An administrator is reviewing performance of a core banking system that routinely has 20,000 concurrent...

In this case, the administrator wants to investigate the performance of a core banking system that consists of application servers and database servers. The application servers have high CPU utilization during business hours, which may indicate a bottleneck or a resource contention issue. The administrator needs to review multiple metrics in Prism Analysis Graphs to identify the root cause and determine if there is a problem with the app servers only, or with other components as well.

The metrics that are relevant for this analysis are:

Cluster IO: This metric shows the input/output operations per second (IOPS) and throughput (MBps) of the cluster. It can help to understand if there is a high demand for disk IO from the VMs or if there is any latency or congestion in the storage layer.

Cluster CPU: This metric shows the CPU utilization (%) and load average of the cluster. It can help to understand if there is enough CPU capacity in the cluster to handle the workload or if there is any imbalance or contention among hosts.

Cluster Memory: This metric shows the memory utilization (%) and available memory (GB) of the cluster. It can help to understand if there is enough memory capacity in the cluster to support the VMs or if there is any pressure or swapping in the memory layer.

Network: This metric shows the network throughput (MBps) and packets per second (pps) of the cluster. It can help to understand if there is enough network bandwidth in the cluster to transfer data between hosts and VMs or if there is any congestion or packet loss in the network layer.

App Server CPU: This metric shows the CPU utilization (%) and load average of each application server VM. It can help to understand if there is any variation or anomaly in the performance of each app server or if there is any correlation with other metrics.

Database Server CPU: This metric shows the CPU utilization (%) and load average of each database server VM. It can help to understand if there is any variation or anomaly in the performance of each database server or if there is any correlation with other metrics.