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Escalating application demand and the end of Dennard scaling have put energy management at the center of cloud operations. Because of the huge cost and long lead time of provisioning new data centers, operators want to squeeze as much use out of existing data centers as possible, often limited by power provisioning fixed at the time of construction. Workload demand spikes and the inherent variability of renewable energy, as well as increased power unreliability from extreme weather events and natural disasters, make the data center power management problem even more challenging. We believe it is time to build a power control plane to provide fine-grained observability and control over data center power to operators. Our goal is to help make data centers substantially more elastic with respect to dynamic changes in energy sources and application needs, while still providing good performance to applications. There are many use cases for cloud power control, including increased power oversubscription and use of green energy, resilience to power failures, large-scale power demand response, and improved energy efficiency. 

Power is becoming a scarce resource for data centers, raising the need for power adaptive system design—the ability to dynamically change power consumption—to match available power. Storage makes up an increasing fraction of total data center power consumption. As such, it holds great potential to contribute to data center power adaptivity. To this end, we conduct a measurement study of power control mechanisms on a variety of modern data center storage devices. By changing device power states and shaping IO, we achieve a power dynamic range of up to 59.4% of the device’s maximum operating power. We also study power control trade-offs, including throughput and latency. Based on our observations, we construct storage device power-throughput models and discuss the implications on power adaptive storage system design. 

Tectonic is Facebook’s exabyte-scale distributed filesystem. Tectonic consolidates large tenants that previously used service-specific systems into general multitenant filesystem instances that achieve performance comparable to the specialized systems. The exabyte-scale consolidated instances enable better resource utilization, simpler services, and less operational complexity than our previous approach. This paper describes Tectonic’s design, explaining how it achieves scalability, supports multitenancy, and allows tenants to specialize operations to optimize for diverse workloads. The paper also presents insights from designing, deploying, and operating Tectonic.