qsim: A discrete-event simulator for queuing systems

Requirements

• Python 3.4+
• scipy
• numpy

The simulator uses multiprocessing to speed up the simulation, however that means it requires a considerable amount of memory. Modify the MULTIPROCESSING_CPUS accordingly.

Usage

    simulator.py [-h] {optimal_cores,tail} [--datapoints DATAPOINTS] [--iterations ITERATIONS]
               [--cpus CPUS] [--max-cpus MAX_CPUS] [--interval INTERVAL]
               [--utilization UTILIZATION] [--sla SLA] [--discipline {fifo|sjf}]
               [--service_time [SERVICE_TIME_DISTRIBUTION [Dist args ...]]]
               [--interarrival_time [INTERARRIVAL_TIME_DISTRIBUTION [Dist args ...]]]
               [--quanta PS_QUANTA_US] [--warmup WARMUP_MS] [--dump DUMPFILE]
               [--rps MRPS] [--mean-service-time MST]

        Supported distributions are:
            constant <val>
            exponential <scale>
            normal <mean> <stddev>
            lognormal <mean> <stddev>
            gpareto <scale> <c>
            uniform <a> <b>
            bimodal <mean1> <percent1> <mean2> <percent2>
            All distribution parameters should be in microseconds

        Supported disciplines:
            fifo
            sjf
            ps
            psjf
            nwc (Non-work-conserving scheduler)
    ----------------------------------------------------------------

    positional arguments:
  {optimal_cores,tail}  Number of rate points to simulate, default=10

optional arguments:
  -h, --help            show this help message and exit
  --datapoints DATAPOINTS
                        Number of rate points to simulate, default=10
  --iterations ITERATIONS
                        Number of simulation iterations, default=1
  --cpus CPUS           Starting number of CPUs to simulate, default=10
  --max-cpus MAX_CPUS   Maximum number of CPUs to simulate, default=16
  --interval INTERVAL   Length of simulation interval in seconds, default=1s
  --utilization UTIL    Number of processor to fully utilize, default=8
  --sla SLA             Service Level Objective for p999 latency, default=200us
  --quanta QUANTA       Processor-sharing/PSJF time quanta in microseconds, default=5us
  --warmup WARMUP       The latency results for this duration will be ignored at the start of the experiments, default=10ms
  --rps RPS             Used in optimal core mode to find the optimal number of core for that given rps, default=0MRPS
  --mean-service-time   MEAN_SERVICE_TIME
                        Mean service time is used to automatically determine the max load the system can tolerate, default=10us
  --dump DUMP           You can specify a file for the simulator to dump all the request latencies
  --discipline DISC     Queuing discipline, default=fifo
  --service_time        SERVICE_TIME_DISTRIBUTION [parameters ...]
  --interarrival_time   INTERARRIVAL_TIME_DISTRIBUTION [parameters ...]

The only positional argument is mode. Select between optimal_cores,tail. The tail mode reports the latency percentiles for the given rates. The optimal_cores mode tries to find the optimal number of servers (cores) for a given rate and SLA combination.

Example

$ python3 main.py tail --interarrival_time exponential 200 --service_time exponential 10 --utilization 16 --discipline sjf --max-cpus 16 --datapoints 30 --cpus 16 --mean-service-time 10

Simulates a queuing system with 16 servers, single queue with SJF scheduling the arrivals are exponentially distributed with lambda of 200us and service times have a lambda of 10us. The simulation will try 30 different rates from near zero to the point where system utilization becomes 1. You must specify the mean service time parameter explicitly to allow the proper definitions of data points for interarrival times.

The result will be something like this:

Util, Requests/s, avg, p50, p90, p95, p99, p999, Cores
0.53, 53333.33, 10.02, 7.00,23.09,29.93,45.78,67.47, 0.0
1.07, 106666.67, 10.00, 6.91,23.03,29.94,46.07,69.44, 0.0
1.60, 160000.00, 9.97, 6.91,22.90,29.83,45.66,69.54, 0.0
2.13, 213333.33, 10.00, 6.94,22.91,29.91,45.77,68.90, 0.0
2.67, 266666.67, 10.00, 6.93,23.06,29.95,46.00,68.99, 0.0
3.20, 320000.00, 9.98, 6.92,22.96,29.88,46.21,69.59, 0.0
3.73, 373333.33, 9.99, 6.92,22.99,29.94,46.05,69.15, 0.0
4.27, 426666.67, 9.99, 6.93,22.99,29.88,45.94,69.12, 0.0
4.80, 480000.00, 9.99, 6.92,23.05,29.94,46.03,68.45, 0.0
5.33, 533333.33, 10.01, 6.94,23.03,29.91,46.05,69.32, 0.0
5.87, 586666.67, 10.00, 6.92,23.06,29.92,46.00,68.59, 0.0
6.40, 640000.00, 10.00, 6.94,23.03,29.89,46.10,68.71, 0.0
6.93, 693333.33, 10.00, 6.94,22.99,29.94,46.12,69.68, 0.0
7.47, 746666.67, 10.00, 6.94,23.00,29.89,46.00,69.13, 0.0
8.00, 800000.00, 10.00, 6.94,23.02,29.92,45.95,69.24, 0.0
8.53, 853333.33, 10.02, 6.95,23.02,29.98,46.12,68.86, 0.0
9.07, 906666.67, 10.02, 6.95,23.04,30.02,46.31,69.35, 0.0
9.60, 960000.00, 10.05, 6.98,23.13,30.09,46.17,69.16, 0.0
10.13, 1013333.33, 10.09, 6.98,23.23,30.27,46.63,69.86, 0.0
10.67, 1066666.67, 10.16, 7.03,23.37,30.41,46.78,69.91, 0.0
11.20, 1120000.00, 10.21, 7.08,23.45,30.52,46.98,71.08, 0.0
11.73, 1173333.33, 10.28, 7.12,23.62,30.81,47.37,71.86, 0.0
12.27, 1226666.67, 10.40, 7.18,23.85,31.19,48.33,73.16, 0.0
12.80, 1280000.00, 10.60, 7.28,24.30,31.86,49.83,78.68, 0.0
13.33, 1333333.33, 10.83, 7.38,24.76,32.59,52.04,84.65, 0.0
13.87, 1386666.67, 11.16, 7.49,25.42,33.74,55.22,98.30, 0.0
14.40, 1440000.00, 11.68, 7.65,26.36,35.49,61.05,125.16, 0.0
14.93, 1493333.33, 12.62, 7.85,27.90,38.54,72.79,190.18, 0.0
15.47, 1546666.67, 14.76, 8.09,30.33,43.83,101.35,437.06, 0.0
16.00, 1600000.00, 197.97, 8.48,35.61,57.21,231.96,12421.20, 0.0

Author

Erfan Sharafzadeh (e.[last name]@jhu.edu)