1. Increased Reliability Through Failure Predictive Scheduling with Temperature Sensor Feedback Wesley Emeneker CSE 534 Dr. Sandeep Gupta

2. Background High temperatures reduce computer reliability Thermal Scheduling is good but doesn’t look at failure rates of components Estimating failure rates allows tasks to be scheduled on the most reliable components

3. Project Goals Use feedback from sensor networks to predict which components are most reliable Increase reliability of system as seen by tasks through failure predictive scheduling

4. Methodology MTBF half-life between 5-10 degrees C MTBF calculation: Temperature floats to max based on equation modeled after measured values: Combined failure probability for distributed tasks: Failure prediction is a random variable:

5. Results Tasks on lightly loaded systems are more reliable with failure predictive scheduling

6. Tasks on heavily loaded systems do not benefit from predictive scheduling Results

7. Conclusions Reliability scheduling with respect to thermal management can make a significant difference for lightly loaded systems Heavily loaded systems do not see a benefit from reliability scheduling