Reducing the Energy Footprint of a Distributed Consensus Algorithm

Pâris, J.-F.; Long, Darrell D. E.

doi:10.1109/edcc.2015.25

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Many companies have found that Raft is easy-toimplement and provides good performance. Pâris et al reduced energy footprint of Raft [34]. Gramoli et al put forward a fast consensus-based dynamic reconfigurations method which can speedup a primary-based rolling upgrade [18].…”

Section: Related Workmentioning

confidence: 99%

Cost-effective BlackWater Raft on Highly Unreliable Nodes at Scale Out

Xu¹,

Du²,

Zhang³

et al. 2022

Preprint

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The Raft algorithm maintains strong consistency across data replicas in Cloud. This algorithm divides nodes into leaders and followers, to satisfy read/write requests spanning geo-diverse sites. With the increase of workload, Raft shall provide scale-out performance in proportion. However, traditional scale-out techniques encounter bottlenecks in Raft, and when the provisioned sites exhaust local resources, the performance loss will grow exponentially. To provide scalability in Raft, this paper proposes a cost-effective mechanism for elastic auto-scaling in Raft, called BlackWater-Raft or BW-Raft. BW-Raft extends the original Raft with the following abstractions: (1) secretary nodes that take over expensive log synchronization operations from the leader, relaxing the performance constraints on locks. (2) massive low cost observer nodes that handle reads only, improving throughput for typical data intensive services. These abstractions are stateless, allowing elastic scale-out on unreliable yet cheap spot instances. In theory, we demonstrate that BW-Raft can maintain Raft's strong consistency guarantees when scaling out, processing a 50X increase in the number of nodes compared to the original Raft. We have prototyped the BW-Raft on key-value services and evaluated it with many state-of-the-arts on Amazon EC2 and Alibaba Cloud. Our results show that within the same budget, BW-Raft's resource footprint increments are 5-7X smaller than Multi-Raft, and 2X better than original Raft. Using spot instances, BW-Raft can reduces costs by 84.5% compared to Multi-Raft. In the real world experiments, BW-Raft improves goodput of the 95th-percentile SLO by 9.4X, thus serving as an alternative for services scaling out with strong consistency.

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Section: Related Workmentioning

confidence: 99%

Cost-effective BlackWater Raft on Highly Unreliable Nodes at Scale Out

Xu¹,

Du²,

Zhang³

et al. 2022

Preprint

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“…The significant additional contribution of our approach lies in incorporating BFT. Markov analysis is performed in [22] as well for a more specific setup to evaluate the protection against data loss and availability for two main improvements on Raft consensus algorithm. The approach presented in this study can be adopted for similar consensus algorithms.…”

Section: Introductionmentioning

confidence: 99%

Availability Evaluation of IoT Systems with Byzantine Fault-Tolerance for Mission-Critical Applications

Marcozzi

Gemikonakli

Gemikonaklı

et al. 2023

Preprint

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“…We believe that reducing the energy footprint of the RAFT protocol is essential to ensure its continuous success. We addressed the issue in a previous paper [14] and proposed two approaches for reducing the number of servers involved in a Raft cluster without significantly altering the Raft algorithm [14]. Our first proposal consisted of adjusting Raft quorums in a way that would allow updates to proceed with as few as two servers while requiring a larger quorum for electing a new leader.…”

Section: Introductionmentioning

confidence: 99%

Pirogue, a lighter dynamic version of the Raft distributed consensus algorithm

Pâris

Long

2015

2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC)

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Raft is a new distributed consensus algorithm that is easier to understand than the older Paxos algorithm. Raft's major drawback is its high energy footprint: as it relies on static quorums for deciding when it can commit updates, it requires five participants to protect against two simultaneous failures. We propose to reduce this footprint by replacing the static quorums that Raft currently uses by quorums that vary according to the number of currently available participants. We present first a modified dynamic-linear voting protocol that disables singleserver updates and show that a Raft cluster with four participants managed by this protocol would be almost as available as a conventional Raft cluster with five participants and always tolerate the irrecoverable failure of any single participant without any data loss. In addition, we show a Raft cluster with three participants and a witness managed by an unmodified dynamic-linear voting protocol would be more available than a conventional Raft cluster with five participants and could still tolerate most irrecoverable failures of any single participant while maintaining recoverability.

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Reducing the Energy Footprint of a Distributed Consensus Algorithm

Cited by 7 publications

References 14 publications

Cost-effective BlackWater Raft on Highly Unreliable Nodes at Scale Out

Cost-effective BlackWater Raft on Highly Unreliable Nodes at Scale Out

Availability Evaluation of IoT Systems with Byzantine Fault-Tolerance for Mission-Critical Applications

Pirogue, a lighter dynamic version of the Raft distributed consensus algorithm

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