vRealize Operations Manager collects CPU use, disk, memory, network, and summary metrics for objects in the vSphere world.
Capacity metrics can be calculated for vSphere world objects. See Capacity Analytics Generated Metrics.
CPU Usage Metrics
CPU usage metrics provide information about CPU use.
Metric Name | Description |
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CPU|Capacity usage | CPU usages as a percent during the interval. Key: cpu|capacity_usagepct_average |
CPU|CPU contention(%) | This metric shows the percentage of time the VMs in the ESXi hosts are unable to run because they are contending for access to the physical CPUs. The number shown is the average number for all VMs. This number is lower than the highest number experienced by the VM most impacted by CPU contention. Use this metric to verify if the host can serve all its VMs efficiently. Low contention means that the VM can access everything it demands to run smoothly. It means that the infrastructure is providing good service to the application team. When using this metric, ensure that the number is within your expectation. Look at both the relative number and the absolute number. Relative means a drastic change in value, meaning that the ESXi is unable to serve the VMs. Absolute means that the real value itself is high. Investigate why the number is high. One factor that impacts this metric is CPU Power Management. If CPU Power Management clocks down the CPU speed from 3 GHz to 2 GHz, the reduction in speed is accounted for because it shows that the VM is not running at full speed. This metric is calculated in the following way: cpu|capacity_contention / (200 * summary|number_running_vcpus) Key: cpu|capacity_contentionPct |
CPU|Demand (%) | This metric shows the amount of CPU resources a virtual machine might use if there were no CPU contention or CPU limit. This metric represents the average active CPU load for the past five minutes. Keep this number below 100% if you set the power management to maximum. This metric is calculated in the following way: ( cpu.demandmhz / cpu.capacity_provisioned)*100 Key: cpu|demandPct |
CPU|Demand (MHz) | This metric shows the amount of CPU resources a virtual machine might use if there were no CPU contention or CPU limit. Key: cpu|demandmhz |
CPU|Demand | CPU demand in megahertz. Key: cpu|demand_average |
CPU|IO wait | IO wait (ms). Key: cpu|iowait |
CPU|number of CPU Sockets | Number of CPU sockets. Key: cpu|numpackages |
CPU|Overall CPU Contention | Overall CPU contention in milliseconds. Key: cpu|capacity_contention |
CPU|Provisioned Capacity (MHz) | Capacity in MHz of the physical CPU cores. Key: cpu|capacity_provisioned |
CPU|Provisioned vCPU(s) | Number of provisioned CPU cores. Key: cpu|corecount_provisioned |
CPU|Reserved Capacity (MHz) | Total CPU capacity reserved by virtual machines. Key: cpu|reservedCapacity_average |
CPU|Usage (MHz) | CPU usages, as measured in megahertz, during the interval.
Key: cpu|usagemhz_average |
CPU|Wait | Total CPU time spent in wait state. The wait total includes time spent in the CPU Idle, CPU Swap Wait, and CPU I/O Wait states. Key: cpu|wait |
CPU|Workload (%) | Percent of workload Key: cpu|workload |
Memory Metrics
Memory metrics provide information about memory use and allocation.
Metric Name | Description |
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mem|Contention (%) | This metric shows the percentage of time VMs are waiting to access swapped memory. Use this metric to monitor ESXi memory swapping. A high value indicates that the ESXi is running low on memory, and a large amount of memory is being swapped. Key: mem|host_contentionPct |
mem|Machine Demand (KB) | Host memory demand in kilobytes. Key: mem|host_demand |
mem|Provisioned Memory | Provisioned host memory in kilobytes. Key: mem|host_provisioned |
mem|Reserved Capacity (KB) | Total amount of memory reservation used by powered-on virtual machines and vSphere services on the host. Key: mem|reservedCapacity_average |
mem|Usable Memory (KB) | Usable host memory in kilobytes. Key: mem|host_usable |
mem|Host Usage (KB) | Host memory use in kilobytes. Key: mem|host_usage |
mem|Usage/Usable (%) | Memory usage as percentage of total configured or available memory. Key: mem|host_usagePct |
mem|Workload (%) | Percent of workload. Key: mem|workload |
Network Metrics
Network metrics provide information about network performance.
Metric Name | Description |
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net|Packets Dropped (%) | This metric shows the percentage of received and transmitted packets dropped in the collection interval. Use this metric to monitor the reliability and performance of the ESXi network. A high value indicates that the network is not reliable and performance decreases. Key: net|droppedPct |
net|Usage Rate (KB per second) | Sum of the data transmitted and received for all of the NIC instances of the host or virtual machine. Key: net|usage_average |
net|Workload (%) | Percent of workload. Key: net|workload |
Disk Metrics
Disk metrics provide information about disk use.
Metric Name | Description |
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disk|Total IOPS | Average number of commands issued per second during the collection cycle. Key: disk|commandsAveraged_average |
disk|Usage Rate (KB per second) | Average of the sum of the data read and written for all of the disk instances of the host or virtual machine. Key: disk|usage_average |
disk|Workload (%) | Percent of workload. Key: disk|workload |
Summary Metrics
Summary metrics provide information about overall performance.
Metric Name | Description |
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summary|Number of Running Hosts | Number of running hosts. Key: summary|number_running_hosts |
summary|Number of Running VMs | This metric shows the number of running VMs at a given point in time. The data is sampled every five minutes. A large number of running VMs might be a reason for CPU or memory spikes because more resources are used in the host. The number of running VMs gives you a good indicator of how many requests the ESXi host must juggle. Powered off VMs are not included because they do not impact ESXi performance. A change in the number of running VMs can contribute to performance problems. A high number of running VMs in a host also means a higher concentration risk, because all the VMs fail if the ESXi crashes. Use this metric to look for a correlation between spikes in the running VMs and spikes in other metrics such as CPU contention, or memory contention. Key: summary|number_running_vms |
summary|Number of Clusters | Total number of clusters. Key: summary|total_number_clusters |
summary|Total Number of Datastores | Total number of datastores. Key: summary|total_number_datastores |
summary|Number of Hosts | Total number of hosts. Key: summary|total_number_hosts |
summary|Number of VMs | Total number of virtual machines. Key: summary|total_number_vms |
summary|Total Number of Datacenters | Total number of data centers. Key: summary|total_number_datacenters |
summary|Number VCPUs on Powered on VMs | Number of virtual CPUs on powered-on virtual machines. Key: summary|number_running_vcpus |
summary|Average Running VM Count per Running Host | Average running virtual machine count per running host. Key: summary|avg_vm_density |