The evolution of Application Monitoring: Embracing AIOps and machine learning

by Shagufta Syed

Application monitoring is as old as the application design and development field. It allows companies to benefit from their tech stack while keeping critical anomalies at bay. But as more organizations increasingly transition to cloud application development services and technologies, traditional monitoring has failed to catch up with these aggressive digital transformations. Cloud technologies have introduced novice challenges that require new tools and technologies for 360-degree monitoring.

Enters AIOps. This cutting-edge solution offers eye-on-everything capability as your technology stack and digital application development operations grow. And the best part? You’ll no longer contend with the increasing monitoring costs and plummeting productivity.

Introduction to Application Monitoring 

Application monitoring ensures your digital assets and services are at optimum performance status. They’re necessary for pinpointing and solving issues before they cause havoc. 

The specific monitoring activity depends on the particular area that needs attention, such as:

  • IT operations
  • IT service management
  • Software development 
  • Observability
  • Overall operation intelligence
  • Cybersecurity

So, monitoring comes in diverse types, such as availability monitoring, business activity monitoring, API monitoring, application performance monitoring (APM), and accurate user monitoring. You need an arsenal of tools to facilitate this process, including observational, analysis, and engagement tools. 


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But Legacy Application Monitoring is Lagging

application monitoring

Businesses are increasingly migrating to cloud-native technologies and distributed systems to enjoy flexibility, agility, and quick time-to-market.

For proper traditional application monitoring, organizations had to meet the following conditions:

  • Relatively static application: Companies had to implement proper instrumentation to minimize sudden change or decommissioning.
  • Monitor critical applications: The organization also had to easily distinguish critical vs. non-critical apps to ensure they focus their resources only on a limited subset of their stack. 
  • Monolithic tech stack: In the past, the simple infrastructure allowed traditional monitoring to easily pinpoint and map events in the components of critical applications.

Businesses are increasingly migrating to cloud-native technologies and distributed systems to enjoy flexibility, agility, and quick time-to-market. Such technologies include containers, Kubernetes, and micro-services.

But this shift renders the above three conditions untrue, sparking new challenges. Thanks to today’s increasingly changing and intertwined technology stacks, every application is critical due to multiple points of failure. 

With legacy monitoring, it’s now harder to identify interdependencies between an application’s components. So, keeping up with these changes is harder, and companies lose time and resources trying to pinpoint and solve IT-related issues.

Sure, early cloud-native workloads are easy to monitor due to simple data. But as the workload increases and the organization embraces more DevOps principles to accelerate operations, the traditional monitoring falls flat regarding the required scalability, reliability, and data insights.

Traditional Application Monitoring Challenges

So, what are the main failures of legacy monitoring tools in the current world of cloud-native technologies? 

  • Poor control: Legacy monitoring doesn’t have robust visibility into observability data across systems, people, and teams. Unexpected overage can result from these data gaps and minor code changes. 
  • Sluggish action: Cloud-native technologies are characterized by lightning-fast server-less functions. Unfortunately, legacy monitoring systems cannot keep up with this speed, leading to more data gaps. 
  • Unable to navigate micro-services: They cannot navigate and pinpoint micro-services interdependencies. Therefore, identifying and resolving issues may take time. 
  • Missing intelligence: Modern technologies’ data intake rate surpasses legacy tools. And even if the tools can accommodate certain pieces of data, they often lack the necessary built-in intel to deliver robust, actionable insights. 
  • Vendor lock-in: It’s hard to switch legacy tools. So when their quality nosedives or prices skyrocket as your data points increase, you become the vendor’s prisoner. 
  • Tool sprawl: The many digital areas to monitor imply scattered tools. Without proper integration, complete monitoring becomes an uphill task. 

These challenges negatively impact the overall business health. Here are just some of the aftermaths: 

  • High costs: The more data ingestion, hosts, and users, the higher the prices of legacy tools. Additionally, it’s hard to monitor and control skyrocketing data. 
  • Engineers fly blind: The increasing costs force you to limit custom metrics. That means your team lacks visibility, leading to poor monitoring. 
  • Productivity nosedives: Burnouts haunt engineers who sacrifice their nights and weekends to troubleshoot an issue. The result is a wider skill gap due to higher employee turnover. 
  • Data loss and downtime: Sluggish monitoring often translates to unmet SLA. Minor tweaks can also cause data loss. 

That’s where artificial intelligence (AI) comes to your rescue. 

AIOps Transforming Application Monitoring

ecommerce application development

An IT team can identify patterns to facilitate a more componentized application development approach, allowing your organization to better leverage cloud-native technologies. 

AIOps uses AI to simplify IT monitoring and issues resolution through automation. While IT teams might fear that technology has come to replace them, nothing could be further from the truth. 

Instead, it boosts efficiency in a complex IT ecosystem. It also powers scalability without forcing organizations to incur huge costs on additional resources and time. 

As your modern IT stack becomes more complex, interdependencies and interactions between components leave behind numerous trails of digital patterns and data points. These data bits are hard for humans to process but a piece of cake for the machine learning algorithms. 

So, AIOps allows the IT team to consolidate and analyze data within a short time. That way, they can identify patterns to facilitate a more componentized application development approach, allowing your organization to better leverage cloud-native technologies. 

