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bashful doc dopey sleepy sneezy happy grumpy
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Figure 17.9 A SAN-based 6-plus-1 cluster.
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In Figure 17.9 s 6-plus-1 cluster, we have a total of seven nodes, including grumpy, which is running as a dedicated standby. Should bashful s services fail, they will be automatically migrated over to grumpy, who becomes the new home for these services. When bashful returns to service, it joins the cluster as the new standby, so that when dopey crashes, its services will migrate over to bashful. Over time, the layout of hosts and services will not match the original layout within the cluster. As long as all of the cluster members have similar performance capabilities, and they can see all of the required disks, it does not actually matter which host actually runs the service. N-plus-1 will provide availability superior to N-to-1, since no manual return to normal is required when bashful returns to service. If dopey fails before bashful has been put back into service, then, of course, there is no dedicated standby host for dopey s service to move to. What happens next depends on how your FMS handles the situation. Lesser quality FMS will take dopey s service groups and move all of them to some other host in the cluster, say, doc. With an eye toward load balancing, high-quality FMS will take those same service groups and divide them up between the surviving hosts in the cluster. Very high-quality FMS will decide where to place the
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applications around the cluster based on the current loads of all of the hosts in the cluster; extremely high-quality FMS will make the determination based on historic system resource consumption, considering CPU and memory consumption, and I/O rates, along with the types of applications that are currently running on the system. The last option will generally result in the most accurate level of load balancing, though perhaps at the cost of a brief delay in failover time. As clusters begin to grow to 15 or 20 nodes or more, it s possible that a single standby node will not be adequate, especially if it is down for repair for an extended period. We have begun to see larger clusters built in an N-plus-2 style, where, for example, 20 nodes are clustered together, but only 18 of them run critical applications at any time. The remaining two are configured as dedicated standbys. We expect to see cluster sizes grow dramatically over the next few years as blade technology begins to enter the mainstream marketplace. With that in mind, we suggest that a roughly 10 percent system overhead is a sensible level. High-quality FMS should be able to handle just about any mixed-andmatched cluster configuration that makes sense. The astute reader may have noticed that there is no material difference between the configuration layouts in Figures 17.8 and 17.9. That is correct. Since there is no hardware difference between the two configurations, we recommend using N-plus-1, rather than N-to-1, in clusters that are larger than two nodes to take the best advantage of hardware resources and to maximize availability. If there is a serious downside to this configuration, we have not been able to identify it.
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How Large Should Clusters Be
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Unfortunately, it s difficult to make a clear recommendation on the maximum size of a cluster. It would be very nice to be able to tell you that clusters shouldn t be larger than, say, 27 nodes. We cannot do that. We also cannot say that clusters can grow in size without limit, subject to the capabilities of your FMS. The FMS itself can represent a single point of failure, and we have seen organizations who are leery of running all of their critical operations in a single cluster for just that reason. This is especially true if your FMS requires the cluster to be shut down in order to make configuration changes within the cluster. The natural tendency is to grow clusters as large as possible and necessary to limit the number of management points. (From a simplicity perspective, it s pretty much a wash: Ten 2-node clusters are roughly just as complex as two 10-node clusters. In the end, you are just moving the complexity from one
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