Homework II

Problem A, Due Thursday, February 7:

This is a variation on the cell phone network in Homework I. Here are the specs:

• Command-line format:

  python Cell2.py arrivalrate durationrate nchannels maxsimtime

• All calls are local. ("All politics is local.")
• If there are no free channels when a call arrives, the oldest call (i.e. the one that started earliest) will be pre-empted.

Your program must find the following:

• The proportion of calls that are pre-empted.
• The average fraction cut off among pre-empted. For example, suppose two calls are pre-empted, as follows. The first would have lasted 1.4 minutes, but was pre-empted after 1.05 minutes. The second would have lasted 2.00 minutes but was pre-empted after 0.75 minutes. So, the first call had 0.250 of its duration cut, while the figure for the second call was 0.675. So, the average fraction lost due to pre-emption for these calls was 0.4625.
• The average number of busy channels, computed in two ways:
  • Using Monitor.timeAverage().
  • Using PeriodSampler.

Some sample output values (PeriodSampler.Per = 0.1:

[root@localhost ~/156]# python Cell2.py 2 2 2 100000
fraction of pre-empted calls: 0.198612925922
average fraction cut among pre-empted: 0.460922131409
mean number of active channels, Method I: 0.795939783419
mean number of active channels, Method II: 0.796024796025
[root@localhost ~/156]#
[root@localhost ~/156]# python Cell2.py 2 2 4 100000
fraction of pre-empted calls: 0.0150618152578
average fraction cut among pre-empted: 0.335597787476
mean number of active channels, Method I: 0.978552568276
mean number of active channels, Method II: 0.978566978567
[root@localhost ~/156]#
[root@localhost ~/156]# python Cell2.py 2 2 1 100000
fraction of pre-empted calls: 0.498266157403
average fraction cut among pre-empted: 0.614047746779
mean number of active channels, Method I: 0.497912893675
mean number of active channels, Method II: 0.497885497885
[root@localhost ~/156]#
[root@localhost ~/156]# python Cell2.py 6 2 4 100000
fraction of pre-empted calls: 0.205598199554
average fraction cut among pre-empted: 0.412846668117
mean number of active channels, Method I: 2.37979082673
mean number of active channels, Method II: 2.37945337945
[root@localhost ~/156]#
[root@localhost ~/156]# python Cell2.py 6 1 4 100000
fraction of pre-empted calls: 0.468714913239
average fraction cut among pre-empted: 0.513897338516
mean number of active channels, Method I: 3.17985370163
mean number of active channels, Method II: 3.17973917974
[root@localhost ~/156]#
[root@localhost ~/156]# python Cell2.py 16 22 15 100000
fraction of pre-empted calls: 0.0
average fraction cut among pre-empted: SimPy: No observations  for mean
None
mean number of active channels, Method I: 0.72712225164
mean number of active channels, Method II: 0.727332727333
[root@localhost ~/156]#
[root@localhost ~/156]# python Cell2.py 16 12 15 100000
fraction of pre-empted calls: 0.0
average fraction cut among pre-empted: SimPy: No observations  for mean
None
mean number of active channels, Method I: 1.33305734281
mean number of active channels, Method II: 1.33365033365
[root@localhost ~/156]#
[root@localhost ~/156]# python Cell2.py 16 9 8 100000
fraction of pre-empted calls: 0.000398390203847
average fraction cut among pre-empted: 0.269166716707
mean number of active channels, Method I: 1.77671591091
mean number of active channels, Method II: 1.77666277666
[root@localhost ~/156]#
[root@localhost ~/156]# python Cell2.py 16 6 6 100000
fraction of pre-empted calls: 0.0351164104918
average fraction cut among pre-empted: 0.319426416557
mean number of active channels, Method I: 2.57360221291
mean number of active channels, Method II: 2.57375157375