Live, Runtime Phase Monitoring and Prediction on Real Systems with Application to Dynamic Power Management

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How researchers see this paper

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GPUWattch: enabling energy optimizations in GPGPUs (2013, unknown): “Prior work [14, 37] leveraged memory/compute phase behavior for CPU energy optimizations.”
Improving smartphone user experience by balancing performance and energy with probabilistic QoS guarantee (2016, background): “…of additional power; however, due to ﬁnite energy capacity, it is not typically optimal for mobile platforms to sustain high performance states, and as this paper will demonstrate, providing guarantees on QoS requires a different approach that takes into account the distribution of page load times.”
Tradeoffs between power management and tail latency in warehouse-scale applications (2014, background): “100M instructions [12] – which would contain many independent (i.”
Q-clouds: managing performance interference effects for QoS-aware clouds (2010, background): “In order to address this need, we can consider if and how prior work on phase monitoring and prediction can be applied to our control and modeling approaches [12].”