Examining Application Performance in the Cloud Age – A Look at Observability

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Last updated on June 24th, 2024

Application performance suggests the operational capability of a deployed software application. It indicates in a calibrated manner how the application is responding to end users under conditions that the software application was designed for. 

Application performance and measurement is a crucial component of a business audit to ensure that software applications satisfy a particular business need as envisioned and expected. As such, application performance monitoring, measurement, and management become integral for all future optimization and design considerations in the application’s life cycle.

Before getting into the nitty gritty of application performance management in the cloud age, it’s essential to shed light on how application performance is evaluated.

What is observability in the cloud?

Observability in the cloud refers to the ability to measure the internal states of a cloud-based system by examining its outputs. It involves collecting, analyzing, and visualizing data from logs, metrics, and traces to gain insights into the system’s performance, health, and behavior. This helps in identifying and diagnosing issues, optimizing performance, and ensuring reliability and security.

Why is observability important in the cloud?

Observability is crucial in the cloud because it allows organizations to proactively monitor and troubleshoot their applications and services. It helps in detecting and resolving issues quickly, optimizing performance, and ensuring reliability and scalability of cloud-based systems.

Application Performance Management: Metrics Collection and Goals

Application performance management (APM) involves the continuous monitoring and evaluation of key operational and telemetry data to assess the performance of an application. Various metrics are collected under different usage conditions, including peak and normal operational states.

  • Continuous Monitoring: APM entails ongoing monitoring of performance metrics to ensure insights are captured across various usage scenarios.

  • Response Time Analysis: Evaluation of response times based on user actions and navigation paths within the application to gauge efficiency and user experience.

  • Infrastructure Resource Utilization: Measurement of how infrastructure resources are impacted by transaction volumes using advanced telemetry.

  • Optimization Focus: The primary goal of APM is optimization, which encompasses usability enhancements, ensuring application availability, and optimizing response times.

  • Performance Standards: APM ensures that application design inputs and outputs meet acceptable performance standards, ensuring a seamless user experience.

  • Business Continuity: A key objective of APM is to address all relevant indicators to maintain uninterrupted business operations.

  • User Interaction: APM aims to enhance the ease and effectiveness of user interactions within the application environment.

  • Scalability Assessment: APM involves assessing how well the application scales with increasing user loads and transaction volumes.

Application Performance Management and the Cloud

With the advent of the cloud and its associated services, application performance management with respect to cloud-centric operations has also evolved. Decisions on how to maintain a high level of performance of the application based on workloads, functions, infrastructure, and services offered specifically to the cloud are taken into consideration. 

Application performance management in the cloud also considers the complexities of the hybrid nature of the network bandwidth, latency, communication, and security infrastructure of the cloud environment.  

Application Performance Management and Observability

Maintaining and monitoring the state of an application in the cloud has become far more involved and complex. Using existing application performance management technologies and tools is insufficient. Just collecting predefined data and telemetrics of an application running in a cloud may not be accurate enough for continuously tweaking for optimal application performance.

Collecting application metrics and telemetric data of a cloud-based application might indicate a possible reason to visit an issue but not give the root cause of the issue. Tracing the root cause of a particular state of the application is challenging to accomplish using just performance data due to the complex environment of the cloud.

To that end, observability and application performance management are complementary technologies that can be used to monitor the visibility and state of any application in the cloud.

Observability – Taking Root Cause Analysis to the Next Level

The classic definition of observability comes from system control theory, where observability is a measure of how well the internal states of a system can be inferred from knowledge of its external outputs.

In observability, the data and telemetrics collected by application performance management tools are further analyzed to understand the state of the application and the environment it depends on. The overall behavior of the application within the system is taken into account, and then queries are built around that for root cause analysis for any system enhancements, errors, or upgrades.

Operational data collected is further mined and queried to get better visibility of the application’s state and whether business objectives are being met. Observability offers flexibility in querying monitored data available in dynamic ways. The more data points that are collected with their relationships, the more effective the adoption of observability. 

