publication . Conference object . 2015

Separating the WHEAT from the Chaff: An Empirical Design for Geo-Replicated State Machines

Sousa, Joao; Bessani, Alysson;
Open Access
  • Published: 01 Jan 2015
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract
State machine replication is a fundamental technique for implementing consistent fault-tolerant services. In the last years, several protocols have been proposed for improving the latency of this technique when the replicas are deployed in geographically-dispersed locations. In this work we evaluate some representative optimizations proposed in the literature by implementing them on an open-source state machine replication library and running the experiments in geographically-diverse PlanetLab nodes and Amazon EC2 regions. Interestingly, our results show that some optimizations widely used for improving the latency of geo-replicated state machines do not bring significant benefits, while others - not yet considered in this context - are very effective. Based on this evaluation, we propose WHEAT, a configurable crash and Byzantine fault-tolerant state machine replication library that uses the optimizations we observed as most effective in reducing SMR latency. WHEAT employs novel voting assignment schemes that, by using few additional spare replicas, enables the system to make progress without needing to access a majority of replicas. Our evaluation shows that a WHEAT system deployed in several Amazon EC2 regions presents a median latency up to 56% lower than a "normal" SMR protocol.
Persistent Identifiers
Sustainable Development Goals (SDG) [Beta]
Subjects
free text keywords: Spare part, Finite-state machine, Computer science, Latency (engineering), State machine replication, PlanetLab, Distributed computing, Geo-replication, Fault tolerance, Computer network, business.industry, business, Byzantine fault tolerance
Funded by
EC| SUPERCLOUD
Project
SUPERCLOUD
USER-CENTRIC MANAGEMENT OF SECURITY AND DEPENDABILITY IN CLOUDS OF CLOUDS
  • Funder: European Commission (EC)
  • Project Code: 643964
  • Funding stream: H2020 | RIA
Validated by funder
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