Fan-speed-aware scheduling of data intensive jobs
Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design
Achieving autonomous power management using reinforcement learning
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Cooperative boosting: needy versus greedy power management
Proceedings of the 40th Annual International Symposium on Computer Architecture
Temperature aware thread block scheduling in GPGPUs
Proceedings of the 50th Annual Design Automation Conference
CoMETC: Coordinated management of energy/thermal/cooling in servers
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Correcting vibration-induced performance degradation in enterprise servers
ACM SIGMETRICS Performance Evaluation Review
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In this work we propose a joint energy, thermal and cooling management technique (JETC) that significantly reduces per server cooling and memory energy costs. Our analysis shows that decoupling the optimization of cooling energy of CPU & memory and the optimization of memory energy leads to suboptimal solutions due to thermal dependencies between CPU and memory and non-linearity in cooling energy. This motivates us to develop a holistic solution that integrates the energy, thermal and cooling management to maximize energy savings with negligible performance hit. JETC considers thermal and power states of CPU & memory, thermal coupling between them and fan speed to arrive at energy efficient decisions. It has CPU and memory actuators to implement its decisions. The memory actuator reduces the energy of memory by performing cooling aware clustering of memory pages to a subset of memory modules. The CPU actuator saves cooling energy by reducing the hot spots between and within the CPU sockets and minimizing the effects of thermal coupling. Our experimental results show that employing JETC results in 50.7% average energy reduction in cooling and memory subsystems with less than 0.3% performance overhead.