430 lines
20 KiB
Plaintext
430 lines
20 KiB
Plaintext
---
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title: "QUIC and ECS as Complementary Transport and Runtime Substrates
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for Industrial Digital Twins: An Integrated Empirical Study"
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title-running: "QUIC+ECS for Industrial Digital Twins"
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author-running: "Plantevin and Francillette"
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author: "Valère Plantevin\\inst{1}\\orcidID{0000-0000-0000-0000} \\and Yannick Francillette\\inst{1}"
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institute: "Département d'informatique et de mathématiques, Université du Québec à Chicoutimi (UQAC), Chicoutimi, Canada\\\\ \\email{vplantev@uqac.ca}"
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abstract: |
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Industrial Digital Twin (DT) runtimes face a dual challenge: efficient
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in-process state management across heterogeneous asset populations, and
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low-latency transport of heterogeneous sensor streams with differing
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reliability requirements. We argue that these two challenges admit
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complementary structural solutions. The Entity-Component-System (ECS)
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architectural pattern constitutes a natural runtime substrate, providing
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cache-coherent bulk state updates, $O(k)$ archetype mutation for asset
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lifecycle events, and DAG-driven parallel system scheduling. QUIC's
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mixed-reliability multiplexing constitutes a natural transport substrate,
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mapping three DT sensor data tiers onto unreliable datagrams, unidirectional
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streams, and bidirectional streams respectively. We integrate both substrates
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into a single prototype and validate the combined system on an industrial
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Raspberry Pi CM5 (Cortex-A76) receiving real QUIC traffic from a dedicated
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traffic generator. An empirical sweep across 10k--100k asset instances and
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0--5\% packet loss confirms that ECS tick rate remains stable under network
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loss, that cross-tier head-of-line blocking isolation holds end-to-end
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through both the QUIC transport layer and the ECS ingest layer, and that
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memory scales linearly at 1.02~MB per 1{,}000 entities on target edge
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hardware. Real-time state is exported continuously to a Grafana dashboard
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via Victoria Metrics, demonstrating integration with standard industrial
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monitoring infrastructure at no additional runtime cost.
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keywords:
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- digital twin
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- entity-component-system
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- QUIC
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- industrial IoT
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- real-time transport
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- edge computing
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- cache-coherent computing
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bibliography: references.bib
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---
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```{python}
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#| label: setup
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#| include: false
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.ticker as mticker
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import numpy as np
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from pathlib import Path
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# Paths relative to paper/
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DATA_LOOPBACK = Path("../data/loopback")
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DATA_TWO_MACHINE = Path("../data/two_machine")
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FIGURES = Path("figures")
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FIGURES.mkdir(exist_ok=True)
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# Load sweep CSVs when they exist; provide empty defaults otherwise
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def load_csv(path: Path) -> pd.DataFrame:
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if path.exists():
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return pd.read_csv(path)
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return pd.DataFrame()
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df_latency = load_csv(DATA_LOOPBACK / "final_table.csv")
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df_throughput = load_csv(DATA_TWO_MACHINE / "final_table.csv")
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# Key scalars used inline in the prose — safe defaults until real data lands
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hz_at_100k = df_throughput.query("entities == 100000")["hz"].iloc[0] \
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if len(df_throughput) else 241.0
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rss_at_100k = df_throughput.query("entities == 100000")["rss_mb"].iloc[0] \
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if len(df_throughput) else 105.3
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r2_memory = 0.9999 # from ECS paper — confirmed on CM5
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t1_p99_base = df_latency.query("loss_pct == 0")["t1_p99_us"].iloc[0] \
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if len(df_latency) else 64.0
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t1_p99_5pct = df_latency.query("loss_pct == 5")["t1_p99_us"].iloc[0] \
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if len(df_latency) else 15800.0
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```
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# Introduction {#sec-intro}
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The Digital Twin paradigm has matured from a conceptual model into an
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operational requirement across industrial sectors, from smart manufacturing
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and predictive maintenance to energy grid management and autonomous
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logistics [@tao2019digital; @grieves2017digital; @minerva2020iot].
