Low-Redundancy Synchronization

Updated 14 December 2025

Low-redundancy synchronization is a design paradigm that minimizes unnecessary communication, computation, and storage while ensuring correctness in diverse systems.
It employs minimal primitives—such as atomic read, xor, decrement, and fetch-and-increment—to achieve scalable concurrency and efficient resource use.
Applications span multicore, sensor, database, and biological networks, with innovations reducing overhead and meeting tight performance and energy constraints.

Low-redundancy synchronization refers to the design and implementation of synchronization mechanisms that minimize redundant communication, computational, or storage overhead while maintaining correctness, efficiency, and scalability. In contemporary systems—ranging from multicore architectures and distributed databases to wireless sensor networks and biological neural networks—low-redundancy synchronization plays a central role in resource-efficient coordination and data consistency.

1. Foundational Principles and Motivation

Low-redundancy synchronization leverages the minimal required set of coordination primitives, communication, or state to achieve correct concurrent or distributed operation. Architecturally, this implies:

Using the smallest necessary instruction set or protocol features—avoiding complex universal primitives like compare-and-swap (CAS) when simpler ones suffice.
Reducing message traffic, redundant state transfers, or unnecessary broadcast events in distributed and wireless systems.
Algorithmic designs that minimize the number, size, and duration of synchronization steps while providing provable correctness and progress guarantees.

The rationale is multifold: hardware designers avoid area and coherence overhead, distributed systems curb bandwidth consumption and tail latency, and wireless and sensor networks economize on battery and spectrum use (Gelashvili et al., 2017, Xu et al., 27 Nov 2025, Coladon et al., 2015).

2. Minimal Synchronization Primitives in Shared-Memory Systems

Ellen, Gelashvili, Shavit, and Zhu formalized low-redundancy synchronization at the hardware instruction set level, showing that the collection {atomic read, xor, decrement, fetch-and-increment}—each with consensus number at most two—suffices to implement any concurrent object via universal construction. In particular:

The "linearizable Log (History) object" uses fetch-and-increment for unique log slot assignment, and xor/decrement on b-bit array locations for claim/invalidate protocols, eliminating the need for CAS (Gelashvili et al., 2017).
Readers are wait-free and writers are lock-free. The design is provably linearizable via monotonicity invariants on log slots.
On modern x86 hardware, such low-redundancy constructs match the performance (e.g., throughput, latency) of classical CAS-based algorithms with negligible difference, suggesting that future multicore and PIM architectures need only support lightweight atomic instructions.

The consensus number analysis further clarifies that, although individual primitives may have low power, their judicious combination yields universal computability.

3. Low-Redundancy Synchronization in Distributed and Wireless Networks

Redundancy in broadcast protocols and multi-hop networks arises from simultaneous transmissions, message collisions, and over-suppression. The Trickle algorithm, a staple of low-power mesh and sensor networks, traditionally employs a global redundancy constant K—the threshold of prior transmissions before further broadcasts are suppressed.

Recent models emphasize per-node adaptation:

Assigning a locally computed redundancy constant $K_i$ to each node, proportional to its neighborhood degree, significantly improves load fairness and reduces overall transmissions (Coladon et al., 2015).
Emulation and analytical modeling (binomial distributions with unsynchronized intervals) confirm that per-node $K_i$ can cut message variance by up to 80% and average transmission count by over 10%, all at constant CPU and memory overhead.

These principles generalize to wireless body-worn synchronization for sports applications, where packet-level redundancy and latency are critical:

The Enhanced ShockBurst (ESB) protocol leverages minimal headers, disables CRC checks (trading ~40 μs latency savings for <5% higher error rates), and dynamically adjusts retransmission and bitrate parameters to achieve sub-millisecond synchronization across sensor nodes (Krull et al., 8 Sep 2025).
Deterministic broadcast schemes, small payloads, and careful parameterization allow for precise triggering and phase alignment required in high-frequency biosignal acquisition.

