Introduction to Thermal Management

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• Introduction to Thermal Management
• Naisan Benatar
• Supervisors Prof. Uwe Aickelin Dr. Milena
  Radenkovic

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Outline

• Introduction to WSNs
• Problem Domain
• Solution Outline
• Future Work

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Introduction Who Am I?

• Undergraduate Degree in CS University of
  Nottingham 2002-2005
• Software Engineer Thales Avionics Worked on
  SatCom system mainly for Airbus
• Returned to UoN for PhD Studies in Sept

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Areas of Research

• Wireless Sensor Networks (WSNs)
• Their Applications
• Protocols related to their applications

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What are Wireless Sensor Networks?

• Constructed from inexpensive nodes with a sensor
  (or multiple sensors) and a wireless networking
  device
• Power is a concern limited battery
• Resource Constraints (CPU, Memory)

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Applications of WSNs

• Many Sizes and Applications
• Military
• Conservation
• Urban Monitoring (Traffic)

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What Im currently Interested In

• Thermal Challenges in Data Centres
• Lots of Heat producing objects (mainly
  servers,storage etc )
• Few cooling units (Active Air Conditioning Units)
• Varying Loads mean varying temperatures
• Each piece of equipment must not exceed its
  operating conditions (Usually around 75 degrees
  Celsius)
• Very heterogeneous environment many
  manufacturers. Changes in data centers are not
  uncommon (Often a 3 year upgrade cycle)

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Current Solution

• Very Brute Force
• All Coolers set so no piece of equipment goes
  above a set level (approx 75 degrees Celsius)
• Not very Intelligent
• Wastes Energy
• Does not adapt to varying workloads
• No one system that handles all aspects of the
  thermal environment

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How can it improve?

• Using a wireless sensor network composed of
  numerous nodes equipped with temperature sensor.
• Gather Data from nodes
• Make Decisions.
• Be flexible, resilient and quick to respond or
  predictive.

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Problems with this approach

• Lots of data Possibly thousands of nodes in a
  large DC.
• Unordered/Unstructured data
• Heterogeneous Environment

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Potential Algorithm Inspiration

• Bio-inspired Artificial Endocrine System
• Traditional WSN approaches

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Bio Inspired Approach

• The human endocrine system regulates processes in
  the body
• E.g. Rate of breakdown of stored energy to
  useable form controlled by 2 hormones (insulin
  glucagon)
• Can something similar be used to regulate cooling
  requirements in a DC?

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Traditional Solutions

• Directed Diffusion is a data centric protocol
  sometimes used with WSNs.
• Uses named data pairs, along with interests to
  specify where data should be sent in the network.
• Most useful for applications where all data ends
  up in a single place for processing not what we
  have here

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How to test A model

• Need to build a Model (or 2) to simulate the
  various algorithms
• Networking Model
• Each node in the network will be individually
  modelled -gt Agent Based Modelling
• Thermal Model
• Thermal Environment will alter all nodes
  gradually -gtSystem Dynamics?

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Software

• Using Anylogic (V 6.4)
• Allows Combinations of Modelling Paradigms
• Uses java for behaviour specification
• Provides good foundation frameworks

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How to get some confidence in the model?

• Performed experiments with real equipment
  measured temperature changes at varying levels of
  load
• Used as basis of thermal model

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Measuring Performance

• Metrics for Measurement of Performance of an
  algorithm
• Packets sent
• Time to respond to peaks
• Energy Usage of cooling system

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Real Life
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Simulation
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Comparisons

• Not very similar!
• Many Reasons for this
• Experimental Data not perfectly controlled
• Many Simplifications in model
• An ongoing topic in modelling research

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Future Work Short term

• Improve Accuracy of model (CFD for thermal?).
• Comparisons of different approaches to solving
  the problem.

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Future Work Long term

• Possible Experiments with small data centres
• More Intelligence - predicative load based on
  prior knowledge (e.g. Weekly peaks)

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Questions?