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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. 

Kernel task scheduling is important for application performance, adaptability to new hardware, and complex user requirements. However, developing, testing, and debugging new scheduling algorithms in Linux, the most widely used cloud operating system, is slow and difficult. We developed Enoki, a framework for high velocity development of Linux kernel schedulers. Enoki schedulers are written in safe Rust, and the system supports live upgrade of new scheduling policies into the kernel, userspace debugging, and bidirectional communication with applications. A scheduler implemented with Enoki achieved near identical performance (within 1% on average) to the default Linux scheduler CFS on a wide range of benchmarks. Enoki is also able to support a range of research schedulers, specifically the Shinjuku scheduler, a locality aware scheduler, and the Arachne core arbiter, with good performance. 

The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path. Datacenters are already a significant fraction of worldwide electricity use, with application demand scaling at a rapid rate. We argue that substantial reductions in the carbon intensity of datacenter computing are possible with a software-centric approach: by making energy and carbon visible to application developers on a fine-grained basis, by modifying system APIs to make it possible to make informed trade offs between performance and carbon emissions, and by raising the level of application programming to allow for flexible use of more energy efficient means of compute and storage. We also lay out a research agenda for systems software to reduce the carbon footprint of datacenter computing. 

Climate change is a pressing threat to planetary well-being that can be addressed only by rapid near-term actions across all sectors. Yet, the cloud computing sector, with its increasingly large carbon footprint, has initiated only modest efforts to reduce emissions to date; its main approach today relies on cloud providers sourcing renewable energy from a limited global pool of options. We investigate how to accelerate cloud computing's efforts. Our approach tackles carbon reduction from a software standpoint by gradually integrating carbon awareness into the cloud abstraction. Specifically, we identify key bottlenecks to software-driven cloud carbon reduction, including (1) the lack of visibility and disaggregated control between cloud providers and users over infrastructure and applications, (2) the immense overhead presently incurred by application developers to implement carbon-aware application optimizations, and (3) the increasing complexity of carbon-aware resource management due to renewable energy variability and growing hardware heterogeneity. To overcome these barriers, we propose an agile approach that federates the responsibility and tools to achieve carbon awareness across different cloud stakeholders. As a key first step, we advocate leveraging the role of application operators in managing large-scale cloud deployments and integrating carbon efficiency metrics into their cloud usage workflow. We discuss various techniques to help operators reduce carbon emissions, such as carbon budgets, service-level visibility into emissions, and configurable-yet-centralized resource management optimizations. 

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