perfsim.traffic

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perfsim.traffic#

Module contents#

Submodules#

perfsim.traffic.load_generator module#

class perfsim.traffic.load_generator.LoadGenerator(name, simulation, notify_observers_on_event=True)[source]#

Bases: Observable

A LoadGenerator is an object responsible for not only generating a given list of traffic objects traffic_prototypes on a given simulation.cluster, but to control the state, events and time of the entire simulation.

Parameters:

  • name (str) – Name of the LoadGenerator instance.

  • simulation (Simulation) – The Simulation object where traffic belongs to.

  • notify_observers_on_event (bool)

threads_dict: Dict[str, ReplicaThread]#

All ReplicaThread instances in the simulation (in dict)

arrivals: pd.DataFrame#

Stores requests arrival times

before_traffic_start: str#

The event that is being triggered before traffic starts.

before_generate_threads: str#

The event that is being triggered

before_requests_start: str#

The event that is being triggered before request is generated.

after_requests_start: str#

The event that is being triggered at the end of initiate_next_batch_of_requests in being triggered.

before_exec_time_estimation: str#

The event that is being triggered before estimating threads execution time.

before_executing_threads: str#

The event that is being triggered before start running threads.

after_completing_load_generation: str#

The event that is being triggered after executing all threads/requests of this load generator.

after_next_batch_arrival_time_calculation: str#

The event that is being triggered after next batch arrival time is being calculated.

before_generate_request_threads: str#

The event that is being triggered before generating a thread for a request (within a subchain).

after_generate_request_threads: str#

The event that is being triggered after generating a thread for a request (within a subchain).

after_transmission_estimation: str#

The event that is being triggered after transmission completion time is being estimated.

after_estimating_time_of_next_event: str#

The event that is being triggered after next event time is being estimated.

before_transmit_requests_in_network: str#

The event that is being triggered before transmitting packets

after_transmit_requests_in_network_and_load_balancing_threads: str#

The event that is being triggered after transmitting requests and load balancing threads on all hosts

before_request_created: str#

The event that is being triggered after transmitting requests and load balancing threads on all hosts

name: str#

Name of the LoadGenerator instance.

sim: Simulation#

The Simulation object where traffic belongs to.

requests: list[Request]#

All Request instances in the simulation

threads: list[ReplicaThread]#

All ReplicaThread instances in the simulation

latencies: pd.DataFrame#

Stores requests latencies

property total_requests_count#
property merged_arrival_table#
register_events()[source]#

This is for performance optimization purposes. Instead of generating strings for each event, we can register the event_names as attributes, then we send the event_name as reference, instead of copying the string every time. This (slightly) improves performance, specially because we are calling the notify_observers method several times for each request during the simulation.

Returns:

execute_traffic()[source]#

This is the main function responsible to start the traffic in the cluster

Param:

debug: Enable/disable debugging mode

Return type:

[<class ‘perfsim.service_chain.replica_thread.ReplicaThread’>]

property completed_requests: int#

Return number of all completed requests

get_latencies_grouped_by_sfc()[source]#

Return list of latencies for all requests of the service chain with the given name

Return type:

SimulationScenarioResultDict

plot_latencies(save_dir=None, marker='o', show_numbers=None, moving_average=False, save_values=False, show=True)[source]#
Parameters:

show (bool)

property last_transmission_id#
property next_trans_completion_times#
property requests_ready_for_thread_generation#
property next_batch_arrival_time#
merge_arrival_tables()[source]#

perfsim.traffic.request module#

class perfsim.traffic.request.Request(request_id, iteration_id, id_in_iteration, load_generator, traffic_prototype, scm, arrival_time=0)[source]#

Bases: Observable

Request class is used to represent a real request in a service chain. Note that a “node” here means a tuple of (subchain_id, microservice_endpoint_function).

Parameters:
before_init_next_microservices: str#
after_init_next_microservices: str#
before_finalizing_subchain: str#
before_concluding_request: str#
before_init_transmission: str#
after_init_transmission: str#
on_init_transmission: str#
before_finish_transmission: str#
after_finish_transmission: str#
load_generator: LoadGenerator#

The load generator that generated this request

traffic_prototype: TrafficPrototype#

The traffic prototype in which this request is being created from

scm: ServiceChainManager#
latency: float#
id: str#

Request ID

iteration_id: int#

ID of the iteration that this request was generated in

id_in_iteration: int#

ID of the request within the iteration that this request was generated in

status: str#

The request status (IN_PROGRESS, COMPLETED, TIMED_OUT)

register_events()[source]#

This is for performance optimization purposes. Instead of generating strings for each event, we can register the event_names as attributes, then we send the event_name as reference, instead of copying the string every time. This (slightly) improves performance, specially because we are calling the notify_observers method several times for each request during the simulation.

Returns:

finalize_subchain(subchain_id)[source]#
Parameters:

subchain_id (int)

set_next_nodes_and_replicas(next_nodes)[source]#
Parameters:

next_nodes (List[Tuple[int, MicroserviceEndpointFunction]])

init_transmission(node_in_alt_graph)[source]#
Parameters:

node_in_alt_graph (Tuple[int, MicroserviceEndpointFunction])

Return type:

int

finish_transmission_by_subchain_id(subchain_id)[source]#
Parameters:

subchain_id (int)

finish_transmission(node_in_alt_graph)[source]#
Parameters:

node_in_alt_graph (Tuple)

Return type:

None

init_next_microservices(subchain_id)[source]#
Parameters:

subchain_id (int)

Return type:

List[Tuple[int, MicroserviceReplica]]

get_node_names()[source]#
static get_next_nodes_names(next_nodes)[source]#
Parameters:

next_nodes (List[Tuple[int, MicroserviceEndpointFunction]])

get_current_replicas_names()[source]#
get_next_replicas_names()[source]#
get_current_replicas_host_names()[source]#
get_next_replicas_host_names()[source]#
conclude(time)[source]#
Parameters:

time (Union[int, float])

property compute_times#
property trans_times#
property trans_exact_times#
property trans_init_times#
property current_nodes#
property current_replicas_in_nodes#
property subchains_status: List[None | str]#
property next_replicas_in_nodes#
property next_nodes#
property trans_deltatimes#

perfsim.traffic.transmission module#

class perfsim.traffic.transmission.Transmission(id, payload_size, src_replica, dst_replica, subchain_id_request_pair, recalculate_bandwidths_in_links=False)[source]#

Bases: Observable

Parameters:
register_events()[source]#

This is for performance optimization purposes. Instead of generating strings for each event, we can register the event_names as attributes, then we send the event_name as reference, instead of copying the string every time. This (slightly) improves performance, specially because we are calling the notify_observers method several times for each request during the simulation.

Returns:

Get the data of a link.

Parameters:

link

Returns:

static recalc_bw_considering_err(bandwidth, error)[source]#
Parameters:

  • bandwidth (float)

  • error (float)

calculate_requested_bw()[source]#

Calculate the requested bandwidth of this transmission.

Returns:

transmit(duration)[source]#
Parameters:

duration (float)

calculate_transmission_time()[source]#
Return type:

float

finish()[source]#
property source_nic#
property dest_nic#
property current_bw#
set_current_bw(new_bw, edge_data)[source]#
Parameters:

  • new_bw (Union[int, float])

  • edge_data (Dict[str, Any])

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