Developed in PACS Lab to ease the process of deployment and testing of our benchmarking workload to AWS Lambda.
Project description
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.
Installation
pip install pacsltk
Examples
You can use the package as simple as the short code snippet below:
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
pandoc -s README.md -o README.rst
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