model, we also simulated a blocking scheme with DCS. In the DCS blocking model,
instead of waiting for the reply by spinning, the remote cache send process blocks itself
immediately and yields the CPU to other processes. The process is waken up after
receiving the reply. Therefore, the blocking scheme can avoid wasting of the CPU time
due to spinning.
Adaptive Web Server Model
The coscheduled Web server model can boost performance when the communicat 
ing processes in the initial and the service node are coscheduled until a remote cache read
job is completed; thus the response time of the request is reduced. This performance
gain diminishes when the load on the server becomes heavy. This is because when a
server is required to process a large number of remote cache read requests, before the
remote cache recv process completes one job, it is forced to be context switched out by
a newly arrived remote job. Then the remote cache send process in the initial node can 
not receive a reply from the service node within its time quantum. Moreover, since the
DCS coscheduling uses a priority boosting mechanism for the remote cache requests, the
processing of local cache read requests can be delayed, consequently causing performance
degradation when the server load becomes heavy,
Therefore, we extended our coscheduled Web server model to adapt to a server's
load conditions. Unlike the DCS coscheduling model, the adaptive coscheduling model
applies the DCS coscheduling scheme only when the coscheduling scheme can enhance
performance. The adaptive model uses two thresholds, T
and T
, which are used at the
initial node and service node, respectively. First, at an initial node, if the number of



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