The componentized approach often increases transiency, leading to difficulties in monitoring modern technologies. But with AIOps, you can pinpoint the context of the connection between operational patterns and business outcomes. It gives your team technical indicators to monitor the whole stack without putting much pressure on your operational capacity. 

So, AIOps help gather data and achieve componentized development. It employs AI to distinguish repetitive patterns and reveal the relationship between these patterns and business outcomes. This data-centric approach helps fill the data gap that legacy systems usually leave. 

Key Components of AIOps in Application Monitoring

AIOps is a collection of technologies powered by AI to speed up monitoring. Here are the key components and examples of how they power application monitoring.

Data aggregation

AIOps can aggregate data from various sources in the DevOps system, including applications, tickets, and event logs. It eliminates data silos, allowing your team to get an eagle’s view of the tech stack and correlate events. So, getting to the root of issues is now easier than before. 

For example, the data aggregator can aggregate micro-services logs spread across various instances and servers. These logs usually contain crucial information such as critical events, errors, and activities. 

Real-time data processing

AI allows monitoring tools to take in and analyze mountains of data in real time. Therefore, you can pinpoint and resolve anomalies and security issues quickly. This capability enhances harmony between performance optimization and security countermeasures efforts. 

A great example is where the data processor processes aggregated logs or metrics (such as CPU usage, network traffic, and memory consumption) for quick resolution.

Rule and pattern

Rule applications and pattern recognition enable your application monitoring tools to distinguish between normal and abnormal events, enhancing quick detection and response to network anomalies. 

And thanks to machine learning, the technology can create its own rules based on training data sets. For example, a DevOps guru can provide predictive insights to train the monitoring tool to detect spikes in CPU usage. 

Domain algorithms

These components are unique to a given IT ecosystem- Your IT enterprise’s specific aims and data shape them. So, they define the goals that AIOps should prioritize. 

Domain algorithms help establish baseline performance metrics for an application, allowing the system to trigger an alert when a deviation crops up. For instance, if an e-commerce response time suddenly spikes, you’ll get a notification immediately to investigate the issue. 

Insights generation

ML and AI technologies are the driving forces behind AIOps. They help analyze, correlate, and parse mountains of data to generate visualization, histograms, and charts. 

Did your e-commerce suffer from transaction failures? AI and ML can give visualized insights behind this issue, such as high user load. 


Remember the productivity issue that legacy tools cause? AIOps’ automation helps slash monitoring workload, keeping burnout at bay. The in-depth and faster analysis of log help detect anomalies that take time or escape human ability. 

Benefits and Future Directions

AIOps’ AI, ML, plus automation are revolutionizing application monitoring operations. Add engineers’ expertise on top of them, and you benefit from the following:

  • Workflow creation and discoverability: AIOps can analyze interconnected processes, activities, components, and systems. That way, it helps you visualize and document workflows efficiently. 
  • Accurate visibility across workflows and IT systems: AIOps-backed application monitoring helps gather and analyze metrics, logs, traces, and other data across multiple sources. Your IT team has an easy time troubleshooting anomalies. 
  • Streamlined communication across IT assets: Integration with collaboration and communication tools means your team gets alerts within a second no more communication silos.
  • Quick application delivery and deployment: Automation enables continuous integration and deployment (CI/CD), accelerating the release cycle.  
  • Pre-emptive and reactive resolution: Thanks to ML models, AIOps can predict anomalies before they happen, enhancing proactive measures. If an incident occurs, the technology quickly analyzes the root cause for faster action. 
  • Lower monitoring costs: Automating routine tasks removes the time-consuming and costly manual intervention. It frees up time and resources to focus on more strategic issues. 
  • Increased innovations: Advanced insights and patterns allow your team to identify optimization opportunities, promoting continuous learning. You can also experience the latest approaches. 

More Benefits

Organizations now enjoy top-notch quality, skyrocketed productivity, optimized IT assets, and data-driven decisions. 

A few key use cases reveal how AIOps can enhance your operations. First, it reduces false positives, allowing you to focus your resources and time on alerts that guarantee investigation. 

AIOps also helps build, enrich, and strengthen customer relations. By mapping user experiences, your teams can easily know their users and their pain points. The teams include IT, marketing, e-commerce application development, and sales teams. 

But automation is the greatest contribution of AIOps. Your IT team can now bid farewell to time-consuming, low-level, and repetitive monitoring tasks. The result is team-wide effectiveness and efficiency in resolving IT-related issues. 

The future is pregnant with all these benefits for organizations embracing AIOps. But it’s crucial to identify and solve two primary hurdles:

  • Culture: A proper transition requires proper planning of resources and responsibilities before rolling out the transition. Teams should know the parts to play in the entire process for successful monitoring.
  • ROI showings: Let’s face it- the initial cost is high and prohibitive. However, the cost of sticking to legacy tools is even higher, especially if your company is growing in leaps and bounds. 

AIOps’ long-term benefits are worth it, and loss of productivity, revenue, and poor customer satisfaction will be things of the past.    

Practical Logix, Your Digital Transformation Companion

It’s time to embrace AIOps-driven digital application development and monitoring without breaking the bank. Why not let Practical Logix hold your hand? Contact us today for top-in-class cloud application development services. 

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