Infrastructure environments constantly change and it is impossible to track the changes and correlate the events using legacy techniques. Manual processing of massive amount of dynamic data across the stacks to identify patterns, anomalies, and predicting capacity requirements or predicting failures in infrastructure is almost impossible. This poses tremendous business risks and hindrance to business innovation
So, what’s the solution?
A solution that combines the power of machine learning with the ability to auto-discover and correlate entities across compute, network and storage.
FixStream’ artificial intelligence quickly predicts infrastructure 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 infrastructure issues in minutes instead of hours.
FixStream Infrastructure AI capabilities include:
- Dynamic thresholding and multivariate anomaly detection
- Sequential Pattern Analysis for Incident Prediction
- Disk/Network Bandwidth Predictive Analytics
across compute, network and storage domains in hybrid (on-prem, virtualized and cloud) infrastructure environments.
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)
- First identifies 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 infrastructure 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 hybrid infrastructure
- Predicts the next set of infrastructure 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
- Gives enough time to IT operations to take preventive actions to avoid the outage to occur
Disk/Network Bandwidth Predictive Analytics
- Analyze historic utilization trends of an infrastructure resource
- Predict when the infrastructure entity will run out of capacity
- Allows to proactively plan to add more capacity before it negatively impacts the business