By Jim Greene
Predictive monitoring has a range of business applications, from uncovering fraud in credit card transactions to detecting intrusions in a computer network.
With deeper context for fluctuating metrics through predictive monitoring, anomaly detection, and forecasting, identifying aberrant behavior in network traffic patterns can help an IT Security team ward off attempted attacks. Algorithms modeled to forecast expected behavior can give your team the ability to both visualize expected trends and specify when they want to receive alerts about potential issues.
CenturyLink’s Cloud Application Manager takes a best-practice approach to automation that weeds out a greater number of false positives than standard systems. This is where other options that rely on threshold-based alerts for cloud monitoring — especially when alerts occur and there’s really nothing wrong — leave much to be desired.
Infrastructure supporting workloads is supposed to work hard, so threshold-based alerting can define maximum efficiency as a problem. Signals that cross a threshold may not represent an actual problem, while changes that may be problematic may not actually cross the threshold. Likewise, signals that cross a threshold close to an outage scenario may not leave enough time to react to a problem, while adjusting thresholds closer to the norm of the signal...