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

is continued till the simulation end condition is reached. During the simulation or at the end of
the simulation statistics are gathered to analyze the results of the simulation. Generally, DES
model can be designed in an event-oriented and a process-oriented point of view. In the eventoriented
technique the DES model designer takes the events of the system and how they affect the
system state variables of the model as major concerns. On the other hand, process oriented point
of view enables to model the entities, their processes and how the inter-process communicates
take place. The event-oriented design produces simulations that can execute faster compared
to process-oriented design, however, modularity extendibility and the understandability of the
system is a trade-off. Both of these design techniques can be used, however process-oriented
design is popular among the commercial simulation products [1]. Further, a DES simulation
can be designed with deterministic and stochastic inputs and variables. For instance, a resource
utilization time is precisely 5 seconds for any request is deterministic, where as the utilization
time is determined by a probabilistic distribution is stochastic.
3.2. DES model of a multi-client class system
In this section, we provide implementation details of the simulation environments developed
following the guidelines provided in [1]. Here, we have taken the process oriented design
technique because it provides modularity, extendibility and convenience to design using general
purpose object-orient programming languages like Java and C#.Net. Further, we use stochastic
inputs and variables in this simulation to represent the variability in multi-client class systems.
The DES simulation model constructed has following entities (components) in the architecture
corresponding to the characteristic architecture of Figure 1.
MasterClock: This component keeps track of the current time instance of the system and
advances the time after all events and activities specific to the current time instance have taken
place. It triggers events on the tick (smallest time unit) and major tick (which is 1000 ticks).
Request: This represents a client request flowing through the simulation model. It has the
properties of client class Id, start time, end time and processing time. The processing time is
determined by a probabilistic distribution specified by the designer.
ClientClassWorkloadGenerator: This component generates workloads for a specific client
class. It needs a client class id, workload script and the corresponding queue instance at the
initialization. Then the process of this component is at each tick the workload script is analyzed
and generates the required number of requests that have to be sent to the system. Then, the
requests are initialized with the class id and the start time and enqueued to the corresponding
queue. Currently it can simulate deterministic time varying and stochastic (e.g., Poisson process)
workloads.
Queue: In a multi-client class system there is a corresponding queue to each client class
(see Figure 1). The Queue component is used for this purpose. It is a container of the requests
generated by the ClientClassWorkloadGenerator and ordered in a first-come-first-out fashion.
The simulation model needs N Queue instances to represent N queues.
ResourceUnit: The ResourceUnit entity is an abstraction of a resource unit in a multi-client
class system. It simulates the time period a resource is reserved/occupied/provisioned to serve a
request of a client class. It has the currently served request, serviced client class, status (idle or
working) as attributes. The process of this entity is at each tick, it simulates the processing time
specified on the request it is serving. When the request has utilized the resource for the specified
period of time it is assumed to be sent back to the client after stamping the end time. However,
in this simulation the copy of the served request is also sent to the statistical analysis component
to compute measurements such as response times.
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