Resilient Cloud-based Replication with Low Latency

Eischer M, Distler T (2020)


Publication Language: English

Publication Type: Conference contribution

Publication year: 2020

Publisher: Association for Computing Machinery, Inc

Pages Range: 14-28

Conference Proceedings Title: Middleware 2020 - Proceedings of the 2020 21st International Middleware Conference

ISBN: 9781450381536

URI: https://www4.cs.fau.de/Publications/2020/eischer_20_middleware.pdf

DOI: 10.1145/3423211.3425689

Abstract

Existing approaches to tolerate Byzantine faults in geo-replicated environments require systems to execute complex agreement protocols over wide-area links and consequently are often associated with high response times. In this paper we address this problem with Spider, a resilient replication architecture for geo-distributed systems that leverages the availability characteristics of today's public-cloud infrastructures to minimize complexity and reduce latency. Spider models a system as a collection of loosely coupled replica groups whose members are hosted in different cloud-provided fault domains (i.e., availability zones) of the same geographic region. This structural organization makes it possible to achieve low response times by placing replica groups in close proximity to clients while still enabling the replicas of a group to interact over short-distance links. To handle the inter-group communication necessary for strong consistency Spider uses a reliable group-to-group message channel with first-in-first-out semantics and built-in flow control that significantly simplifies system design.

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How to cite

APA:

Eischer, M., & Distler, T. (2020). Resilient Cloud-based Replication with Low Latency. In Middleware 2020 - Proceedings of the 2020 21st International Middleware Conference (pp. 14-28). Association for Computing Machinery, Inc.

MLA:

Eischer, Michael, and Tobias Distler. "Resilient Cloud-based Replication with Low Latency." Proceedings of the 21st International Middleware Conference, Middleware 2020 Association for Computing Machinery, Inc, 2020. 14-28.

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