Figure 9-17. Shown is the response time effect of tuning. By tuning, a new response-time curve takes effect (dotted line), and the response time drops from point A to point B. By controlling the workload, performance can remain at point B or by allowing the workload to increase to point C, the system can still maintain both improved response time and an increased workload.

When additional or faster CPUs or IO devices are added to a system, we have effectively increased capacity. For example, because the old CPUs were replaced with CPUs that are twice as fast, instead of a SQL statement consuming 4 seconds of CPU, it now consumes only 2 seconds of CPU. Or perhaps six additional CPU cores were added. Thinking about the basic utilization formula of requirements divided by capacity, if capacity increases and the requirements remain the same, then the utilization must decrease. The only way to increase the utilization is to increase the requirements. One way to do this is to increase the workload (the arrival rate). So, by increasing capacity, we have provided the basic performance-enhancing options of decreased response time, increased throughput, or some combination of both.

From a queuing theory perspective, what really happens when capacity is added depends on if additional transaction processors (think more CPU cores) are implemented or the transaction processors are faster (think faster CPUs)-or if we're lucky, both. If the transaction processors are faster, the service time decreases with the same effect as with tuning. We can expect a new response time curve similar to the one shown in Figure 9-17 to take effect. However, if we add transaction processors with no change to service time, the response-time curve does not shift down. But because there are more transaction processors available, as a whole, they can process more transactions per unit of time, which shifts the curve to the right, allowing for an increase in the arrival rate before queuing sets in.