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Watch for free. There is a short peak just before midnight, which is possibly a batch or a backup job. The black line is the observed metric and we can see that normal behavior has occurred; so in this case, there is no alerting to be done as the metric is operating normally.
If an observed metric deviates outside of the DT range, it is classed as an Anomaly and highlighted in yellow in the Metric Selectors and the associated Metric Graphs in the vRealize Operations dashboards. The number of anomalies observed over time is also recorded for every object, and vRealize Operations uses these derived metrics to determine whether the number of anomalies being observed is significant and if it is required that an alert is generated.
Performance or availability problems are generally caused by something different happening with the resources within an environment, and this "something different" causes associated metrics to breach their DTs.
This means that the majority of alerts that are performance or metric related will only be generated when abnormal behavior occurs. This dramatically reduces the number of alerts that IT operations receive and increases the quality of those alerts. The content baked into vRealize Operations is how the solution creates the intelligent and meaningful alerts. There is a lot of content provided by the solution and much more content will be added with the installation of Management Packs. Custom content can also be created very easily and will be described in Chapter 5 , Alerts, Symptoms, Recommendations, and Actions.
An example of one of the out of the box content alerts and how it is constructed is as follows:. Symptom s : They are descriptions of one or more conditions under which the alert is triggered.
In the preceding example, the symptoms are that a VM is swapping to disk, has high ballooning or has memory compressed, and has high memory contention. Recommendations : They are remediation action s that can be taken to resolve the symptoms. In the preceding case, the action may be to add a memory reservation or initiate a vMotion to migrate some VMs to another host or cluster with more capacity. Actions : They act on the recommendation s.
In the preceding example, the out of the box Python script can be used to set a memory reservation, or vRealize Orchestrator can be used to initiate a vMotion. Alerting based on metrics, which are outside the range of the calculated DTs, can be considered fairly generic and caused by "things happening differently". They tend to be used to troubleshoot and alert on unexpected behavior. As well as triggering alerts based on unexpected behavior, much of the content in vRealize Operations Manager is based on specific behavior and documented best practices.
For instance, storage latency would generally be considered performance impacting by a storage administrator, when it reaches ms. Content within vRealize Operations Manager can also include Hard Thresholds HTs , such as a figure of ms for storage latency, which can trigger alerts regardless of the state of the DT for the given metrics.
Content and alerts will be covered in much more depth in Chapter 5 , Alerts, Symptoms, Recommendations, and Actions. Capacity management is one of the most important disciplines in IT Operations. Unfortunately, as virtualization has matured, traditional capacity management techniques have tended not to keep up with the technology.
My experience of working with clients with mature virtualized environments and outdated capacity management practices is that they find themselves with a lot of underutilized infrastructure, resulting in a lot of wasted resources.
Capacity remaining : Taking into account the reserved capacity for vSphere HA and the headroom buffers, it answers the question about how much capacity remaining does a given resource have?
In the preceding screenshot, we can see that we have enough capacity available in this resource to support a further 32 average sized virtual machines. Time remaining : Again, taking into account the reserved capacity for vSphere HA and the headroom buffers, it answers the question when am I going to run out of capacity?
In the following screenshot, we can see that the capacity for this resource is going to run out in 87 days and that CPU is the constrained resource. Every object or resource in vRealize Operations can have a capacity model configured against it.
This describes the metric s used to determine the capacity and the other factors, or constraints to be considered, such as vSphere HA. The models themselves are not configurable, however, how they are applied generally is configurable, and is managed within the policies section of vRealize Operations. Many of the VMware and third-party Management Packs have capacity models associated with the resources they are managing.
The documentation for these Management Packs usually provides the administrator detail on how the capacity of a given object type is calculated. The policies governing the capacity management in vRealize Operations are very granular and controllable.
This allows the administrator to define what combination of demand or allocation capacity policies are applied against specific resources or groups of resources. Watch for free Migrating to Hyper-V for VMware administrators — this session is geared towards VMware admins who are looking to broaden their horizons by adding Hyper-V know-how to their toolbox.
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If you are a VMware administrator who is interested in automating your infrastructure, this book is for you. An understanding of basic programming concepts is advised.
No previous knowledge of Orchestrator is required, although some previous knowledge of it will allow you to get started more easily.
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