may pass undetected. This is the part where human interaction becomes inevitable. However, once a problem is handled by a human, the learning algorithms are quickly able to reevaluate and add the interaction to their learning set. Consequently, when a similar problem occurs again, the algorithm already knows how to behave based on the history of interactions.
Predictive solutions are an integral section of the AIOps architecture. The process involves data collection, analysis, statistical analysis, and predictive modelling. This predictive model is deployed and monitored for precision and accuracy.
The data pool maintains all the historic and real-time records which will be analysed with the help of algorithms, all tickets raised, all solutions suggested and all the actions are taken along with results
A closer look at AI in Ops
AIOps in action:
With the emergence of AIOps, it is easy to assume that ITOA (IT Operations Analytics) will go the way of the dinosaurs, however it could not be further from the truth. Rather ITOA pertains to services around analyzing operational data from various sources such as monitoring metric data, logs, security logs, etc.
AIOps works in tandem with ITOA to analyze the data generated by ITOA telemetry and understand the working of the IT environment. Subsequently, it is able to predict potential system failures, bugs, threats and other issues while also suggesting modifications to the existing environment to improve health, performance and other metrics.
Currently, the marketplace offers a number of robust ITOA and AIOps products like Elastic,, Hewlett Packard Enterprise, IBM, Splunk and Sumo Logic, among many others.
The Machine learning perspective:
AIOps boasts of a large arsenal of machine learning algorithms at its behest which popularly include- association learning, clustering, recommendation engines, classification, similarity matching (as well as anomaly detection), neural networks, bayesian networks and genetic algorithms. These algorithms are capable of solving complex business problems as described below:
- On the business operations side, AIOps analyses the IT as well as business data to identify behaviour patterns that generate the profitable business outcomes. Consequently, it is also able to identify the new business opportunities and potential revenue streams based on the earlier behaviour analysis.AIOps can also provide data-driven recommendations based upon both real-time and historical data to inform decision-making.
- Furthermore, pattern matching and anomaly detection can be supported by the AIOps platform with the help of robust fuzzy matching, neural nets and clustering algorithms. A simple use case scenario would be to group multiple messages related to one fault or programming bug. For example, a programming error that creates database performance issues could result in hundreds of separate messages, which humans would otherwise have to parse. Machine learning makes it easier to aggregate these collections of messages so that operations teams and developers can focus on the root cause of a problem.
- On a different note, with the rise of the cloud, there has been a shift away from SNMP toward unstructured messages. Natural Language Processing techniques make sense of the messages in the way a human would. This adds the ability of reading the context of a particular system/human interaction to the AIOps platform.
- Improvements in machine learning and integration with bug tracking and issue-tracking services like JIRA and ServiceNow can help to streamline the resolution process of performance-related bugs.
The colossal volume of application deployments everyday, facilitated by the DevOps and automation culture, are an indication of evolving and complicated times for the IT operations. The rapid advances in application development necessitate the need to handle increasingly complex system infrastructures while also keeping track of performance, Mean-time-to-recovery, and change volume. This may well be out of bounds for handling by human capabilities soon. AIOps, replete with unsupervised and reinforcement learning algorithms, provides a special modus operandi to address this situation. It ensures that the human-machine interaction is more meaningful and fruitful by focussing on delivering key insights and reduce workloads.
In the face of such enticements, AIOps is definitely here to stay and more! To know more about AIOps, drop us a line