A simulation model to implement multiple client class server-client ...

class. This is a complex policy, which will cause jittery behavior in system measurements,
inconsistent states in transaction and additional overhead on the shared resource
system during the implementation at runtime [2, 3]. In contrast, in the non-preemptive
setting, the resource is taken away once the request being processed is completed. The
non-preemptive setting is a desirable configuration for shared resource environments [2].
Thus, we have implemented this non-preemptive setting in the scheduler process of the
simulation model. However, the inaccuracy in decision implementation can be reduced by
selecting the processing times comparatively smaller than the sample period. For instance,
if the service time varies in ticks range, sample period can be selected in major ticks. This
means the decision made will be implemented before the next decision made. In addition,
a large amount of requests will be processed during a sample time so that the error due to
incomplete requests during a sample period becomes insignificant.
2. End-to-end response time is not a consideration.
3. Time taken to reschedule the resource is assumed to be zero.
4. Overhead from the scheduler and the statistic calculator is zero.
From the various simulations designed and executed from this implementation indicated that
it can simulate consistent behavior under same settings and N number of clients. It also provides
accurate measurements of the system outputs, correct implementations of resource allocation
decisions and fast executions. The process-oriented design approach taken in this implementation
provides modularity by delegating responsibilities among entities and extendibility. Therefore,
this DES simulation model achieves many of the requirements mentioned in Section 2. What
is left is verify and validate this DES model is capable to simulate the complex behavior of a
multi-client class system.
4. Validation of the DES model
After building a simulation, the next major step is to verify and validate the implementation.
In this section we provide three forms of validations using queuing theory. It is noteworthy
that to apply queuing theory, certain assumptions on the system structure, arrival workloads
and processing time statistical distributions should hold. In the following sections, the required
simulation systems are constructed using the DES model proposed in Section 3, adhering to the
assumptions.
4.1. Conformant to Little’s law
One of the fundamental results of queuing theory was developed by John Little in 1960’s,
which is used as a basic building box in the development of theories of large scale queuing
systems. Little’s law is defined as follows:
For a queuing system in steady state, if the mean time waiting in the system is W = E(T),
and the mean number of customers entering the system is λ, then the mean number of customers
in the system is given by E(L) = W × λ. This result applies to any queuing system and even to
systems within a system. However, system has to be in steady state, meaning that the arrival rate
should be less than the service rate of the system. Therefore, the simulation model presented in
this chapter can be validated using Little’s law.
In order to do the validation, we constructed a queuing simulation using the constructs introduced
in Section 3. We used two client class workload generators and queues with 5 resource
5