AppsOne® is an IT Operations Analytics software, which adopts a proactive monitoring approach,
to capture and analyze performance metrics across the technology stack.


AppsOne uses a patented real-time self-learning Analytics technology, which is built upon a three-dimensional performance management model; anomaly detection, capacity planning and user-behavior learning.

The analytical engine captures performance metrics and discovers high-level usage patterns. Then it correlates the behavior of the application with the underlying infrastructure to establish baselines and detect performance anomalies.

Performance is measured with regards to how fast transactions are completed on behalf of, or information is delivered to the end user by the application via a network, application and/or web services infrastructure.


Auto Discovery

Auto Discovery is a feature that dynamically discovers and adjusts performance thresholds.

Pattern matching techniques are used to generate early warning alerts to isolate fault and provide actionable insights; thereby eliminating the manual efforts required to set static thresholds and correlate KPI data across tiers.


Real-Time User Monitoring

Real-time User Monitoring is a module that measures the performance metrics of the configured transactions.

The key performance indicators of each transaction is captured to measure the response time, count of transactions and transaction status.


Synthetic Monitoring

Synthetic Monitoring is a functionality that monitors a website or an application by performing a web browser emulation or via scripted recordings of web transactions.

Using synthetic monitoring techniques, AppsOne captures the business response details of a web application. The Synthetic Monitoring framework will allow the user to post the response details that are captured using simulated test runs for web application, which can be scheduled to post the response details such as success, failure, timeout, slow if the response time thresholds are set for individual pages per scenario and also the error messages to AppsOne.


Intrusive & Non-Intrusive Monitoring

In today’s digital era, applications are built on different platforms and development technologies such as Java, ASP, Native etc.

AppsOne supports both modes of monitoring, depending on the technology on which the application was developed. For example, a Java-based Application needs intrusive monitoring on the JVM. Java Intrusive Monitoring (JIM) is a module that provides insight on the performance of individual methods in the supported Java-based applications.

The Java-Intrusive Monitoring Agent captures details such as Average Response Time and Number of Invocations of the Java methods. If an application is built on native technology, then a non-intrusive mode of monitoring is preferred.


Log Analyzer

Log Analyzer is a module that mines the critical logs for components that constitutes the application.

Specific critical errors are analyzed to know if defined-errors have emerged. By parsing the log information in real-time, the log analyzer provides the number of times an expression occurred, time-based occurrences and error patterns in the machine log.


Application Flow Map

This module provides an end-to-end application centric environment and performs dependency checks on each layer of the application stack. This is achieved by monitoring install binaries, logs and configuration entries.

Based on the application transaction flow, identification and validation of all the infrastructure points is performed in relation to earlier baselines. Infrastructure changes like unavailability of multipath and redundancy will be identified and alerted.

Application – Infrastructure based fault resilience will be tested. In addition, detection of application data flow across all layers is done dynamically, leading to the plotting of a topology map to monitor traffic inflow and outflow.


Batch-Job Monitoring

As part of Batch Monitoring, AppsOne performs the following actions:

  • Learns the behavior of a job and the expected time to complete execution.
  • Regularly check the status of a batch job.
  • Display the progress status of a job on a dashboard along with highlighting jobs that are taking more time to complete than expected.


Big-Data Data Store

In today’s dynamic IT environment, application owners are constantly bracing for impact due to the sheer amount of data that is generated.

The challenge does not stop at just capturing this granular-level data, we need to go a step further to store, analyze, curate and visualize this data using preventive analytics techniques to extract real-time actionable insights in the form of alerts and remedial actions.

AppsOne provides faster data access through the availability of a NoSQL database management system, which is built to process large data sets across multiple servers.