arXiv subjects

Amos Brocco

Publications and source records attributed to Amos Brocco.

6 recordsLinked to original sources

arXiv2026

A Dual-CRDT Architecture for Decentralized Trust Governance and Evolution

While CRDTs provide decentralized replication and eventual consistency, Byzantine-resilient deployments require mechanisms for deciding which updates should be trusted and therefore contribute to the reconstructed state. In practice, the trust relationships underlying these decisions may evolve over time as participants join or leave, identities change, and governance rules are revised. However, the information used to make trust decisions is typically managed outside the replicated state itself. This paper introduces a dual-CRDT architecture composed of a \emph{Trust CRDT} and a \emph{Data CRDT}. The Trust CRDT stores and evolves governance information, while the Data CRDT is reconstructed according to the trust configuration derived from the Trust CRDT. Governance therefore becomes replicated state rather than an externally managed artifact. Building upon deterministic reconstruction and Byzantine trust filtering, the proposed model allows trust relationships and governance rules to evolve through ordinary CRDT updates. The resulting architecture provides a recursive governance model in which governance rules determine their own future evolution while simultaneously governing application data. The approach is implemented as a prototype on top of Melda and melda-sec and should be viewed as an initial exploration of decentralized trust governance and evolution for Byzantine-resilient CRDT systems.

cs.DC↗

arXiv2026

Decoupling Trust in Byzantine CRDTs: Fine-grained Post-Compromise Handling without Breaking Causality

Conflict-free Replicated Data Types (CRDTs) provide strong eventual consistency without coordination, but classical approaches assume benign participants. In Byzantine settings, convergence is typically enforced through agreement on update validity, often relying on identity-based filtering. However, such approaches struggle in post-compromise scenarios, where a previously correct participant becomes malicious: retroactive exclusion of its updates may break causal dependencies and invalidate subsequent computations. In this paper, we decouple identity-based trust from content-based trust and introduce a fine-grained trust model that combines both dimensions. Building on deterministic reconstruction, our approach allows replicas to preserve previously accepted updates while enabling selective inclusion or exclusion based on both the originating identity (e.g., public keys) and the semantics of individual updates. Trust decisions can incorporate application-level policies, enabling precise control over the impact of each update on the system state. Our approach preserves causal consistency and enables robust and flexible handling of both Byzantine and faulty behavior in decentralized CRDT systems.

cs.DC↗

arXiv2026

A Composable CRDT Layer for Byzantine-Resilient Deterministic Reconstruction

Conflict-free Replicated Data Types (CRDTs) ensure Strong Eventual Consistency without coordination, but typically assume benign participants and rely on validation or exclusion to handle Byzantine behavior. We address this problem through deterministic state reconstruction: rather than deciding which updates are admissible, all accepted updates are incorporated, while only a subset contributes to the reconstructed state. We instantiate this approach in Melda, a non-intrusive delta-state CRDT for JSON documents, and show that its reconstruction model guarantees convergence even under arbitrary update injection: adversarial updates are either structurally rejected or treated as inputs to the reconstruction process. We formalize this model and prove that replicas deriving state from the same set of updates cannot diverge despite equivocation, omission, or message reordering. We further show that authentication, authorization, and confidentiality can be layered without affecting convergence. Overall, this approach suggests that Byzantine tolerance can be achieved by decoupling update propagation from state derivation, allowing agreement on updates to be handled independently by external dissemination or consensus mechanisms.

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arXiv2025

Introducing Support for Move Operations in Melda CRDT

In this paper, we present an extension to Melda (a library which implements a general purpose delta state JSON CRDT) to support move operations. This enhancement relies on minimal changes to the underlying logic of the data structure, has virtually no runtime overhead and zero storage overhead compared to the original version of the library, ensuring simplicity while addressing multiple use cases. Although concurrent reordering of the elements in a list was already supported in the original version of the library, moving objects between different containers lead to undesired outcomes, namely duplicate entries. To address this problem we revisited the original approach and introduced the necessary changes to support for relocating elements within a JSON structure. We detail those changes and provide some examples.

cs.PL↗

arXiv2024

BinomialHash: A Constant Time, Minimal Memory Consistent Hash Algorithm

Consistent hashing is a technique for distributing data across a network of nodes in a way that minimizes reorganization when nodes join or leave the network. It is extensively applied in modern distributed systems as a fundamental mechanism for routing and data placement. Similarly, distributed storage systems rely on consistent hashing for scalable and fault-tolerant data partitioning. This paper introduces BinomialHash, a consistent hashing algorithm that executes in constant time and requires minimal memory. We provide a detailed explanation of the algorithm, present a pseudo-code implementation, and formally establish its strong theoretical guarantees. Finally, we compare its performance against state-of-the-art constant-time consistent hashing algorithms, demonstrating that our solution is both highly competitive and effective, while also validating the theoretical boundaries.

cs.DC↗

arXiv2024

MementoHash: A Stateful, Minimal Memory, Best Performing Consistent Hash Algorithm

Consistent hashing is used in distributed systems and networking applications to spread data evenly and efficiently across a cluster of nodes. In this paper, we present MementoHash, a novel consistent hashing algorithm that eliminates known limitations of state-of-the-art algorithms while keeping optimal performance and minimal memory usage. We describe the algorithm in detail, provide a pseudo-code implementation, and formally establish its solid theoretical guarantees. To measure the efficacy of MementoHash, we compare its performance, in terms of memory usage and lookup time, to that of state-of-the-art algorithms, namely, AnchorHash, DxHash, and JumpHash. Unlike JumpHash, MementoHash can handle random failures. Moreover, MementoHash does not require fixing the overall capacity of the cluster (as AnchorHash and DxHash do), allowing it to scale indefinitely. The number of removed nodes affects the performance of all the considered algorithms. Therefore, we conduct experiments considering three different scenarios: stable (no removed nodes), one-shot removals (90% of the nodes removed at once), and incremental removals. We report experimental results that averaged a varying number of nodes from ten to one million. Results indicate that our algorithm shows optimal lookup performance and minimal memory usage in its best-case scenario. It behaves better than AnchorHash and DxHash in its average-case scenario and at least as well as those two algorithms in its worst-case scenario. However, the worst-case scenario for MementoHash occurs when more than 70% of the nodes fail, which describes a unlikely scenario. Therefore, MementoHash shows the best performance during the regular life cycle of a cluster.

cs.DC↗