Metadata-Version: 1.1
Name: pacsltk
Version: 0.1.12
Summary: Developed in PACS Lab to ease the process of deployment and testing of our benchmarking workload to AWS Lambda.
Home-page: https://nima-dev.com
Author: Nima Mahmoudi
Author-email: nma@ieee.org
License: UNKNOWN
Description: PACSLTK (PACS Lambda ToolKit)
        =============================
        
        Developed in PACS Lab to ease the process of deployment and testing of
        our benchmarking workload to AWS Lambda. To see how you can use pacsltk,
        check out the `github
        repository <https://github.com/pacslab/serverless-performance-modeling>`__.
        
        Installation
        ------------
        
        .. code:: sh
        
           pip install pacsltk
        
        Examples
        --------
        
        You can use the package as simple as the short code snippet below:
        
        .. code:: py
        
           from pacsltk import perfmodel
        
           arrival_rate = 100
           warm_service_time = 2
           cold_service_time = 25
           idle_time_before_kill = 10*60
        
           print("arrival_rate:", arrival_rate)
           print("warm_service_time:", warm_service_time)
           print("cold_service_time:", cold_service_time)
           print("idle_time_before_kill:", idle_time_before_kill)
        
           props1, props2 = perfmodel.get_sls_warm_count_dist(arrival_rate, warm_service_time, cold_service_time, idle_time_before_kill)
           perfmodel.print_props(props1)
        
        which produces an output similar to the following:
        
        ::
        
           arrival_rate: 100
           warm_service_time: 2
           cold_service_time: 25
           idle_time_before_kill: 600
        
           Properties:
           ------------------
           avg_server_count: 251.043927
           avg_running_count: 200.148828
           avg_running_warm_count: 199.987058
           avg_idle_count: 51.056869
           cold_prob: 0.000065
           avg_utilization: 0.796622
           avg_resp_time: 2.001488
           rejection_prob: 0.000000
           rejection_rate: 0.000000
           ------------------
        
        Updating README in RST file
        ---------------------------
        
        .. code:: sh
        
           pandoc -s README.md -o README.rst
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