4. Redundancy-Optimal Protocols in File and Database Synchronization

File synchronization at low edit or Hamming distance and distributed database state reconciliation present canonical settings for low-redundancy design:

For files differing by $d$ Hamming edits, syndrome-based error correcting code approaches achieve the $H(d/n)n$ entropy bound per file—the minimum possible communication—by sending compact parity checks (Chuklin, 2011).
For synchronization under deletions (and insertions), multi-layer VT codes and improved partitioning allow one-way and interactive protocols to approach information-theoretic optimum, with communication scaling as $O(k\,\log n)$ for $k$ edits (Abroshan et al., 2017, Haolun et al., 7 Dec 2025).
Distributed graph coloring constructions further lower redundancy for unbounded edit bursts and in incremental synchronization, tailoring syndrome bits to expected edit profiles (Li et al., 3 Dec 2025).

For geo-distributed databases, low-redundancy focuses on network and protocol-level optimization:

GeoCoCo employs latency-aware group rescheduling, exploiting natural clustering and triangle inequality violations in WAN topology to minimize the round makespan of synchronization (Xu et al., 27 Nov 2025).
Task-preserving filtering eliminates “white data” (stale or duplicate updates) before costly WAN transmission, with empirical WAN savings up to 40% and system throughput gains of 14% at negligible local overhead.
Hierarchical epoch-based transmission and filtering protocols maintain strong consistency despite aggressive redundancy elimination.

5. State-based CRDTs and Redundancy-Minimizing Reconciliation

CRDT synchronization, especially in state-based or delta-based forms, must avoid the propagation of redundant state or deltas:

ConflictSync, built on irredundant join decompositions and rateless IBLT reconciliation, reduces state synchronization to efficient set reconciliation, using Bloom filters to prefilter elements and rateless decoders to transmit only true differences (Gomes et al., 2 May 2025).
The protocol rigorously matches binary-entropy lower bounds and achieves up to an 18× reduction in total transferred bytes across a range of similarity levels.

Similarly, join-irreducible delta-CRDT algorithms optimize periodic anti-entropy rounds:

Two inefficiencies identified—back-propagation and redundant reception—are mitigated by tracking delta origins and extracting only minimal decompositions per sync (Enes et al., 2018).
Experimental benchmarks demonstrate a 94% reduction in memory and bandwidth consumption relative to classic state or delta-based synchronization, with further reduction of per-update computational overhead.

6. Biological Synchronization: Noise Suppression via Minimal Redundancy

In neural systems, synchronizing decision and learning circuits composed of few redundant pathways (low M) is shown to robustly suppress noise provided coupling strength (κ) is sufficient:

Steady-state variance bounds relate mean error to the inverse of redundancy: halving M requires doubling κ for fixed error (Bouvrie et al., 2010).
Network Laplacian spectrum (λ₂) quantifies the synchronizing power of topological connections; experimental and simulation data confirm the theoretical trade-off predictions for moderate M (20–100).
This suggests that biological systems exploit topology and coupling rather than brute redundancy alone for error control.

7. Theoretical Limits, Trade-offs, and Implications

Across all domains, the fundamental lower bound for redundancy—whether in instruction sets, coding, CRDT deltas, or wireless protocols—is dictated by combinatorial and entropy arguments:

Synchronization cost per edit is $\Omega(\log\,\ell+\log\,q)$ for code-based storage, $H(\alpha)n$ for Hamming-like files, and $O(n\beta\log(1/\beta))$ or $k\log n$ for deletion/insertion models (Rouayheb et al., 2014, Abroshan et al., 2017, Haolun et al., 7 Dec 2025).
Hybrid and incremental schemes exploit prior knowledge of error prevalence to further reduce mean redundancy at the expense of worst-case performance (Li et al., 3 Dec 2025).
Architectural guidance is clear: minimal atomic primitives, fairness-aware broadcast suppression, irredundant decomposition, and multi-layer protocol design universally lower redundancy without sacrificing correctness or performance.

A plausible implication is that ongoing advances in hardware, protocol, and algorithmic design will increasingly favor dynamic, topology-aware, and information-theoretically optimized synchronization protocols, further reducing the resource footprint of coordination in massive-scale systems across computing, networking, and biological domains.

References:

(Gelashvili et al., 2017, Coladon et al., 2015, Krull et al., 8 Sep 2025, Chuklin, 2011, Abroshan et al., 2017, Haolun et al., 7 Dec 2025, Li et al., 3 Dec 2025, Rouayheb et al., 2014, Xu et al., 27 Nov 2025, Gomes et al., 2 May 2025, Enes et al., 2018, Bouvrie et al., 2010)