for the incoming request interval, P (x) =  k
. The request interval decreases
with k, and consequently the load on the servers increases.
Figure 3.6 (a) shows the reduction of response time due to deployment of the
DCS coscheduling scheme on the Web server. The response time of the dcs model is
over four times less than that of the press via model when the Web cluster is lightly
loaded. However, as the server load increases, the latency of dcs increases and the latency
differences of the dcs and press via models decrease. After the point k = 5, there is a
sharp increase in the the average response time. This is because the initial node waits
for a reply from the service node by spinning. When the servers are lightly loaded, the
spinning time is relatively short and DCS scheme saves over the blocking and context
switch overhead to process remote cache read requests. However, as servers become
heavily loaded, the large number of remote cache read requests on the service nodes
compete for CPU cycles, thus the chance that the remote servers reply remote cache read
requests back to the initial nodes within a time quantum is reduced. If the initial node
cannot receive a reply within a quantum, CPU time is wasted for spinning.
The adaptive model can yield the shortest latency time over the entire workload
by enhancing the dcs model. In a light workload, the adaptive model behaves like the
dcs model, thus, yielding a similar latency. However, when the server load increases, the
adaptive model gives lower latency unlike the dcs model. Because the adaptive model
yields the CPU to other processes instead of wasting the CPU time depending on the
thresholds, it shows a much better latency time than the dcs model. When k > 4,
the latency of the adaptive model is over three times less than that of the press via
model. The latency of the both models increases when k is less than 3. The latency



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