A cost-effective, high-bandwidth storage architecture

Author:

Gibson Garth A.1,Nagle David F.2,Amiri Khalil2,Butler Jeff2,Chang Fay W.1,Gobioff Howard1,Hardin Charles2,Riedel Erik2,Rochberg David1,Zelenka Jim1

Affiliation:

1. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA

2. Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA

Abstract

This paper describes the Network-Attached Secure Disk (NASD) storage architecture, prototype implementations oj NASD drives, array management for our architecture, and three, filesystems built on our prototype. NASD provides scalable storage bandwidth without the cost of servers used primarily, for transferring data from peripheral networks (e.g. SCSI) to client networks (e.g. ethernet). Increasing datuset sizes, new attachment technologies, the convergence of peripheral and interprocessor switched networks, and the increased availability of on-drive transistors motivate and enable this new architecture. NASD is based on four main principles: direct transfer to clients, secure interfaces via cryptographic support, asynchronous non-critical-path oversight, and variably-sized data objects. Measurements of our prototype system show that these services can be cost-effectively integrated into a next generation disk drive ASK. End-to-end measurements of our prototype drive andfilesysterns suggest that NASD cun support conventional distributed filesystems without performance degradation. More importantly, we show scaluble bandwidth for NASD-specialized filesystems. Using a parallel data mining application, NASD drives deliver u linear scaling of 6.2 MB/s per clientdrive pair, tested with up to eight pairs in our lab.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

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2. DeltaFS: A Scalable No-Ground -Truth Filesystem For Massively -Parallel Computing;INT CONF HIGH PERFOR;2021

3. Interfacing HDF5 with a scalable object‐centric storage system on hierarchical storage;Concurrency and Computation: Practice and Experience;2020-03-09

4. Tuning Object-Centric Data Management Systems for Large Scale Scientific Applications;2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC);2019-12

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