Scalable State-Machine Replication

Bezerra, Carlos Eduardo; Pedone, Fernando; Renesse, Robbert van

doi:10.1109/dsn.2014.41

Cited by 47 publications

(29 citation statements)

References 35 publications

(55 reference statements)

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“…When the initial coordinator i decides to go onto the slow path, it performs an analog of Paxos Phase 2: it sends an MConsensus message with its proposal and ballot i to a slow quorum that includes itself (line 18) 3 . Following Flexible Paxos [12], the size of the slow quorum is only f + 1, rather than a majority like in classical Paxos.…”

Section: Slow Pathmentioning

confidence: 99%

“…We compared Atlas with EPaxos in detail in §3. 3. There have been two follow-up protocols to EPaxos, Alvin [33] and Caesar [2].…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, Atlas is the first protocol to reduce the size of fast quorums to ⌊ n 2 ⌋ + f . An approach to scaling SMR is to shard the state of the application being replicated and add cross-shard coordination to preserve consistency [3]. Such approaches build on a non-sharded SMR protocol and are hence orthogonal to our proposal: Atlas can be combined with them to scale SMR even further.…”

Section: Related Workmentioning

confidence: 99%

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State-machine replication for planet-scale systems

Enes

Baquero

Rezende

et al. 2020

Proceedings of the Fifteenth European Conference on Computer Systems

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Online applications now routinely replicate their data at multiple sites around the world. In this paper we present Atlas, the first state-machine replication protocol tailored for such planet-scale systems. Atlas does not rely on a distinguished leader, so clients enjoy the same quality of service independently of their geographical locations. Furthermore, clientperceived latency improves as we add sites closer to clients. To achieve this, Atlas minimizes the size of its quorums using an observation that concurrent data center failures are rare. It also processes a high percentage of accesses in a single round trip, even when these conflict. We experimentally demonstrate that Atlas consistently outperforms state-of-the-art protocols in planet-scale scenarios. In particular, Atlas is up to two times faster than Flexible Paxos with identical failure assumptions, and more than doubles the performance of Egalitarian Paxos in the YCSB benchmark.

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Section: Slow Pathmentioning

confidence: 99%

“…We compared Atlas with EPaxos in detail in §3. 3. There have been two follow-up protocols to EPaxos, Alvin [33] and Caesar [2].…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

State-machine replication for planet-scale systems

Enes

Baquero

Rezende

et al. 2020

Proceedings of the Fifteenth European Conference on Computer Systems

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“…Unfortunately, few applications can be optimally partitioned (i.e., all requests fall within a single shard and load is balanced among shards) and so the system must handle requests that span multiple shards. There are two classes of solutions when it comes to handling a multi-shard request: (a) having the involved shards coordinate and execute the request in a distributed fashion (e.g., Spanner [3], S-SMR [4]) and (b) moving the necessary state to one shard that will execute the request locally (e.g., [5]). In either case, one pitfall of sharding is that if the application state is poorly partitioned, overall system performance will most likely decrease, instead of increase, due to the overhead of multi-shard requests.…”

Section: Introductionmentioning

confidence: 99%

Challenges and Pitfalls of Partitioning Blockchains

Fynn

Pedone

2018

2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)

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Blockchain has received much attention in recent years. This immense popularity has raised a number of concerns, scalability of blockchain systems being a common one. In this paper, we seek to understand how Ethereum, a well-established blockchain system, would respond to sharding. Sharding is a prevalent technique to increase the scalability of distributed systems. To understand how sharding would affect Ethereum, we model Ethereum blockchain as a graph and evaluate five methods to partition the graph. We assess methods using three metrics: the balance among shards, the number of transactions that would involve multiple shards, and the amount of data that would be relocated across shards upon repartitioning of the graph.

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“…To provide a scalable design, clustering is the common approach. The nodes of the system are partitioned in multiple groups, each group working independently; the system can grow by simply adding more groups [13,41]. To achieve flexibility and handle churn, join and leave operations should be lightweight and entail small, localized changes to the system [53].…”

Section: Introductionmentioning

confidence: 99%

Atum

Guerraoui

Kermarrec

Pavlovic

et al. 2016

Proceedings of the 17th International Middleware Conference

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This paper presents Atum, a group communication middleware for a large, dynamic, and hostile environment. At the heart of Atum lies the novel concept of volatile groups: small, dynamic groups of nodes, each executing a state machine replication protocol, organized in a flexible overlay. Using volatile groups, Atum scatters faulty nodes evenly among groups, and then masks each individual fault inside its group. To broadcast messages among volatile groups, Atum runs a gossip protocol across the overlay.We report on our synchronous and asynchronous (eventually synchronous) implementations of Atum, as well as on three representative applications that we build on top of it: A publish/subscribe platform, a file sharing service, and a data streaming system. We show that (a) Atum can grow at an exponential rate beyond 1000 nodes and disseminate messages in polylogarithmic time (conveying good scalability); (b) it smoothly copes with 18% of nodes churning every minute; and (c) it is impervious to arbitrary faults, suffering no performance decay despite 5.8% Byzantine nodes in a system of 850 nodes.

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Scalable State-Machine Replication

Cited by 47 publications

References 35 publications

State-machine replication for planet-scale systems

State-machine replication for planet-scale systems

Challenges and Pitfalls of Partitioning Blockchains

Atum

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