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Recent technological advancements have enabled a generation of Ultra-Low Latency (ULL) SSDs that blurs the performance gap between primary and secondary storage devices. However, their power consumption characteristics are largely unknown. In addition, ULL performance in a block device is expected to put extra pressure on operating system components, significantly affecting energy efficiency of the entire system. In this work, we empirically study overall energy efficiency using a real ULL storage device, Optane SSD, a power meter, and a wide range of IO workload behaviors. We present a comparative analysis by laying out several critical observations related to idle vs. active behavior, read vs. write behavior, energy proportionality, impact on system software, as well as impact on overall energy efficiency. To the best of our knowledge, this is the first published study of a ULL SSD's impact on the system's overall power consumption, which can hopefully lead to future energy-efficient designs. 

The growing energy consumption of data centers is a compelling global problem and effective server consolidation is at the heart of energy efficient cloud data centers. A variant of bin packing can be used to model the server consolidation problem, where the constraints are multidimensional and heterogeneous vectors rather than scalars and the goal is to satisfy the requested resource allocation using the minimum number physical servers. Since bin packing is NP-hard, we rely on heuristics for practical solutions. Variations of First Fit Decreasing (FFD) based heuristics have been shown to be effective both in theory and practice for the one dimensional homogeneous case. However, the multidimensional and heterogeneous aspects of the server consolidation problem make it more complicated, requiring additional research to adapt FFD to the server consolidation problem. In this paper, we present a new FFD-based server consolidation technique using a Monte Carlo method and Shannon entropy, which considers resource bottlenecks and dynamically adjusts to variance in the utilization of different resources. The proposed heuristic outperforms existing techniques in all scenarios, achieving within 2-5% of optimal on average for medium to high variance in resource utilization, and within 10% worse than optimal on average for all scenarios.