Datacenters often underutilize expensive AI accelerators (GPUs, TPUs, etc). A natural solution is disaggregation, where servers borrow network-attached accelerators on demand. However, current approaches to disaggregation suffer from a semantic translation gap: as computation descends the software stack, critical application knowledge—like model structure or execution phases—is lost. This forces an undesirable choice between low-level, general-purpose systems that are semantically-blind and inefficient, and high-level, single-workload systems that are efficient but not general.

Modern distributed systems rely on consensus protocols to build a fault-tolerant-core upon which they can build applications. Consensus protocols are correct under a specific failure model, where up to f machines can fail. We argue that this f -threshold failure model oversimplifies the real world and limits potential opportunities to optimize for cost or performance. We argue instead for a probabilistic failure model that captures the complex and nuanced nature of faults observed in practice. Probabilistic consensus protocols can explicitly leverage individual machine failure curves and explore side-stepping traditional bottlenecks such as majority quorum intersection, enabling systems that are more reliable, efficient, cost-effective, and sustainable.

Modern applications span multiple clouds to reduce costs, avoid vendor lock-in, and leverage low-availability resources in another cloud. However, standard object stores operate within a single cloud, forcing users to manually manage data placement across clouds, i.e., navigate their diverse APIs and handle heterogeneous costs for network and storage. This is often a complex choice: users must either pay to store objects in a remote cloud, or pay to transfer them over the network based on application access patterns and cloud provider cost offerings. To address this, we present SkyStore, a unified object store that addresses cost-optimal data management across regions and clouds. SkyStore introduces a virtual object and bucket API to hide the complexity of interacting with multiple clouds. At its core, SkyStore has a novel TTL-based data placement policy that dynamically replicates and evicts objects according to application access patterns while optimizing for lower cost. Our evaluation shows that across various workloads, SkyStore reduces the overall cost by up to 6X over academic baselines and commercial alternatives like AWS multi-region buckets. SkyStore also has comparable latency, and its availability and fault tolerance are on par with standard cloud offerings.

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.

We present Dinomo, a novel key-value store for disaggregated persistent memory (DPM). Dinomo is the first key-value store for DPM that simultaneously achieves high common-case performance, scalability, and lightweight online reconfiguration. We observe that previously proposed key-value stores for DPM had architectural limitations that prevent them from achieving all three goals simultaneously. Dinomo uses a novel combination of techniques such as ownership partitioning, disaggregated adaptive caching, selective replication, and lock-free and log-free indexing to achieve these goals. Compared to a state-of-the-art DPM key-value store, Dinomo achieves at least 3.8X better throughput at scale on various workloads and higher scalability, while providing fast reconfiguration.

Modern persistent-memory (PM) file systems perform well in benchmark settings, when the file system is freshly created and empty. But after being aged by usage, as will be the normal mode in practice, their memory-mapped performance degrades significantly. This paper shows that the cause is their inability to use 2MB hugepages to map files when aged, having to use 4KB pages instead and suffering many extra page faults and TLB misses as a result.

Most modern data processing pipelines run on top of a distributed storage layer, and securing the whole system, and the storage layer in particular, against accidental or malicious misuse is crucial to ensuring compliance to rules and regulations. Enforcing data protection and privacy rules, however, stands at odds with the requirement to achieve higher and higher access bandwidths and processing rates in large data processing pipelines. In this work we describe our proposal for the path forward that reconciles the two goals. We call our approach "Software-Defined Data Protection" (SDP). Its premise is simple, yet powerful: decoupling often changing policies from request-level enforcement allows distributed smart storage nodes to implement the latter at line-rate. Existing and future data protection frameworks can be translated to the same hardware interface which allows storage nodes to offload enforcement efficiently both for company-specific rules and regulations, such as GDPR or CCPA. While SDP is a promising approach, there are several remaining challenges to making this vision reality. As we explain in the paper, overcoming these will require collaboration across several domains, including security, databases and specialized hardware design.

We present C rash M onkey and A ce , a set of tools to systematically find crash-consistency bugs in Linux file systems. C rash M onkey is a record-and-replay framework which tests a given workload on the target file system by simulating power-loss crashes while the workload is being executed, and checking if the file system recovers to a correct state after each crash. A ce automatically generates all the workloads to be run on the target file system. We build C rash M onkey and A ce based on a new approach to test file-system crash consistency: bounded black-box crash testing ( B 3 ). B 3 tests the file system in a black-box manner using workloads of file-system operations. Since the space of possible workloads is infinite, B 3 bounds this space based on parameters such as the number of file-system operations or which operations to include, and exhaustively generates workloads within this bounded space. B 3 builds upon insights derived from our study of crash-consistency bugs reported in Linux file systems in the last 5 years. We observed that most reported bugs can be reproduced using small workloads of three or fewer file-system operations on a newly created file system, and that all reported bugs result from crashes after fsync()-related system calls. C rash M onkey and A ce are able to find 24 out of the 26 crash-consistency bugs reported in the last 5 years. Our tools also revealed 10 new crash-consistency bugs in widely used, mature Linux file systems, 7 of which existed in the kernel since 2014. Additionally, our tools found a crash-consistency bug in a verified file system, FSCQ. The new bugs result in severe consequences like broken rename atomicity, loss of persisted files and directories, and data loss.