By Shantanu Kaushik
Today, applications are the primary part of the puzzle for businesses. In some cases, they are the primary business of an organization. A well-performing application is critical to business growth, and it requires careful monitoring and observability to maintain a continuous application production cycle.
With application lifecycle management, implementing continuous integration and continuous deployment (CI/CD) regulates the overall feature-set depending on user feedback or general metrics collection. While everything can be configured to run in a certain way, it takes a lot more than that to make the application architecture dependable enough to withstand the highest loads.
Application performance monitoring is used in application environments to record and analyze the application response time, availability, and behavior in different situations depending on the variables involved. This enables the organizations to ensure all standards related to performance, security, and other critical compliance standards are being met.
Connecting business outcomes with application performance are the baseline parameters that the IT teams follow and require from application monitoring. This happens by collecting vital information that showcases how the application behaves and how quickly it can process the required information. Most crucial business applications are extremely high performance due to the nature of the business or the extensive user base of the application.
Mean time to resolution (MTTR) must be lowered using services that monitor applications, such as Alibaba Cloud Application Real-Time Monitoring Service (ARMS). It applies monitoring and observability with AI and big data technologies to detect a failure before it happens and presents extensive resolution reports to reduce the amount of time to resolve any issue.
Alibaba Cloud ARMS is used for:
While monitoring and observing applications or environments, the most core factors that are recorded to measure performance and changes are:
Application performance monitoring creates a primary monitoring task to watch the resource usage. Server-level CPU usage and memory demands must be monitored to record patterns related to resource availability changes at different points in time and load scenarios.
The disk read/write cycles and the application response times are also monitored. You might wonder why the response time is important. Imagine a stock market or any financial organization that handles ten thousand transactions a minute.
The application could have multiple threads to enhance performance hosted in multiple regions with a good server load balancer (SLB) at the backend. The Alibaba Cloud infrastructure will do the job without fail, but you need to ask yourself a few questions as an administrator or strategist, “Who is responsible for future release acceleration? How quickly can your application cater to all ten thousand requests? What is an acceptable time-frame that you are willing to provide as “standard time to execute” for an application?
Your business value and business process outcome are directly proportional to your application availability. Response time also reflects the optimized resource usage levels. There is a fine line between how well your application is configured and how many times your application requires scaling up or down.
Alibaba Cloud introduced the Application Real-Time Monitoring Service (ARMS) as an end-to-end solution to facilitate monitoring and observability to gather important data and metrics for Application Performance Management (APM). We will discuss Alibaba Cloud ARMS further in Part 3 of this series.
The current Information Technology universe is a wide-spread network connected to form a strong resource pool. We have multiple architectures producing varied results based on different technologies. Handling resource orchestration and operations and maintenance (O&M)is a highly tedious task.
The cloud-native architecture, hybrid cloud, and multi-cloud have enabled a technological net that is very complex but highly productive and featureful. Similarly, microservices have reduced dependency issues while increasing the complexity of a solution based on effective O&M.
As the solutions become more effective and less dependent, they will require more maintenance. This can only be achieved using precise and valuable data that is extracted using monitoring technologies based on AI, machine learning, and big data. This is the only way we can begin to fathom an effective application structure and seamless user experience.
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