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XenServer Host Monitoring
	What is the CPU load on the XenServer kernel, on the control domain?
	What is the free memory in the XenServer kernel and the control domain?
	Which network interfaces are seeing the most traffic?
	Which volume groups are experiencing a space crunch?
	How much free space is available on each of the disk partitions?
	Are there processes on the control domain that are taking up excessive resources?

Virtual Machine Monitoring
	How many virtual machines are running? What are their IP addresses/host names and operating systems?
	What portion of the XenServer’s CPU is used by each VM?
	How much of the memory allocated is a guest actively using?
	Which processes on a guest are taking up high disk, CPU or memory resources?

	Is there excessive paging or memory thrashing in a guest?
	Do all the disk partitions inside the guest operating system have adequate space?
	Is there excessive queuing for disk access on any guest operating system? Which applications could be causing these accesses?

XenServer Desktop Monitoring
	How many desktops are powered on simultaneously on the XenServer?
	How much CPU, memory, disk and network resources is each desktop taking?
	What are the peak usage times?
	What applications are running on each desktop?

XenMotion Monitoring
	Which XenServer is a VM running on?
	When was a VM moved from a XenServer? Which XenServer was the VM moved to?
	Why was the VM migrated? What activities on the XenServer host caused the migration?

Multi-tier IT infrastructures are a nightmare to troubleshoot because of the dependencies that exist between application tiers. For instance, a failure in the database tier could result in slow downs in the application and web server tiers. Hence, monitoring solutions that view the infrastructure as independent silos cannot effectively monitor and diagnose problems in such infrastructures. The addition of virtualization to such infrastructures makes monitoring and management of these infrastructures even more challenging!

Fig 1: A problem in one application can affect all the other applications involved in the service delivery.

Fig 2: Excessive disk reads by the media server slow down Oracle database accesses

Since a single VMware^® ESX/ESXi Server is used to host multiple virtual machines (VMs), a single malfunctioning application on a VM can degrade the performance seen by applications hosted on the other VMs. Figures 1 and 2 illustrate such an example. In this scenario, users are experiencing slowness in their access to a web-based service. From the service topology, it is clear that the database server is the cause of the slowdown. Figure 2 illustrates that since the database server is hosted on the same ESX/ESXi server as a media server, high I/O activity due to increased access to the media server is resulting in the database server seeing slow disk accesses. To accurately diagnose the problem in this example, a monitoring solution must not only consider the inter-dependencies between applications that are involved in service delivery, but it must also consider the existential relationships between applications, virtual machines, and physical machines. Besides resource contention among guest virtual machines, applications executing on the ESX/ESXi service console can also affect the performance of the virtual infrastructure.

While knowing which VM is consuming excessive resources is helpful, it is even more important to understand whether the VM's behavior is normal. For instance, a memory leak in one of the applications executing inside a VM may be causing the VM's memory usage to increase over time. In such cases, it is essential that the monitoring solution be able to look in-depth into each guest VM and detect abnormalities. While deploying individual agents inside each VM provides this level of visibility, this can result in additional resource overhead, licensing fees, and maintenance cost.

Performance degradations in a virtual infrastructure may also be because a virtual machine has not been configured with sufficient resources to handle its workload. A monitoring solution must be able to differentiate problems resulting from inadequate virtual machine configuration and those resulting from hot-spots created by uneven distribution of load across ESX/ESXi servers.


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