Py-tracy evaluates the inter-arrivals times between logs applying different filters based on the user input.
The logs comes from the Google's traces clusterdata-2011-2 (available on Google Storage) for job_events and task_events.
Py-tracy was created furing the course of Computer Systems Performance Evaluation @ Politecnico of Milan.
This repository contains the code used to analyse the data provided by Google.

For this project we used
IPython
Spark
Bokeh
I wrote a tutorial on how to link IPython with Spark on OS X and Linux.

Up to now the code is available only as ipynb, so you need to open the source file with IPython.
The traces contain data starting from the 1st of May 2011 19 EDT until the 30th of May 2011.
Important: many parameters may be set to filter the traces, in any case (except time parameters) you can type None and no filter will be applied.

j_eval_day(None, 2, 3600)

will evaluate traces with any event_type.

Open the inter_arrival_jobs file and run all the cells till the Usage section.
After that you can call the function to start the computation.

# j_eval_day(event_type, day, granularity)
j_eval_day(0, 2, 3600)

• event_type : the type of event, e.g. 0 correspond to SUBMIT
• day : day you wish to evaluate, e.g. 2 correspond to the 2nd of May from 0:00 until 23:59
• granularity : interval at which you want to perfom the evaluation (expressed in seconds!), e.g. 3600 will evaluate the model at intervals of 1 hour

# j_eval_days(event_type, init_day, finish_day, granularity)
j_eval_days(0, 2, 5, 3600)

• event_type : the type of event, e.g. 0 correspond to SUBMIT
• init_day : day you wish to start the evaluation, e.g. 2 correspond to the 2nd of May 0:00
• finish_day : day you wish to end the evaluation, e.g. 5 correspond to the 5th of May 23:59
• granularity : interval at which you want to perfom the evaluation (expressed in seconds!), e.g. 3600 will evaluate the model at intervals of 1 hour

If you wish you could also select periods of time which are not day or days

# j_eval_time_window(event_type, init_time, finish_time, granularity)
j_eval_time_window(0, 600, 86400, 3600)

• event_type : the type of event, e.g. 0 correspond to SUBMIT
• init_time : time you wish to start the evaluation (in seconds!), e.g. 600 correspond to the 600 seconds after the start of the collection of the traces (May 2nd 19:00 ETD)
• finish_time : time you wish to end the evaluation (in seconds!)
• granularity : interval at which you want to perfom the evaluation (expressed in seconds!), e.g. 3600 will evaluate the model at intervals of 1 hour

Open the inter_arrival_tasks file and run all the cells till the Usage section.
After that you can call the function to start the computation.

# t_eval_day(event_type, machine_ID, job_ID, day, granularity)
t_eval_day(0, 2, 3600)

• event_type : the type of event, e.g. 0 correspond to SUBMIT
• machine_ID: ID of the machine running the task
• job_ID: ID of the jobs the task belongs to
• day : day you wish to evaluate, e.g. 2 correspond to the 2nd of May from 0:00 until 23:59
• granularity : interval at which you want to perfom the evaluation (expressed in seconds!), e.g. 3600 will evaluate the model at intervals of 1 hour

# t_eval_days(event_type, machine_ID, job_ID, init_day, finish_day, granularity)
t_eval_days(0, 1, 1, 2, 5, 3600)

• event_type : the type of event, e.g. 0 correspond to SUBMIT
• machine_ID: ID of the machine running the task
• job_ID: ID of the jobs the task belongs to
• init_day : day you wish to start the evaluation, e.g. 2 correspond to the 2nd of May 0:00
• finish_day : day you wish to end the evaluation, e.g. 5 correspond to the 5th of May 23:59
• granularity : interval at which you want to perfom the evaluation (expressed in seconds!), e.g. 3600 will evaluate the model at intervals of 1 hour

If you wish you could also select periods of time which are not day or days

# t_eval_time_window(event_type, machine_ID, job_ID, init_time, finish_time, granularity)
t_eval_time_window(0, 1, 1, 600, 86400, 3600)

• event_type : the type of event, e.g. 0 correspond to SUBMIT
• machine_ID: ID of the machine running the task
• job_ID: ID of the jobs the task belongs to
• init_time : time you wish to start the evaluation (in seconds!), e.g. 600 correspond to the 600 seconds after the start of the collection of the traces (May 2nd 19:00 ETD)
• finish_time : time you wish to end the evaluation (in seconds!)
• granularity : interval at which you want to perfom the evaluation (expressed in seconds!), e.g. 3600 will evaluate the model at intervals of 1 hour

Py-tracy outputs a csv file containing the processed traces in the format described below, and it also outputs some graphs showing the computed inter-arrival values.
Those graphs represents:

• Mean
• Variance
• Median
• Standart Variation

init_time,finish_time,timestamp_1,timestamp_2,inter_time

• init_time and finish_time: those are time values considered with respect to the granularity, for example if we consider a granularity of 1 hour (3600 seconds) over the 2nd of May, the values will be 0:00 am - 1:00 am, 1:00 am - 2:00 am and so on (in seconds).
• timestamp_1 and timestamp_2: those represent 2 subsequent logs in the traces, used to evaluate the inter-arrival rate.
• inter time: the computed inter-arrival time

The name of the csv in written in this format

jobs_intervals_event-type_init-time_finish-time_@granularity.csv

init_time,finish_time,timestamp_1,timestamp_2,inter_time

• init_time and finish_time: those are time values considered with respect to the granularity, for example if we consider a granularity of 1 hour (3600 seconds) over the 2nd of May, the values will be 0:00 am - 1:00 am, 1:00 am - 2:00 am and so on (in seconds).
• timestamp_1 and timestamp_2: those represent 2 subsequent logs in the traces, used to evaluate the inter-arrival rate.
• inter time: the computed inter-arrival time

The name of the csv in written in this format

tasks_intervals_event-type_machine-ID_job-ID_init-time_finish-time_@granularity.csv