With the NBA All-Star game just ahead and the NHL game just behind, it made me consider the All-Stars of Application Monitoring — Automation and Artificial Intelligence (AI).
Automation is the workhorse that does all the little things: In basketball terms, that means rebounding and setting screens. For hockey, that's killing penalties and floor-checking.
For APM, "Automation" supports tasks like Discovery, Setup, Configuration, Reporting.
Then there's the superstar playmaker — the player that sees the whole field of play, understands what will happen beforehand and places resources in the right position to make the winning plays. For the new generation of APM, that's AI.
AI sees everything; analyzes everything. AI recognizes strengths and weaknesses. AI sees events and problems before they actually occur and takes actions as needed to address the situation.
A Brave New World
Today's digitally transformed organizations have embraced Agile and DevOps to help them quickly and efficiently deploy dynamic applications using containers and microservice architectures. Developing applications at an increasingly rapid pace with these new technologies helps businesses succeed, but it adds tremendous complexity to the application and IT environments.
Automation and AI technologies are critical to the next step in APM evolution
Operations teams can no longer manually monitor and manage today's applications due to their dynamic and complex nature, as well as the speed of development and deployment. APM had to evolve to keep pace with development velocity and maintain the service quality for the modern applications born out of digital transformation.
Automation and artificial intelligence (AI) technologies are critical to the next step in APM evolution, helping to address speed, scalability and intelligence demands.
Automation and AI are Better Together
On basketball and hockey teams, when each star does their job together with their teammates, it's a winning combination. APM is no different when AI and Automation get together. Unlike a sports team, though, "losing a game" for APM owners can be catastrophic.
Today's businesses ARE their applications. If an application isn't running well, users (employees, partners, or customers) are unhappy and the business suffers. If an application completely breaks, it can severely impact the bottom line.
For APM to work, it needs more than data. It needs actionable information to analyze and manage application performance.
Early APM tools did pretty well managing applications that were 3-tiered monoliths or SOA-based. But with the growth of cloud, microservices and containers, monitoring and managing today's applications requires increased speed, scalability and intelligence.
Unfortunately, the development velocity (approaching continuous delivery) and application complexity make it impossible for humans (even experts) to analyze all the necessary information and take appropriate actions to keep applications performing properly.
Enter the two new APM all-stars — AI and Automation — the only way for any organization to regain control over their complex applications. Why are both of these great concepts required, and required to work together?
1. To match the speed and scalability of the modern dynamic applications
2. To analyze the massive amounts of data gathered and measured from each application
3. To take appropriate actions to adjust the infrastructure, platform or microservices to ensure the application performs as needed
Proper automation sets the stage for AI to analyze and act on application performance through:
■ Continuous discovery and mapping: Proper application of AI requires completely accurate data, especially dependency information. Otherwise, you're wasting time analyzing bad data. With automated, continuous discovery, application maps are always accurate and always reflect reality.
To deliver actionable information, AI has to start with Hi-Fidelity data
■ Hi-Fidelity data: To deliver actionable information, AI has to start with Hi-Fidelity data. After automatically discovering the components and mapping the application, monitoring can begin. But the information must be in real-time. As fast as applications change, operations teams need to those updates — in one second or less. This ensures AI is applied to the most accurate data possible.
■ A full stack application model: Because true AI can only operate with the deepest visibility, understanding the dependencies and configurations among the physical and logical components — in real-time — is critical.
■ Accommodating container and microservice fluidity: Modern applications are in constant change, are deployed in hybrid environments and will likely include multiple programming languages, middleware and databases, especially if using microservices. Automated discovery with zero configuration will help operations keep pace with development teams and application change.
With automation providing up-to-the-second, high-quality data; a full understanding of the application component dependencies; and the ability to handle modern development and deployment paradigms such as microservices and containers, AI confidently can be engaged to provide:
■ Real-time, AI-driven incident monitoring and prediction: DevOps teams need accurate and actionable information within seconds after change occurs. That type of information also is invaluable when a performance problem is imminent. Applying real-time AI to the monitoring data, which is coming in real-time, delivers analysis and performance insights in less than three seconds.
■ AI-powered troubleshooting and resolution: Modern applications are simply too complex for humans to effectively manage on their own. Because of the dynamic nature of their structure, their complex dependencies and the sheer scale, machines learning and AI must be applied. The application intelligence generated provides a baseline for problem prediction and resolution.
Automation and AI working together (and operating in seconds, not minutes) are the core components for managing modern applications. They can combine to create a democratized APM solution capable of handling the speed, scalability and intelligence that today's dynamic applications require.
The Latest
The 2024 State of the Data Center Report from CoreSite shows that although C-suite confidence in the economy remains high, a VUCA (volatile, uncertain, complex, ambiguous) environment has many business leaders proceeding with caution when it comes to their IT and data ecosystems, with an emphasis on cost control and predictability, flexibility and risk management ...
In June, New Relic published the State of Observability for Energy and Utilities Report to share insights, analysis, and data on the impact of full-stack observability software in energy and utilities organizations' service capabilities. Here are eight key takeaways from the report ...
The rapid rise of generative AI (GenAI) has caught everyone's attention, leaving many to wonder if the technology's impact will live up to the immense hype. A recent survey by Alteryx provides valuable insights into the current state of GenAI adoption, revealing a shift from inflated expectations to tangible value realization across enterprises ... Here are five key takeaways that underscore GenAI's progression from hype to real-world impact ...
As software development grows more intricate, the challenge for observability engineers tasked with ensuring optimal system performance becomes more daunting. Current methodologies are struggling to keep pace, with the annual Observability Pulse surveys indicating a rise in Mean Time to Remediation (MTTR). According to this survey, only a small fraction of organizations, around 10%, achieve full observability today. Generative AI, however, promises to significantly move the needle ...
While nearly all data leaders surveyed are building generative AI applications, most don't believe their data estate is actually prepared to support them, according to the State of Reliable AI report from Monte Carlo Data ...
Enterprises are putting a lot of effort into improving the digital employee experience (DEX), which has become essential to both improving organizational performance and attracting and retaining talented workers. But to date, most efforts to deliver outstanding DEX have focused on people working with laptops, PCs, or thin clients. Employees on the frontlines, using mobile devices to handle logistics ... have been largely overlooked ...
The average customer-facing incident takes nearly three hours to resolve (175 minutes) while the estimated cost of downtime is $4,537 per minute, meaning each incident can cost nearly $794,000, according to new research from PagerDuty ...
In MEAN TIME TO INSIGHT Episode 8, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AutoCon with the conference founders Scott Robohn and Chris Grundemann ...
Numerous vendors and service providers have recently embraced the NaaS concept, yet there is still no industry consensus on its definition or the types of networks it involves. Furthermore, providers have varied in how they define the NaaS service delivery model. I conducted research for a new report, Network as a Service: Understanding the Cloud Consumption Model in Networking, to refine the concept of NaaS and reduce buyer confusion over what it is and how it can offer value ...
