Treating the Storage Stack Like a Network

Author:

Stefanovici Ioan1,Schroeder Bianca2,O'Shea Greg1,Thereska Eno3

Affiliation:

1. Microsoft Research, Cambridge, United Kingdom

2. University of Toronto, Ontario, Canada

3. Confluent, Imperial College London, CA, United States

Abstract

In a data center, an IO from an application to distributed storage traverses not only the network but also several software stages with diverse functionality. This set of ordered stages is known as the storage or IO stack. Stages include caches, hypervisors, IO schedulers, file systems, and device drivers. Indeed, in a typical data center, the number of these stages is often larger than the number of network hops to the destination. Yet, while packet routing is fundamental to networks, no notion of IO routing exists on the storage stack. The path of an IO to an endpoint is predetermined and hard coded. This forces IO with different needs (e.g., requiring different caching or replica selection) to flow through a one-size-fits-all IO stack structure, resulting in an ossified IO stack. This article proposes sRoute, an architecture that provides a routing abstraction for the storage stack. sRoute comprises a centralized control plane and “sSwitches” on the data plane. The control plane sets the forwarding rules in each sSwitch to route IO requests at runtime based on application-specific policies. A key strength of our architecture is that it works with unmodified applications and Virtual Machines (VMs). This article shows significant benefits of customized IO routing to data center tenants: for example, a factor of 10 for tail IO latency, more than 60% better throughput for a customized replication protocol, a factor of 2 in throughput for customized caching, and enabling live performance debugging in a running system.

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Research challenges in nextgen service orchestration;Future Generation Computer Systems;2019-01

2. From Application to Disk: Tracing I/O Through the Big Data Stack;Lecture Notes in Computer Science;2018

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