These days, our lives revolve around applications everywhere, every minute, almost like breathing. Apps are available for a wide range of purposes, from social apps to work apps to play apps to health apps. Truth be told, technology has seamlessly integrated into our day-to-day activities, improving and streamlining them. As we use these apps on a daily basis, we expect them to work flawlessly and fast. Take a moment to recall a time when you used an application and it didn’t work – what did you do? Did you try it or did you move on? A recent consumer survey shows that people have zero tolerance for poor app performance and feel stressed and angry, which in turn negatively impacts brand perception and subsequent referrals.
Hold on! This is where APM(Application Performance Monitoring) comes into play. APM ensures that critical applications meet established performance, availability, and customer experience expectations, enabling an organization to achieve its business goals.
In today’s era of digital reflex with numerous apps, performance issues become increasingly difficult to discover, comprehend and resolve, as applications become complex and bulky. Artificial intelligence can play a significant role in aiding application performance monitoring. With AI-based APM tools, issues can be analyzed and quickly fixed at scale. Additionally, AI-based APMs continuously learn from historical data and can predict problems and suggest solutions even before they arise. Thus, AI is set to revolutionize APM, and companies looking for a competitive edge should take note of this.
Let’s discuss in detail why APM is important and an absolute necessity for organizations.
Why do we need APM?
In the new age of media, everything is defined by good customer experience. Businesses can suffer brand damage or lose revenue if their apps crash, load slowly, or don’t load at all. When internal business applications stop working, employee productivity can suffer as well. APM tools are used to view and address the many variables that can impact the performance of an application. APM software allows collecting metrics about business-critical apps, enabling one to identify any issues before they happen. Additionally, some of the key benefits to the business include:
1. Improved User Experience/Satisfaction
Improved end-user experience is one of the biggest benefits of using an APM tool. The chances of our customers going to our competitors are high when they are not satisfied with our products or services. An APM tool improves the quality of the application, leading to improved user satisfaction and more productive business interactions. It helps ensure productivity and satisfaction by providing deep insights into our app’s performance and its various facets.
APM solutions have been shown to reduce costs by identifying potential problems at the outset and preventing them, resulting in a huge opportunity for businesses to develop new products and services in the future. By tweaking our business constantly, we can minimize the impact poor performance would otherwise have on it.
4. Better Productivity
APM monitoring tools provide 24-hour monitoring support and troubleshooting of problematic applications and codes. This not only helps to stay on top of what’s happening in the organization but also predicts any issues that need immediate attention. An effective prediction facilitates fixing issues appropriately before customers are affected, thereby improving efficiency and productivity. In the last few years, artificial intelligence has been steadily advancing as a technology, and companies are starting to see measurable results in integrating AI into their operations. In order to make sense of the amount of data generated by IT infrastructure every second, a significant investment of time is often required. APM simplifies complex IT systems, automates application environment discovery, and makes intelligent decisions faster by incorporating AI or machine learning technologies.
How can AI facilitate Application Performance Monitoring?
1. Automation of Performance Monitoring & Analysis
AI-enabled APM tools enable enterprises to capture and analyze application and server performance and error metrics from a variety of sources
Metric Collection and Analysis: APM tools can automatically collect and analyze metrics such as response time, error rate, and resource utilization. This data can be used to discover bottlenecks, narrow down performance issues and problems, and set performance baselines for evaluations.
Request-level Tracing and Profiling: APM tools can track and profile the performance of individual requests and transactions. Data at the level of granularity helps in a detailed analysis of the app behavior and can be used to identify and alleviate bottlenecks at a low level and even do optimizations customized to a type or category of requests.
Log analysis: Log data can be analyzed by APM tools to comprehend reasons for observed performance behavior. For example, log analysis can reveal slow database query processing or network issues and can help explain the long response time observed at the app level. AI can play a significant role in the automated correlation of observed metrics and behavior and aid this process. For example, tools such as Retrace provide centralized logging that combines monitoring, error, and logging capabilities tomake it easier to find and resolve production problems.
2. Predictive Maintenance
Predictive maintenance refers to the ability to use volumes of data to anticipate and address potential issues before they lead to breakdowns in operations, processes, services, or systems. AI-based tools can predict what kind of performance issues can occur in the application when they can occur and suggest preventive measures that can be taken to avoid them. For example, online gambling companies can use AI to predict when their servers will likely be overloaded and increase server resource limits to handle the excess traffic.
3. Anomaly Detection
The APM system continuously collects data on various metrics such as CPU usage, memory usage, and network traffic. Machine learning algorithms can analyze this data and identify patterns that are indicative of the average user behavior and system and application performance. These algorithms can also automatically spot any anomaly in observed parameters and trigger appropriate alerts to concerned units.
For example, when CPU utilization rises or network traffic drops in unreasonable amounts, the system can automatically dispatch alerts to concerned parties to notify them of potential problems. AI-based APM systems can also automatically investigate the cause of anomalies and suggest possible solutions. An e-commerce store may see an unexpected spike in traffic due to the launch of a new product. AI-based APM can detect user numbers much higher than previous trends and alerts developers to avoid downtime.
4. Root Cause Analysis
Machine learning algorithms can process and study and analyze large amounts of data and identify patterns and relationships that are difficult to detect manually. AI-based models can analyze data from multiple sources, such as logs, metrics, and traces, and correlate that data to identify the root cause of problems with precision and speed. This is especially useful in complex environments where problems span multiple components (microservices) or systems and a manual root cause analysis may be almost impossible. AI can also suggest solutions to common problems, saving developers time.
5. Personalization & Optimization of the User Experience
AI-based APM can be used to gauge and measure how customers interact with your application on various platforms such as the web and mobile. This data may include user actions, clicks, and other interactions with the application. Performance metrics can also be categorized by device, region, product, product journey, or user journey to better understand the data. By analyzing this data, one can identify patterns, problems, and pain points that users are facing with their applications and provide user-centric optimizations and experience In a digital world where user experience is a critical differentiator, this can enable businesses to boost their market shares.
BYOB: A Side-Kick for APM monitoring
APM tools are crucial in monitoring app performance and addressing factors impacting application efficiency. At Akaike, we’ve developed BYOB (Build Your Own Brain), an AI analyst delivering instant insights on provided data. BYOB enhances monitoring by incorporating advanced features, offering an extra layer to performance assessment.
APM tools collect response time, error rate, and resource utilization metrics to identify bottlenecks and establish performance baselines. BYOB stands out by seamlessly linking various data sources and autonomously arranging information. Through the analysis of crucial metrics and patterns, as well as enabling follow-up questions, BYOB offers valuable insights. Consequently, it effectively supports APM tools, simplifying the pathway for applications to acquire richer insights into data. This, in consequence, elevates user experiences by delivering personalized and optimized results through aggregating diverse data.
Modern, AI-powered APM tools are crucial because they enable automated, real-time responses to many application performance issues. For example, combining AI with edge computing and other technologies, like the 5G network, will enable real-time, proactive application performance monitoring and optimization at scale. This will eliminate the need for IT engineers to wait for an alert, investigate the issue, and create and carry out a remediation plan. APM tools are primarily used by IT operations teams, but the data and insights they collect can also assist developers in identifying ways to improve their applications’ performance and reliability. APM tools that assess complex application behavior and make analytics-driven recommendations thus play an important role in bringing IT operations teams and developers together and achieving DevOps goals.
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