The point is that even with the same IO activity perfectly balanced across all devices, because every transaction must go to a specific device, there is no way all devices can always have the same run queue. The result is that even at a low IO device utilization, there will be queuing! And the queue times are highly variable when compared to the CPU queuing configuration. Figure 4-18 graphically shows just how dramatic and immediate queue time occurs when each device has its own queue, resulting in an increase in response time. This happens with the same exact arrival rate and the same speed devices. The only difference is the number of queues in the system.

Figure 4-18. This response-time graph contrasts two configurations with the same device speed and same system arrival rate. The one difference is the dotted line has a queue for each device (like an IO subsystem) and the solid line has a single queue feeding all the devices (like a CPU subsystem). As the graph shows, when each device has its own queue, queuing immediately occurs, resulting in an immediate response time increase.

Based on Figure 4-18, IO subsystem vendors have a difficult problem. Their goal is to somehow, with all their caching and advanced algorithms, to transform the dotted-line situation into the solid-line situation. To transform one fundamentally different queuing system into another is expensive and very difficult. This is one reason IO subsystems seem so expensive.