perfsim.environment

perfsim.environment#

Module contents#

Submodules#

perfsim.environment.perfsim_server module#

class perfsim.environment.perfsim_server.PerfSimServer(host='0.0.0.0', port=8081, perf_logs=True, log_filename=None, log_format=None, log_level=20, debug=False)[source]#

Bases: object

Initialize the server.

Parameters:

  • host (str) – The host address for the server. Default is ‘0.0.0.0’

  • port (int) – The port for the server. Default is 8081.

  • perf_logs (bool) – Enable/disable the logs related to the execution time of math functions. Default is True.

  • log_filename (str) – The filename for the log file. If None, the log will be written to the console.

  • log_format (str) – The format for the log file. If None, the default format will be used.

  • log_level (int) – The log level based on the logging module. If None, the logging.INFO will be used.

  • debug (bool) – Enable/disable the debug mode. Default is False.

perf_logs: bool#

Enable/disable the performance logging

app: Dash#

The Dash app

server: Flask#

The Flask server

sm: Optional[SimulationScenarioManager]#

The SimulationScenarioManager object, created from the scenario/config

run()[source]#

Run the server.

Return type:

None

Returns:

None

create_scenario_manager()[source]#

Create a scenario manager from the configuration.

Returns:

run_scenario()[source]#

Run the scenario.

Returns:

save_all()[source]#

Save all the results.

Returns:

configure_routes()[source]#

Configure the routes for the server.

Return type:

None

Returns:

None

static make_error_response(error_code, error_message)[source]#

Make an error response.

Parameters:

  • error_code (int) – The error code.

  • error_message (str) – The error message.

Return type:

Response

Returns:

The error response.

make_function_response(func, *args)[source]#

Make a response for a function call.

Parameters:

  • func (Callable) – The function to call.

  • args – The arguments to pass to the function.

Return type:

Response

Returns:

The response of type Response.

property host#

Get the host address.

Returns:

property port#

Get the port number.

Returns:

perfsim.environment.settings module#

perfsim.environment.settings.get_space(size, low, high)[source]#

Get a space of size with low and high values.

Parameters:

  • size

  • low

  • high

Returns:

perfsim.environment.settings.merge_spaces(spaces)[source]#

Merge multiple spaces into one.

Parameters:

spaces

Returns:

class perfsim.environment.settings.Settings[source]#

Bases: object

The Settings class is a class that contains the settings of the simulation.

