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Optimizing the Efficiency of the NCAR-Wyoming Supercomputing Center Facility

ASoftware Perspective

Theophile Nsengimana Collaborator: Ademola Olarinde

Mentor: Aaron Andersen

August 1, 2014

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Project Goals• Work on Software Tools to Automate:

– Collection of NWSC Building Sensor Data

– Quality Control

– Visualization of Key Building Parameters

• Work with Ademola Olarinde:– Software Tools

– Requirements

– Data Exploration

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• Preplanned Method:– Store sensor-based raw data generated by Johnson Controls Inc. into a

data store.– sMAP (Simple Measurement and Actuation Profile) from Berkeley. [1]

• Current Method:– Restructuring raw data into CSV file(s).

Data Collection

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sMAP Overview• An open source modular software designed fundamentally to ease

the collection, storage and retrieval of time series data. • Time series source: archival and real-time from sensors.• Time series can be tagged with metadata

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sMAP Components

• sMAP sources– Connect to physical sensors to expose the data they generated to

sMAP archiver (repository) via http.– Real-time data.

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sMAP components

• sMAP archiver– A high-performance data store – Connects to both relational and time series databases. (Postgres for

metadata storage and Readingdb for time series storage)

• Applications– Make use of data: visualization, computing control optimal

strategies, etc.

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Challenges with sMAP

• General– Documentation– Community support

• Relatable to this project– Couldn’t load Archival data whose time was out of the range

[(time_now – 24 hours), time_now]

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Quality Control

• Proper Formatting– Hourly (or daily, monthly, yearly) Interval as opposed to 15-minutes

interval– Match time format across all generated csv files– Handle missing data– Eliminate irrelevant data– Merge properly formatted csv files into one csv file for faster access

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Quality Controldef day_interval(self): BY = ‘day’ init_dt, init_value = self.init_data.popitem() samedts = self.getSameDatetimes(self.init_data, init_dt, BY) #find datetimes with same day as init_dt value = init_value #the sum of values of ‘samedts’ missingCounter = 0 #missing data zeroCounter = 0 #how many times this sensor has been offline. for ndt in samedts: ch_value = self.init_data.pop(ndt) try:

if float(ch_values) != 0: value += float(ch_values) elif: zeroCounter += 1 except ValueError: missingCounter += 1 #calculate the average if missingCounter == len(samedts): value = ‘M’ elif (zeroCounter == len(samedts)) or (zeroCounter > 0 and (zeroCounter + missingCounter == len(samedts))): value = 0 else: value = value / ( len(samedts) – (missingCounter + zeroCounter)) self.fin_data[self.makedt(init_dt, BY)] = value

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Data Analysis & Visualization

• Python packages– Matplotlib – Numpy

• Main focus– Statistical correlation– Basic Control Charts– Plots

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Statistical Correlation• How a sensor’s performance is related to other various factors.• E.g., Cooling Towers vs Outside Air and Computer Load

Cooling Towers’ correlations against time, wet-bulb, wet-bulb depression, dry-bulb and computer load.

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Control Charts

• Whether a particular sensor’s performance is in a statistical control.

• E.g., evaluate the performance of Condenser Water pumps.

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Plots

• Visualize and understand the relationship between sensors’ performance and other factors (or sensors).

• E.g., Condenser Water Pumps vs time.

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Summary• Development of software tools to facilitate the process of collecting

data from NWSC, performing basic quality control, and analyzing as well as visualizing key parameters from data collected.

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Future Work

• Two possibilities:– Configuring sMAP properly and develop a high level custom

application on top of sMAP archiver for NWSC staff to monitor the facility.

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Future Work

• Two possibilities:– Stick with CSV files and take advantage of Google Fusion

Tables, an experimental yet powerful data visualization web application developed by Google. [2]

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References

[1] sMAP 2.0 documentation http://www.cs.berkeley.edu/~stevedh/smap2/

[2] Google Fusion Table Help Center https://support.google.com/fusiontables/?hl=en

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Thank you

Theophile Nsengimanansengimana.theophile@philander.edu