CS6352 - Performance of Computer Systems and Networks - IT Assignment Help

Assignment Task

In this problem, you will implement an event-driven simulation of a queueing system. In an event-driven simulation, the system state is updated only when an event (e.g., an arrival or a departure) occurs, rather than being updated at periodic time intervals. When an event occurs, several steps must be taken to update the system state. The first step is to update the system time to the time at which the event occurred. The next step is to update any other state parameters, such as the number of customers in the queue. Finally, new events are generated based on the current event. Once the system state is updated, the simulation moves on to the next event in chronological order.

Queueing System Description
Two machines generate components that are sent to a processing center for packaging. The first machine generates components according to a Poisson process with rate ? components/minute. The second machine generates components according to a Poisson process with rate ? components/minute. If there are two or more components in the processing center, the first machine stops generating components. If there are K or more components in the processing center (K ? 2), the second machine continues to generate components, but these components are discarded (they do not enter the queueing system). The processing center employs m workers who package the components. The time it takes each worker to package a component is exponentially distributed with an average packing time of 1 µ minutes.

Event-Driven Simulation
In the simulation of a Markovian queueing system, we need to consider two basic types of events: arrivals and departures. When an arrival event occurs, we need to perform the following tasks:
• Update the system time to reflect the time of the current arrival.
• Increment the number of customers in the system if the system is not at its full capacity and the arrival is not blocked.
• If there is an idle server that can take the arriving customer, then generate a departure event for the new arrival. The departure time will be the current system time plus an exponentially distributed length of time with parameter µ.
• Generate the next arrival event, if applicable. The time of the next arrival will be the current system time plus an exponentially distributed length of time.

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