- Predictive Analytics
Proactively detect problems in the making with dynamic thresholds that account for seasonality and identify anomalous behavior tracked across multiple variables.
Normalize, sequence, and analyze millions of events leveraging machine learning to predict and prevent future issues based on advanced pattern identification.
Analyze historic utilization trends for critical infrastructure resources, predict when entities will run out of capacity, and automate expansion to prevent outages.
Given the dynamic, complex nature of today’s IT environments, legacy monitoring techniques and manual processing are no longer viable options when it comes to identifying patterns, spotting anomalies, and predicting future outages. And yet customer expectations for performance and availability have never been higher. What’s an overburdened IT team to do?
Resolve Insights can finally deliver the agility you need while ensuring that your business-critical applications and infrastructure are at their best. Our machine learning algorithms, multi-layer correlation, and cross-domain automation work together across your on-prem, virtualized and cloud infrastructure to predict future issues and automate proactive fixes before they ever impact your business. And that means you can finally declare victory on autonomous IT operations.
Our anomaly detection algorithm leverages unsupervised machine learning to learn your environment, recognize expected behavior, and set dynamic thresholds across multiple performance metrics. The platform draws on this data as it analyzes event patterns in real time and compares those to expected behavior, alerting you when a sequence of events or groups of events demonstrate anomalous activity.
Resolve Insights is smart enough to account for seasonality, meaning that you will only be alerted when a critical system exhibits abnormal behavior across multiple parameters during an unexpected time frame. For example, 90% system utilization might be normal during peak hours from 4-6pm on a weekday, but indicate an issue is forming when those metrics are hit at 4am on a Sunday.
Anomalous groups of events are helpful not only in alerting you to unplanned events, like a DDOS attack, but also to improve planning for expected events like Black Friday or a big sale. Capacity can be dynamically increased for the latter to ensure applications and infrastructure perform well during periods of high demand based on historical patterns, but dropped back down after the fact to account for business-as-usual conditions. That ensures you aren’t paying for infrastructure you don’t need and makes your IT operations more agile.
Resolve Insights stores correlated event data in a time series and then applies machine learning algorithms to identify patterns. These patterns enable us to proactively detect problems before they happen — as well as reduce alarm noise and help pinpoint root cause. Additionally, you can leverage the time-series correlations to playback all of the events that occurred in a time period. The playback highlights every status change, so you can quickly see the patterns for yourself.
How It Works:
Millions of events across applications and infrastructure are normalized and sequenced in a time series and then analyzed by a machine-learning-powered algorithm. The algorithm consolidates multiple incidents based on learned sequences, and then proactively alerts you to potential outages and future impacts when repeating patterns are identified.
Our algorithms can process millions of events in less than five minutes, enabling the identification of unique sequences with 80-100 percent probability with two to ten depths of sequence.
Resolve Insights analyzes historical utilization trends of critical infrastructure resources, such as disk utilization. This allows us to predict when an infrastructure device will become non-operational due to capacity exhaustion based on a weighted slope.
These predictive capabilities ensure more capacity can be added dynamically via a triggered automation (or through manual intervention) to avoid business outages stemming from common capacity issues.
Predict and prevent outages before they impact business-critical applications
Trigger autonomous actions to resolve issues without human intervention
Improve application and infrastructure reliability, performance, and uptime
Be alerted only to real issues based on dynamic data analysis
Dynamically adjust capacity and resources to meet demand, while eliminating unnecessary costs
Eliminate countless hours of manual work to validate, sort, and correlate data
Achieve more agile IT operations through prediction and automation