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Brief Announcement

DOI: 10.4230/LIPIcs.DISC.2025.45

Brief Announcement: DAGs for the Masses

Authors: Michael Anoprenko, Andrei Tonkikh, Alexander Spiegelman, and Petr Kuznetsov

Published in: LIPIcs, Volume 356, 39th International Symposium on Distributed Computing (DISC 2025)

Abstract

A recent approach to building consensus protocols on top of Directed Acyclic Graphs (DAGs) shows much promise due to its simplicity and stable throughput. However, as each node in the DAG typically includes a linear number of references to the nodes in the previous round, prior DAG protocols only scale up to a certain point when the overhead of maintaining the graph becomes the bottleneck. To enable large-scale deployments of DAG-based protocols, we propose a sparse DAG architecture, where each node includes only a constant number of references to random nodes in the previous round. We present a sparse version of Bullshark - one of the most prominent DAG-based consensus protocols - and demonstrate its improved scalability. Remarkably, unlike other protocols that use random sampling to reduce communication complexity, we manage to avoid sacrificing resilience: the protocol can tolerate up to f < n/3 Byzantine faults (where n is the number of participants), same as its less scalable deterministic counterpart. The proposed "sparse" methodology can be applied to any protocol that maintains disseminated system updates and causal relations between them in a graph-like structure. Our simulations show that the considerable reduction of transmitted metadata in sparse DAGs results in more efficient network utilization and better scalability.

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Michael Anoprenko, Andrei Tonkikh, Alexander Spiegelman, and Petr Kuznetsov. Brief Announcement: DAGs for the Masses. In 39th International Symposium on Distributed Computing (DISC 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 356, pp. 45:1-45:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{anoprenko_et_al:LIPIcs.DISC.2025.45,
  author =	{Anoprenko, Michael and Tonkikh, Andrei and Spiegelman, Alexander and Kuznetsov, Petr},
  title =	{{Brief Announcement: DAGs for the Masses}},
  booktitle =	{39th International Symposium on Distributed Computing (DISC 2025)},
  pages =	{45:1--45:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-402-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{356},
  editor =	{Kowalski, Dariusz R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2025.45},
  URN =		{urn:nbn:de:0030-drops-248617},
  doi =		{10.4230/LIPIcs.DISC.2025.45},
  annote =	{Keywords: Consensus, Atomic Broadcast, Byzantine Fault Tolerance, DAGs, Scalability, Sampling}
}

Document

Brief Announcement

DOI: 10.4230/LIPIcs.DISC.2023.35

Brief Announcement: BatchBoost: Universal Batching for Concurrent Data Structures

Authors: Vitaly Aksenov, Michael Anoprenko, Alexander Fedorov, and Michael Spear

Published in: LIPIcs, Volume 281, 37th International Symposium on Distributed Computing (DISC 2023)

Abstract

Batching is a technique that stores multiple keys/values in each node of a data structure. In sequential search data structures, batching reduces latency by reducing the number of cache misses and shortening the chain of pointers to dereference. Applying batching to concurrent data structures is challenging, because it is difficult to maintain the search property and keep contention low in the presence of batching. In this paper, we present a general methodology for leveraging batching in concurrent search data structures, called BatchBoost. BatchBoost builds a search data structure from distinct "data" and "index" layers. The data layer’s purpose is to store a batch of key/value pairs in each of its nodes. The index layer uses an unmodified concurrent search data structure to route operations to a position in the data layer that is "close" to where the corresponding key should exist. The requirements on the index and data layers are low: with minimal effort, we were able to compose three highly scalable concurrent search data structures based on three original data structures as the index layers with a batched version of the Lazy List as the data layer. The resulting BatchBoost data structures provide significant performance improvements over their original counterparts.

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Vitaly Aksenov, Michael Anoprenko, Alexander Fedorov, and Michael Spear. Brief Announcement: BatchBoost: Universal Batching for Concurrent Data Structures. In 37th International Symposium on Distributed Computing (DISC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 281, pp. 35:1-35:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{aksenov_et_al:LIPIcs.DISC.2023.35,
  author =	{Aksenov, Vitaly and Anoprenko, Michael and Fedorov, Alexander and Spear, Michael},
  title =	{{Brief Announcement: BatchBoost: Universal Batching for Concurrent Data Structures}},
  booktitle =	{37th International Symposium on Distributed Computing (DISC 2023)},
  pages =	{35:1--35:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-301-0},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{281},
  editor =	{Oshman, Rotem},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DISC.2023.35},
  URN =		{urn:nbn:de:0030-drops-191612},
  doi =		{10.4230/LIPIcs.DISC.2023.35},
  annote =	{Keywords: Concurrency, Synchronization, Locality}
}

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Brief Announcement: DAGs for the Masses

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Brief Announcement: BatchBoost: Universal Batching for Concurrent Data Structures

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