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Optimizing the Efficiency of the NCAR-Wyoming Supercomputing Center Facility A

Optimizing the Efficiency of the NCAR-Wyoming Supercomputing Center Facility A Software Perspective. Theophile Nsengimana Collaborator: Ademola Olarinde Mentor: Aaron Andersen August 1, 2014. Project Goals. Work on Software Tools to Automate: Collection of NWSC Building Sensor Data

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Optimizing the Efficiency of the NCAR-Wyoming Supercomputing Center Facility A

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  1. Optimizing the Efficiency of the NCAR-Wyoming Supercomputing Center Facility A Software Perspective Theophile Nsengimana Collaborator: AdemolaOlarinde Mentor: Aaron Andersen August 1, 2014

  2. Project Goals • Work on Software Tools to Automate: • Collection of NWSC Building Sensor Data • Quality Control • Visualization of Key Building Parameters • Work with AdemolaOlarinde: • Software Tools • Requirements • Data Exploration

  3. 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

  4. 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

  5. sMAP Components • sMAP sources • Connect to physical sensors to expose the data they generated to sMAParchiver (repository) via http. • Real-time data.

  6. sMAP components • sMAParchiver • 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.

  7. 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]

  8. 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

  9. Quality Control defday_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. forndtinsamedts: ch_value= self.init_data.pop(ndt) try: if float(ch_values)!= 0: value += float(ch_values) elif: zeroCounter += 1 exceptValueError: 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

  10. Data Analysis & Visualization • Python packages • Matplotlib • Numpy • Main focus • Statistical correlation • Basic Control Charts • Plots

  11. 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.

  12. Control Charts • Whether a particular sensor’s performance is in a statistical control. • E.g., evaluate the performance of Condenser Water pumps.

  13. Plots • Visualize and understand the relationship between sensors’ performance and other factors (or sensors). • E.g., Condenser Water Pumps vs time.

  14. 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.

  15. Future Work • Two possibilities: • Configuring sMAP properly and develop a high level custom application on top of sMAParchiver for NWSC staff to monitor the facility.

  16. 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]

  17. 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

  18. Thank you  Theophile Nsengimana nsengimana.theophile@philander.edu

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