OpsCruise Detects and Resolves a Range of Problems with Modern Apps & Infrastructure
What’s happening with pods in my Kubernetes clusters, and do I need to do anything?
Browser receives a 500 error. A pod is continually restarting, which one?
OpsCruise continually integrates insights across containers, pods, and nodes from Kubernetes events and configuration to detect restarts, pending issues and other problems with all Kubernetes objects.
Beyond traditional monitoring tools, OpsCruise checks if the provisioned limits for the containers are appropriate, including cases where pod rebalancing is needed to avoid potential evictions.
Tired of chasing metrics and thresholds, and trying to guess what thresholds to set?
Onboarding an app with 60 microservices. How do I set up alerts?
OpsCruise removes the guesswork in determining which metrics to monitor and what thresholds to set. Deriving 'influencing metrics' and 'auto-thresholding' are features of OpsCruise’s contextual ML which learns the expected behavior of each container. Dynamic metric selection and thresholds will enable you to detect emerging, otherwise undetectable problems.
Take the guesswork required with traditional monitoring tools.
Flying blind on interactions between microservices across clusters?
One service in a cluster is having problems with a service in another cluster. Which one and why?
OpsCruise's operational Flow Tracing tracks traffic in real time between all components without touching your application. You can now see flows on request rates, data traffic, latency performance and even error rates across the system. By linking application metrics, Kubernetes and the infrastructure across the container path into one dynamic graph, you get an integrated view of the ‘fenced’ local context around the faults.
No other monitoring solution has so accurately 'stitched' together today's modern microservices environment.
How do you know whether a container image update caused a problem?
The customer is unhappy with the response time of a business service. One or more pods were updated. Which ones are those, and how are they performing?
One of the most challenging problems is confirming that an application code update creating ‘under the radar’ changes is the source of a problem. Using its adaptive machine learning, OpsCruise automatically detects application behavior changes (e.g., cache hits or Disk I/O changes that result in resource and service demands for the same incoming load). It then updates the learned behavior model through continuous monitoring. Combining this behavior model adaptation with detecting changes such as image updates enables pinpointing the root cause.
How do you increase your business agility when rolling out new services and changes?
Canary testing unreliable and taking too long ?
OpsCruise creates a continuously learning behavior model for each container. This model can be used to determine how the changed image impacts performance as well as infrastructure needs.. When the code for the container is modified, OpsCruise’s monitoring records the new performance, the resource usage and the service requests. Comparing model-predicted outputs with the actual metrics indicates whether the current provisioning will cause performance problems across the expected range of workload volumes and transaction mixes. Differences are highlighted and explained. Changes in required resources and services needed to meet performance SLOs are identified. Once the new image is approved, a new behavior model is built for the green version and deployed for runtime use.
Are you always finding a problem after a component has gone off the rails?Would you like to know that there is a possible impending problem? Tell you ahead of a major performance impact that a container is not working correctly?
OpsCruise creates a continuously updated behavior model for each container. This model is built using a set of curated metrics that include inbound and outbound demands, resource usage and performance metrics.The model captures the valid operational range of the container across varying demand levels. Using the model, performance, resource utilization and services can be predicted in real-time for the current demand load. This predicted behavior is compared to the actual monitored behavior. Anomalous behavior situations are then found without need for setting thresholds or relying on statistical outliers, explained and alerts created.