Application Monitoring Vs Business Monitoring
Monitoring are the set of processes and tools to ensure that applications remains highly available and responds to user requests within an acceptable time limit. Monitoring tools help to achieve these goals by monitoring metrics such as response times, memory, network bandwidth, IOPS and CPU time. The modern tools that are based on cutting edge technologies like machine learning and AI provides ways to diagnose, triage and resolve issues and bottlenecks in the applications and infrastructure. The aspects that are critical interms of the application monitoring or APM are monitoring metrics, tracing and logging.
- Application Monitoring: In terms of monitoring the metric is a quantified measure that can be leveraged to understand the status of a service, process or an application. Metrics are often compared to a defined baseline to analyse the application or process's status and its overall heath.
- Transaction Tracing: A trace aka transaction tracing is used to understand the complete journey of an end-user request as it travels through all the components, services, processes and different layers of a business application.
- Application Logging: Log files are automatically created by an application and they provide information about end-user behaviour and events that took place in the application life cycle from the start-up till the shutdown.
These next paragraphs deep dives into application vs business monitoring aspects and specifically around the metrics domain.
Application monitoring provides detailed visibility into the performance, availability, response times and user experience of applications and its underlying infrastructure. The application monitoring helps not only to monitor but rapidly triage, diagnose and resolve issues leveraging cutting edge tools and technologies. Application monitoring tools collect, store, and analyses the necessary data and metadata for troubleshooting, optimizing performance, root cause analysis and finally resolutions. They typically rely on different types of instrumentation and profiling processes to provide real-time insights in the application health and its status. When performance exceeds automatically defined thresholds, application teams are notified and then can drill down contextually to trace transaction and performance issues across the distributed infrastructure for triage and resolution.
Few most critical applications monitoring metrics include:
- Performance Monitoring: Measures the average response time for end-user interactions to check if application performance is affecting the speed.
- CPU usage: Monitors CPU usage, Disk read/write speeds, IOPS and memory to see if application performance is impacting these key parameters.
- Application availability and uptime: Measures whether the application is online and available and in good health to end-users. This is also leveraged to determine compliance with organization's SLA.
- Request Rates: Measures the amount of traffic received by the application to identify any significant increases, decreases or coinciding users.
- Error rates: Observes how the application degrades or fails at the software level.
- Discovery: Counts and monitors the servers, applications, services and processes that are running at any one time.
Organization create rules, so the Monitoring tools alerts them when a problem arises or when an application's performance metrics dips in a specific area. They can also prioritize applications based on business-criticality. In virtualized deployments, APM tools can help monitor application servers to ensure that they comply with an SLA. Cloud introduces a host of additional dependencies into application performance. There is cloud application performance monitoring, which focuses on tracking the performance of applications based in private or hybrid cloud deployment models.
Monitoring business metrics, such as revenue, sales, product cost etc. adds huge value in addition to pure technical metrices or observability. The established monitoring tools both commercial and open source allow gathering, processing, analysing and virtualizing of monitoring data including the business metrics. The process requires the tools to collect, store, visualise and analyse data on the target system. There is no significant difference whether these tools are collecting and visualising a response time metric or a metric describing the revenue for e.g. an online shop. As those tools are already in-place in the landscape and will be able to leverage them for business metrices or observability as well. The typical metrics that can be captured leveraging Monitoring tools for e.g. Online Shop example are:
- Overall revenue, product costs, conversion rate and margin for the selected time range.
- Manufacturers with their margin, revenue and number of sold products.
- Number of views broken down by price category.
- Number of views broken down by the heat range of the sauces.
- Number of sales over time broken down by manufacturer.
- Track changes in buyer behaviour across product segments and channels
Business monitoring systems fill an important gap in the monitoring landscape and will soon be part of organization’s analytics portfolio. They are very powerful tools that will supplement technical monitoring dashboards that require humans to observe, detect, and resolve application issues and challenges. The business monitoring system will enable critical insights for IT teams and executives that they won’t see in existing technical dashboards and reports.
Key Characteristic of the Modern Monitoring Systems:
- Anomaly Detection. Anomaly detection capabilities in monitoring tools can automatically alert users when metrics deviate from the thresholds, assess their impact, all without human intervention.
- Correlations. Correlation engines go a level deeper and compare all the parameters that contribute to metric outcomes. They analyse and measure subtle changes in one or more business metrics.
- Root Cause Analysis. Root cause analysis engines go even further and suggest possible causes of a deviation from the normal benchmark of a business metric or group of metrics. These engines articulate the cause from historical correlations, or they provide IT-users with tools to assist them localize a cause by comparing multiple correlations.
- Automation: After detecting an anomaly the products will determine its root cause, suggest a remediation, and predict future event. They may even suggest ways to optimize the business process to eliminate future incidents.
The views expressed in this article are the author’s views and AtoS does not subscribe to the substance, veracity or truthfulness of the said opinion.