Power management has become important in data centers. Since data center workloads are often dynamic, it is common practice to conserve energy by scaling resources up or down to match the workload. And since data centers often oversubscribe the power delivery infrastructure, operators can add power capping on top of these workload-proportional systems to adjust to available power. We find that this combination leaves performance on the table and provides only a limited power control range. Instead, we argue for power-adaptive systems that attempt to make the best use of the available power budget. To illustrate this approach, we built PASS, a power-adaptive storage system. PASS considers the interactions between different system components, including software and hardware, when making its power management decisions. For example, when under the same power constraint, PASS achieves 3–25× better throughput on filebench workloads than Intel's SPDK storage stack with Google Thunderbolt, a state of the art power capping system.

Holographic Storage is an old idea that has always promised high density and fast random access, but has never been commercially competitive with Hard Disk Drives (HDDs) and Solid State Devices (SSDs). In Project HSD at Microsoft Research we asked the question: “Does holographic storage finally make sense for cloud storage?” This article describes our journey toward answering this question. We achieved 1.8× higher density than the previous state-of-the-art, using commodity components available today and leveraging machine learning to compensate for the noise and distortions introduced by commodity components. This uncovered two new challenges which are the focus of this article: achieving high end-to-end energy efficiency without sacrificing capacity, and spatial multiplexing without mechanical movement. Improving end-to-end energy efficiency requires joint optimization across low-level media parameters and higher-level system parameters that govern background maintenance operations such as read refresh and garbage collection. We developed new physics models of the media; analytic and simulation models of the media access and background media maintenance; and workload-driven optimization to find optimal parameter combinations. These techniques resulted in a 14× improvement over the previous approach for typical workloads without sacrificing capacity. We also designed the first scalable and mechanical movement free spatial multiplexing system for holographic storage. Despite these advances, we conclude that currently, holographic storage is still far from the combination of density, capacity scaling, and energy efficiency needed to compete with the incumbent technologies. We need fundamental advances in the physical media that improve energy efficiency by another 1–2 orders of magnitude without reducing data density. Further advances in optics are also required to achieve spatial multiplexing that is simultaneously scalable, low-loss, and high-density.

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.

Direct-attached accelerators, where application accelerators are directly connected to the datacenter network via a hardware network stack, offer substantial benefits in terms of reduced latency, CPU overhead, and energy use. However, a key challenge is that modern datacenter network stacks are complex, with interleaved protocol layers, network management functions, and virtualization support. To operators, network feature agility, diagnostics, and manageability are often considered just as important as raw performance. By contrast, existing hardware network stacks only support basic protocols and are often difficult to extend since they use fixed processing pipelines. We propose Beehive, a new, open-source FPGA network stack for direct-attached accelerators designed to enable flexible and adaptive construction of complex network functionality in hardware. Application and network protocol elements are modularized as tiles over a network-on-chip substrate. Elements can be added or scaled up/down to match workload characteristics with minimal effort or changes to other elements. Flexible diagnostics and control are integral, with tooling to ensure deadlock safety. Our implementation interoperates with standard Linux TCP and UDP clients, with a 4x improvement in end-to-end RPC tail latency for Linux UDP clients versus a CPU-attached accelerator. Beehive is available at https://github:com/beehive-fpga/beehive

Research on flash devices almost exclusively focuses on conventional SSDs, which expose a block interface. Industry, however, has standardized and is adopting Zoned Namespaces (ZNS) SSDs, which offer a new storage interface that dominates conventional SSDs. Continued research on conventional SSDs is thus a missed opportunity to unlock a step-change improvement in system performance by building on ZNS SSDs. We argue for an immediate and complete shift in research to ZNS SSDs and discuss research directions.