More observability means a clearer understanding of the system’s state, thus making decisions on application performance and interactivity more effective and accurate. In essence, observability also means the narrowing down of the root cause due to the availability of an increased number of data points.

Observability – 3 Pillars

Logs, metrics data, and traces are considered the pillars of observability of application performance. The data for these pillars are based on the application performance management tools installed within the system. This data is vital to observability. The focus or objective of observability is defined for that application, and then queries based on these objectives can be raised. 

Benefits of Observability

Observability provides a comprehensive analysis of applications, operating environments, transmission networks, and endpoints. This holistic approach reduces the perceived complexity of the infrastructure and enhances the accuracy of root cause analysis.

Key Benefits:

  • Holistic Analysis: Applications, operating environments, transmission networks, and endpoints are analyzed collectively.

  • Reduced Complexity: Simplifies the perceived complexity of the entire infrastructure.

  • Accurate Root Cause Analysis: Easier and more precise identification of issues.

  • Performance Optimization: Enhances application performance and end-user experience.

  • Improved Business Processes: Streamlines business operations by optimizing application performance.

  • Simplified Troubleshooting: Facilitates easier error handling and resolution of component response issues.

  • Intelligent Data Mining: Enables varied queries about the state of the application and operating environment using logs, traces, and telemetry data.

Steps To Introduce Observability into Application Performance Management

To introduce observability into an enterprise, there has to be a relook into corporate strategy and whether an enterprise deployment is achieving the business goals. More profoundly:

  • An application’s observability has to be built around updated business processes to increase customer usability. 

  • There should be a focus on current metric collections, and gaps in data and analytic mechanisms must be unearthed.

  • A detailed study of how the introduction of observability into the enterprise can increase root cause discovery also needs to be done.

  • Existing application performance management tools and the data collected via the various dashboards must be updated and upgraded to take advantage of the visibility introduced by observability. 

  • Furthermore, IT teams must select appropriate observability platforms and vendors and ensure due diligence is done on their suitability to the enterprise.

Observability Is the Need of the Hour

Technology has evolved to collect a massive amount of data relating to the performance of an application and its environment. Unfortunately, just having colled vast amounts of data has not always addressed the issues of business processes, performance optimization, and better user experience. 

Favorably, with observability, a holistic view of the state of the system is possible, thus making it easier to ensure the objective of the business process itself is being met. 


How does observability impact application performance and reliability in the cloud?

By providing real-time insights into performance metrics, identifying bottlenecks, and tracing dependencies, observability helps optimize resource utilization, enhance reliability, and improve overall user experience in cloud environments.

What role does AI and machine learning play in enhancing observability in the cloud?

AI and machine learning technologies can analyze large datasets generated by observability tools to detect patterns, predict anomalies, and automate responses. This proactive approach helps in preemptively addressing issues before they impact users.

How can organizations ensure compliance with observability requirements in the cloud?

Organizations can implement auditing and governance frameworks to ensure observability practices comply with regulatory standards and industry best practices. This involves secure data handling, access controls, and regular audits of observability tools and processes.

What are some best practices for implementing observability in cloud-native applications?

Best practices include:

  • Instrumenting applications with metrics, logs, and tracing from the outset.

  • Leveraging automated monitoring and alerting to detect and respond to issues promptly.

  • Adopting centralized logging and distributed tracing solutions for comprehensive visibility.

  • Using service meshes and observability tools designed for microservices architectures to manage complexity.

What are some common challenges in achieving observability in the cloud?

Challenges include managing large volumes of data generated by distributed systems, ensuring compatibility and integration across various monitoring tools and platforms, maintaining data security and privacy, and handling the complexity of dynamic and ephemeral cloud environments.

How can observability contribute to DevOps practices?

Observability supports DevOps practices by enabling teams to collaborate more effectively across development, operations, and QA. It facilitates faster deployment cycles, quicker incident response times, and continuous improvement through data-driven decision-making and feedback loops.

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