Path Analysis Module¶
Compositional Performance Analysis Algorithms for Path Latencies
Authors: |
|
---|
Description¶
This module contains methods for the ananlysis of path latencies. It should be imported in scripts that do the analysis.
-
pycpa.path_analysis.
cause_effect_chain
(chain, task_results, details=None, semantics='data-age')[source]¶ computes the data age of the given cause effect chain :param chain: model.EffectChain :param task_results: dict of analysis.TaskResult
-
pycpa.path_analysis.
cause_effect_chain_data_age
(chain, task_results, details=None)[source]¶ computes the data age of the given cause effect chain :param chain: model.EffectChain :param task_results: dict of analysis.TaskResult
-
pycpa.path_analysis.
cause_effect_chain_reaction_time
(chain, task_results, details=None)[source]¶ computes the data age of the given cause effect chain :param chain: model.EffectChain :param task_results: dict of analysis.TaskResult
-
pycpa.path_analysis.
end_to_end_latency
(path, task_results, n=1, task_overhead=0, path_overhead=0, **kwargs)[source]¶ Computes the worst-/best-case e2e latency for n tokens to pass the path. The constant path.overhead is added to the best- and worst-case latencies.
Parameters: - path (model.Path) – the path
- n (integer) – amount of events
- task_overhead (integer) – A constant task_overhead is added once per task to both min and max latency
- path_overhead (integer) – A constant path_overhead is added once per path to both min and max latency
Return type: tuple (best-case latency, worst-case latency)
-
pycpa.path_analysis.
end_to_end_latency_classic
(path, task_results, n=1, injection_rate='max', **kwargs)[source]¶ Computes the worst-/best-case end-to-end latency Assumes that all tasks in the system have successfully been analyzed. Assumes that events enter the path at maximum/minumum rate. The end-to-end latency is the sum of the individual task’s worst-case response times.
This corresponds to Definition 7.3 in [Richter2005].
Parameters: - path (model.Path) – the path
- n (integer) – amount of events
- injection_rate (string 'max' or 'min') – assumed injection rate is maximum or minimum
Return type: tuple (best case latency, worst case latency)
-
pycpa.path_analysis.
end_to_end_latency_improved
(path, task_results, n=1, e_0=0, **kwargs)[source]¶ Performs the path analysis presented in [Schliecker2009recursive], which improves results compared to end_to_end_latency() for n>1 and bursty event models. lat(n)