indices = ['ms_count', 'sc_edge_bytes', 'cfs_period_ns', 'ms_replica_cpu_requests_shares', 'ms_replica_cpu_limits', 'host_cpu_core_count', 'sc_arrival_rate', 'ms_replica_thread_count', 'ms_replica_count', 'host_network_bandwidth', 'host_cpu_clock_rate', 'ms_replica_thread_instructions', 'ms_replica_thread_avg_cpi', 'ms_replica_reserved_network_bandwidth', 'ms_replica_average_cpu_usage', 'ms_replica_thread_average_cpu_utilization', 'ms_replica_thread_weight', 'sched_min_granularity_ns', 'sched_latency_ns', 'load_balancing_threshold', 'nice_0_load']#
columns = ['min', 'max', 'chunks']#
data = [[1, 10, 10], [1000, 1000000, 10], [100, 100, 1], [512, 8192, 16], [512, 8192, 16], [8, 8, 1], [10, 10000, 10], [1, 10, 10], [1, 5, 5], [12500000, 1250000000, 2], [3400000000, 3400000000, 1], [1000000, 10000000000, 10], [1, 1, 1], [0, 0, 1], [10, 100, 10], [1, 1, 1], [1, 1, 1], [2, 2, 1], [6, 6, 1], [0, 0, 1], [1024, 1024, 1]]#
args = min          max  chunks ms_count                                            1           10      10 sc_edge_bytes                                    1000      1000000      10 cfs_period_ns                                     100          100       1 ms_replica_cpu_requests_shares                    512         8192      16 ms_replica_cpu_limits                             512         8192      16 host_cpu_core_count                                 8            8       1 sc_arrival_rate                                    10        10000      10 ms_replica_thread_count                             1           10      10 ms_replica_count                                    1            5       5 host_network_bandwidth                       12500000   1250000000       2 host_cpu_clock_rate                        3400000000   3400000000       1 ms_replica_thread_instructions                1000000  10000000000      10 ms_replica_thread_avg_cpi                           1            1       1 ms_replica_reserved_network_bandwidth               0            0       1 ms_replica_average_cpu_usage                       10          100      10 ms_replica_thread_average_cpu_utilization           1            1       1 ms_replica_thread_weight                            1            1       1 sched_min_granularity_ns                            2            2       1 sched_latency_ns                                    6            6       1 load_balancing_threshold                            0            0       1 nice_0_load                                      1024         1024       1#
chunks = {'cfs_period_ns': array([100]), 'host_cpu_clock_rate': array([3400000000]), 'host_cpu_core_count': array([8]), 'host_network_bandwidth': array([  12500000, 1250000000]), 'load_balancing_threshold': array([0]), 'ms_count': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10]), 'ms_replica_average_cpu_usage': array([ 10,  20,  30,  40,  50,  60,  70,  80,  90, 100]), 'ms_replica_count': array([1, 2, 3, 4, 5]), 'ms_replica_cpu_limits': array([ 512, 1024, 1536, 2048, 2560, 3072, 3584, 4096, 4608, 5120, 5632,        6144, 6656, 7168, 7680, 8192]), 'ms_replica_cpu_requests_shares': array([ 512, 1024, 1536, 2048, 2560, 3072, 3584, 4096, 4608, 5120, 5632,        6144, 6656, 7168, 7680, 8192]), 'ms_replica_reserved_network_bandwidth': array([0]), 'ms_replica_thread_average_cpu_utilization': array([1]), 'ms_replica_thread_avg_cpi': array([1]), 'ms_replica_thread_count': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10]), 'ms_replica_thread_instructions': array([    1000000,  1112000000,  2223000000,  3334000000,  4445000000,         5556000000,  6667000000,  7778000000,  8889000000, 10000000000]), 'ms_replica_thread_weight': array([1]), 'nice_0_load': array([1024]), 'sc_arrival_rate': array([   10,  1120,  2230,  3340,  4450,  5560,  6670,  7780,  8890,        10000]), 'sc_edge_bytes': array([   1000,  112000,  223000,  334000,  445000,  556000,  667000,         778000,  889000, 1000000]), 'sched_latency_ns': array([6]), 'sched_min_granularity_ns': array([2])}#
action_spaces = {'ms_affinity': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0]), array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,        2])), 'ms_replica_count': (array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1]), array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5])), 'ms_replica_cpu_limits': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([16, 16, 16, 16, 16, 16, 16, 16, 16, 16])), 'ms_replica_cpu_requests_shares': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([16, 16, 16, 16, 16, 16, 16, 16, 16, 16]))}#
obs_spaces = {'arrival_rate': (array([0]), array([10])), 'cfs_period_ns': (array([1]), array([1])), 'host_cpu_clock_rate': (array([1]), array([1])), 'host_cpu_core_count': (array([1]), array([1])), 'host_network_bandwidth': (array([1]), array([2])), 'ms_affinity_rules': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0]), array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,        2])), 'ms_replica_average_cpu_usage': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10])), 'ms_replica_count': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5])), 'ms_replica_cpu_limits': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([16, 16, 16, 16, 16, 16, 16, 16, 16, 16])), 'ms_replica_cpu_requests_shares': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([16, 16, 16, 16, 16, 16, 16, 16, 16, 16])), 'ms_replica_net_bandwidth': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])), 'ms_replica_thread_average_cpu_utilization': (array([0]), array([1])), 'ms_replica_thread_avg_cpi': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])), 'ms_replica_thread_count': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10])), 'ms_replica_thread_instructions': (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), array([10, 10, 10, 10, 10, 10, 10, 10, 10, 10])), 'sc_edge': (array([0, 0, 0, 0, 0, 0, 0, 0, 0]), array([10, 10, 10, 10, 10, 10, 10, 10, 10]))}#
action_space = {'high': array([ 2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,         2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,         2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2, 16, 16, 16, 16, 16, 16,        16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,  5,  5,  5,         5,  5,  5,  5,  5,  5,  5]), 'low': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,        1, 1, 1, 1, 1, 1, 1, 1, 1])}#
observation_space = {'high': array([ 1,  1,  1,  2,  5,  5,  5,  5,  5,  5,  5,  5,  5,  5, 16, 16, 16,        16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16,         1,  1,  1,  1,  1,  1,  1,  1,  1,  1, 10, 10, 10, 10, 10, 10, 10,        10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  1,  1,  1,  1,         1,  1,  1,  1,  1,  1, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,  2,         2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,         2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,  2,         2,  2,  2,  2,  2,  2,  2,  2,  2,  2, 10, 10, 10, 10, 10, 10, 10,        10, 10, 10,  1]), 'low': array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,        0, 0, 0, 0, 0, 0, 0, 0])}#
static get_random_chunk(index, min_value=None)[source]#
index = 'nice_0_load'#
row = min       1024 max       1024 chunks       1 Name: nice_0_load, dtype: int64#