Holographic Storage for the Cloud: advances and challenges

Nathanaël Cheriere(Microsoft Research (United Kingdom)), Jiaqi Chu(Microsoft Research (United Kingdom)), Grace Brennan(Microsoft Research (United Kingdom)), Pashmina Cameron(Microsoft (Ireland)), Pedro F. da Costa(King's College London), Jannes Gladrow(Microsoft Research (United Kingdom)), Guilherme Ilunga(Amazon (United Kingdom)), Douglas J. Kelly(Microsoft Research (United Kingdom)), Sarah Lewis(Microsoft Research (United Kingdom)), Joowon Lim(Microsoft Research (United Kingdom)), Giorgio Maltese(Microsoft (Ireland)), Tony Mason(Georgia Institute of Technology), Greg O’Shea(Microsoft Research (United Kingdom)), Soujanya Ponnapalli(Microsoft Research (United Kingdom)), Michael Rudow(Microsoft Research (United Kingdom)), Alan Sanders(Microsoft Research (United Kingdom)), Theano Stavrinos(University of Washington), Xingbo Wu(Microsoft Research (United Kingdom)), Mengyang Yang(Microsoft Research (United Kingdom)), Dushyanth Narayanan(Microsoft Research (United Kingdom)), Benn C. Thomsen(Microsoft Research (United Kingdom)), Antony Rowstron(Microsoft Research (United Kingdom))
ACM Transactions on Storage
December 20, 2024
Cited by 7Open Access
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Abstract

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.


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