Tradeoffs between power management and tail latency in warehouse-scale applications

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Chasing Carbon: The Elusive Environmental Footprint of Computing (2020, background): “Recently proposed schedulers optimize infrastructure efﬁciency in terms of total power consumption while balancing performance for latency-critical and batch-processing workloads [79], [80], [89]–[91].”
Improving smartphone user experience by balancing performance and energy with probabilistic QoS guarantee (2016, unknown): “Another very relevant work appears in [32].”
SoftSKU: Optimizing Server Architectures for Microservice Diversity @Scale (2019, background): “There is a growing trend towards microservice implementation models [2–6], wherein a complex application is decomposed into distributed microservices [7–10] that each provide specialized functionality [11], such as HTTP connection termination, key-value serving [12], protocol routing [13,14], or ad ”
Profiling a warehouse-scale computer (2015, background): “These observations motivate several interesting directions for future warehouse-scale computers.”
Heracles: Improving resource efficiency at scale (2015, background): “optimizing various aspects of latency-critical workloads, including energy proportionality [53, 54, 47, 46, 34], networking performance [35, 8], and hardware-acceleration [41, 63, 72].”