PECNS T or F

Assume that in a subway station trains arrive at the platform with random interarrival time, exponentially distributed, on average every 10 minutes. We visit the platform everyday at 12:00. On average, we have to wait 5 minutes for the train.

false

Assume that in a queue with a finite buffer the loss ration is 0.01, the burst ratio is 2. In such a case, the average length of the series of consecutive losses is slightly smaller than 2.

false

Assume that in a queue with an infinite buffer (no losses), the arrival system is Poisson, the service time is uniform. In such a case, the output process is Poisson.

false

Assume that the transition matrix of a discrete-time Markov chain is [0.2 0 0.8 1]. This Markov chain is irreducible.

true

Assume that the transition matrix of a discrete-time Markov chain is [1 0.5 0 0.5]. The period of this chain equals 2.

false

Assume we have a discrete-time Markov chain: X0, X1, X2, X3, ... The subsequence X0, X3, X6, X9, ... is a discrete-time Markov chain

true

FES is an acronym from Forward Entrance System

false

FES is an acronym from Future Event Set

true

If the interarrival time distribution is exponential then the steady-state queue size distribution is the same as queue size distribution observed by arriving jobs

true

In a queueing system the real, unknown probability of the queue size 30 is equal to 1.2345678910-6. Finding this probability with the six-digit precision, i.e. 1.2345610-6, requires more than 108 measurements of the queue size

true

In a queueing system with a finite buffer it holds: X=(1-L)R

true

In a queueing system with a finite buffer it is possible to compute the burst ratio, if we know only the loss ratio and the buffer size

false

In a queueing system with a finite buffer it is possible to compute the empty system probability, if we know only the system load and the loss ratio.

false

In a queueing system with a finite buffer the service time is constant and equal to 2. The duration of the buffer overflow period may be 1 in this system

true

In a queueing system with a finite buffer the service time is constant and equal to 10. The duration of the buffer overflow period may be 15 in this system

false

In a queueing system with losses it holds: L=1-(1-pn/p)

true

In a queueing system with no losses, the average queue size is equal to the arrival rate multiplied by the average response time.

true

In a queueing system with Poisson arrivals the average queue size distribution observed at arbitrary times is the same as when observed at arrival times.

true

In an open Jackson network, for every queue it holds: Xi=(1-p)/p

true

In every queueing system with a finite buffer, the loss ratio is equal to the full-buffer probability: L=pN

false

In some queueing systems the distribution of the waiting time can be the same as the distribution of the virtual waiting time

true

In some queueing systems with a finite buffer, the loss ratio is equal to the full-buffer probability: L=pN

true

In the M/M/1 queueing system it holds: X=(1-p)/p

false

Merging two Poisson processes of rates lambda1 and lambda2, respectively, creates another Poisson process of rate lambda1+lambda2

true

Method empty() is used to remove all jobs from the queue

false

Method front() adds a message at the beginning of the queue

false

Method setNumCells() sets the maximum allowed queue size in a cQueue object

false

Method scheduleAt() is used to schedule a message in the past

false

simtime_t is a function returning the current simulated time

false

The loss ratio may be equal to the full-buffer probability (i.e. the probability that queue size is N)

true

The queue size distribution observed just before arrival times is the same as the queue size distribution observed just after departure times.

false

The steady-state queue size distribution depends on the variance of the service time

true

The system response time is the sum of the waiting time and the service time of a job

true

The uniform distribution has the memoryless property

true

The waiting time distribution is always the same as the virtual waiting time distribution

false

Waiting distribution is always the same as the virtual waiting time distribution

false