ConSil Jeff Chase Duke University

Collaborators Justin Moore –received PhD in April, en route to Google. Did this research. Wrote this paper. Named the system. –Something to do with “Get Smart” (?) Did not send me slides… Partha Ranganathan (HP) has led this work.

Context: Dynamic Thermal Management for Data Centers CRAC Rack Temperature Scale (C) Heat build-ups

Goals ConSil is part of a larger system to analyze data center thermals and manage heat proactively. –Temperature-aware workload placement –“Smart cooling” Preliminary conclusion: it is practical to reduce total energy by about 15% under “typical” conditions. –Your mileage may vary. Other goals: –Reduce capital cost with “common case” cooling system. Allow cluster to “burst”, but stop short of meltdown. –Improve long-term reliability and availability –Better data center design

“Green” Workload Placement Making Scheduling "Cool": Temperature-Aware Resource Assignment in Data Centers by Justin Moore, J. Chase, P. Ranganathan, and R. Sharma. In the 2005 USENIX Annual Technical Conference, April USENIX Annual Technical Conference Place workload intelligently to promote an even temperature distribution, given the “thermal topology” of the data center.

The Subproblem that Consil Solves How hot is point (x, y, z) in your data center? –Placement policies need a thermal map Option 1: install new instrumentation –Tradeoff $$$ vs. granularity Option 2: use built-in sensors –But: how to derive the inlet temperatures? If we can do that, then we can obtain a precise and accurate thermal map with low instrumentation cost.

Thermal Instrumentation Inlet Heat (Q inlet ) Heat Sources (Q workload ) Temperature Sensors (Q observed ) Observed: ▲= f(▲, ▲) Learn: ▲= g(▲, ▲)

ConSil in Context Workload measures

Learning a Model Learn statistical model for Y from m samples of YmYm s mn..s m2 s m1 smsm Y2Y2 s 2n..s 22 s 21 s2s2 Y1Y1 s 1n..s 12 s 11 s1s1 YXnXn..X2X2 X1X1 Attributes Samples

First Cut: Neural Nets Infer ambient temperature from an input sample: –Last N workload measure samples (epoch E) –Internal temperature sensor readings Use off-the-shelf FANN library Some static (SWAG) structural choices: –Four layers of neurons Input  hidden  hidden  output –Neurons use FANN sigmoid transform function –Train the net using FANN back-propagation to set input weights on each neuron.

Experiments with Consil Collected data for 12 servers in a data center. Pick servers whose inlet temperatures are known –i.e., they have a sensor near them 45 hours of data collected under active/varying load –Two server models (HP DL360 G3, Dell 1425) –CPU data: 1 second granularities –temperature data: 5 or 30 second granularities CPU utilization only –CPU uses 80% of power (225/275 watts peak) 266 Lines of FANN code

Methodology FFCV –Divide observations into fifths –Train on one fifth, test on four –Do it for each fifth –Compute SSE Output: CDFs of errors Sensitivity study –Training time –Accuracy

ConSil: Accuracy Accurate inference using workload and onboard data –75% of inferred values are within 1C of actual value

Sensitivity Time-to-train –Most significant: FFCV sub-experiment –Training time is highly data-dependent –Epoch length –Number of sensor/workload epochs Accuracy (SSE) –Most significant: FFCV sub-experiment –Indicates not enough variation in behavior –Coarse granularity (more history) improves

ConSil in Context Workload measures

Predicting Thermal Effects Model relationship using machine learning –Inputs: Workload data, AC settings, fan speeds –Output: Predicted thermal map –Learns from observations during normal operation –FANN neural net library –Active “burn in” may speed learning Weatherman: Automated, Online, and Predictive Thermal Mapping and Management for Data Centers by Justin Moore, J. Chase, and P. Ranganathan. Third IEEE International Conference on Autonomic Computing, June 2006.Third IEEE International Conference on Autonomic Computing

Accurate inferences using workload and AC data –Data from validated Flovent CFD models –92% of predicted values are within 1.0C of actual value Weatherman: Accuracy

Summary/Conclusion Machine learning is a useful tool for “autonomic” self- optimization. –Sense and respond –Optimizing control loops based on learned models Neural nets don’t always suck. –Initial results suggest they work well here. –Maybe we can do better. Need good baseline datasets for training/validation. –Variance –History

Why “ConSil”? Cone of Silence –“Mask out” unwanted signals

The maximum number of training iterations was set to $10^5$. Each neural net contained one input, one output, and two hidden layers. Each hidden layer contained twice the number of neurons as the input layer; varying the number of recent epochs we use as input, we vary the number of workload epochs~---~parameter $B$~---~and internal sensor epochs~---~parameter $C$~---~independently. Using general full factorial design analysis, we can identify which parameters have a significant effect when changed, and for which parameters we can simply select a ``reasonable'' value.