A Layered Architecture for Erasure-Coded Consistent Distributed Storage
Abstract
Motivated by emerging applications to the edge computing paradigm, we introduce a two-layer erasure-coded fault-tolerant distributed storage system offering atomic access for read and write operations. In edge computing, clients interact with an edge-layer of servers that is geographically near; the edge-layer in turn interacts with a back-end layer of servers. The edge-layer provides low latency access and temporary storage for client operations, and uses the back-end layer for persistent storage. Our algorithm, termed Layered Data Storage (LDS) algorithm, offers several features suitable for edge-computing systems, works under asynchronous message-passing environments, supports multiple readers and writers, and can tolerate and crash failures in the two layers having and servers, respectively. We use a class of erasure codes known as regenerating codes for storage of data in the back-end layer. The choice of regenerating codes, instead of popular choices like Reed-Solomon codes, not only optimizes the cost of back-end storage, but also helps in optimizing communication cost of read operations, when the value needs to be recreated all the way from the back-end. The two-layer architecture permits a modular implementation of atomicity and erasure-code protocols; the implementation of erasure-codes is mostly limited to interaction between the two layers. We prove liveness and atomicity of LDS, and also compute performance costs associated with read and write operations. Further, in a multi-object system running independent instances of LDS, where only a small fraction of the objects undergo concurrent accesses at any point during the execution, the overall storage cost is dominated by that of persistent storage in the back-end layer, and is given by .
Cite
@article{arxiv.1703.01286,
title = {A Layered Architecture for Erasure-Coded Consistent Distributed Storage},
author = {Kishori M. Konwar and N. Prakash and Nancy Lynch and Muriel Medard},
journal= {arXiv preprint arXiv:1703.01286},
year = {2017}
}
Comments
To appear in ACM PODC 2017