Predictive Model-Based Thermal Management for Network Applications

As processor power density has increased at an alarming rate, chip/core temperature control becomes critical in satisfying given thermal constraint and avoiding hotspots. Unlike "run-to-finish" applications whose temperature will simply rise to saturation point and then stabilize, network applications do periodic packet processing, which causes temperature to rise and fall over time. However, no existing studies have focused on characterizing the temperature variation for periodic tasks. We envision that volatile thermal behavior has to be well understood in order to optimize thermal management. In this paper, we first build a novel predictive thermal model for generic periodic tasks running on a single core. This model can dynamically derive the core temperature at any time quickly and accurately. To verify the model, we use both Hot Spot simulator and a real Linux machine to run six network applications chosen from Net Bench. Then, we propose an online model update strategy using on-chip thermal sensors, which can effectively correct incidental errors by adjusting model parameters "on-the-fly". Finally, by combining the thermal model and the online update, we design, implement and evaluate a predictive model-based thermal management scheme on an Intel Xeon E5335 core for network applications based on the Stop & Go technique. Compared with two other alternatives, our scheme achieves lower temperature, higher throughput, no thermal constraint violation, and negligible overhead cost.