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Application AI

Modern digital business environments are very dynamic and the reliance of the business on the performance and uptime of business applications, as well as on the underlying infrastructure is critical. Manual processing of massive amount of dynamic data from business transactions to applications to infrastructure to identify patterns, anomalies, and predicting future outages is almost impossible. This poses tremendous business risks and hindrance to business agility.

The answer is a platform that combines the power of machine learning with the ability to auto-discover and correlate entities across transactions, applications and infrastructure.

FixStream’ artificial intelligence quickly predicts business issues across an enterprise’s entire hybrid IT stack. With its machine learning (ML) algorithms and advanced multi-layer correlation across on-prem, virtualized and cloud infrastructure, FixStream can rapidly identify business impacting issues in minutes instead of hours.

FixStream Application AI capabilities include:

  • Dynamic thresholding and Multivariate anomaly detection
  • Sequential Pattern Analysis for Incident Prediction
  • Disk/Network Bandwidth Predictive Analytics

across business transactions (e.g. built on Oracle ERP, SAP ERP, etc.), applications and infrastructure across hybrid IT (on-prem, virtualized and cloud))

Dynamic Thresholding and Multivariate Anomaly Detection

  • Helps to detect an unplanned event (like a DDOS attack) or better plan for a critical event (like Black Friday)
  • Identify a sequence or group of anomaly events
  • Machine learning algorithm automatically learns the expected behavior and sets thresholds accordingly
  • Real time event patterns across the full stack are compared to the expected behavior
  • Alerts are raised only when a sequence of events demonstrates anomaly behavior
  • The anomaly detection is computed across multiple variables such as CPU, memory, bandwidth, etc.

Sequential Pattern Analysis

  • Machine learning algorithms detect event patterns across the entire stack, e.g. from Oracle/SAP ERP through the infrastructure
  • Predicts the next set of business outages that are expected to occur over a certain period of time (e.g. 30 minutes)
  • Indicates the probability that such an outage will occur
  • Predicts, for instance, that eCommerce transactions will stop working after 1 hour due to a detected event such as network bandwidth alert
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