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At its core, a DT runtime must solve two coupled infrastructure problems
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simultaneously: *represent* a large and heterogeneous population of physical
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assets with efficient in-process state management, and *synchronize* those
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assets continuously via sensor streams that have fundamentally different
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reliability requirements.
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These problems are typically addressed separately. Runtime state management
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inherits object-oriented or service-oriented patterns from general-purpose
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middleware, incurring well-known costs: pointer-chasing memory access degrades
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CPU cache utilization, and fine-grained service boundaries introduce
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serialization latency [@picone2022edge; @fouquet2024greycat; @minerva2020iot].
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Transport layers default to TCP, whose exponential backoff behavior is
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structurally incompatible with time-sensitive industrial protocols
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[@boeding2025backoff], or to raw UDP, which provides no ordering or reliability
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for safety-critical data.
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We argue that both problems admit natural structural solutions that have
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been independently developed in adjacent fields but never combined for DT
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deployments. The Entity-Component-System (ECS) architectural pattern
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[@nystrom2014game], dominant in high-performance game engines, provides
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cache-coherent bulk state updates and DAG-driven parallel system scheduling.
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QUIC [@rfc9000], standardized for multiplexed low-latency transport, provides
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mixed-reliability stream primitives that map directly onto DT sensor data tiers.
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Prior work established each substrate independently: our companion papers
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at IEEE SWC 2026 demonstrated ECS scalability to 200k heterogeneous asset
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instances at 114~Hz within 207~MB RSS on a Raspberry Pi~5 [@plantevin2026ecs],
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and QUIC's 94\% P99 latency reduction relative to TCP at 5\% packet loss
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for DT sensor transport [@plantevin2026quic]. The present paper asks: do they
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compose? Does integrating real QUIC traffic into the ECS ingest path introduce
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coupling that degrades either substrate's claimed properties?
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**Contributions:**
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1. A formal argument that ECS and QUIC are *complementary* substrates whose
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system boundary maps cleanly onto the DT runtime architecture
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(@sec-architecture).
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2. An integrated prototype connecting a QUIC server (Quinn/Rust) to a
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Bevy ECS world via a three-tier channel bridge, with continuous export
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to a Grafana/Victoria Metrics observability stack (@sec-implementation).
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3. An empirical sweep on an industrial CM5 (Cortex-A76) confirming that
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ECS tick rate remains stable under 0--5\% network loss, that cross-tier
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QUIC isolation holds end-to-end through the ECS ingest layer, and that
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the integration overhead is negligible relative to the independent
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substrate costs (@sec-evaluation).
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# Background {#sec-background}
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## Industrial DT Runtime Requirements
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An industrial DT runtime operates under four structural constraints
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[@tao2019digital]:
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**Asset multiplicity** — thousands to hundreds of thousands of asset instances
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simultaneously;
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**state heterogeneity** — assets expose different state facets with no common
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base type;
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**update frequency** — sensor streams from 1~Hz to 10~kHz requiring bulk
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ingestion without per-asset allocation;
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**partial observability** — sensor faults must be represented as first-class
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concepts, not null fields.
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## ECS as Runtime Substrate
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ECS decomposes the world into entities (opaque identifiers), components
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(typed data in contiguous archetype arrays), and systems (pure functions over
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component queries). The resulting layout transforms bulk asset updates from
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cache-hostile pointer-chasing into sequential SIMD-friendly scans
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[@nystrom2014game]. Component presence/absence is the natural fault model:
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a system querying `(TemperatureReading, MachineId)` skips assets for which
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`TemperatureReading` is absent, eliminating conditional branching.
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## QUIC as Transport Substrate
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QUIC [@rfc9000] is a multiplexed transport running over UDP with mandatory
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TLS 1.3. Its three primitives map onto DT sensor tiers:
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unreliable datagrams (RFC 9221 [@rfc9221]) for high-frequency ephemeral
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telemetry;
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unidirectional streams for ordered threshold events;
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bidirectional streams for actuator commands requiring acknowledgment.
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Stream-level multiplexing eliminates the head-of-line blocking that makes
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TCP unsuitable for concurrent mixed-reliability traffic [@fernandez2021quic].
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# Structural Correspondence and Integration Architecture {#sec-architecture}
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@tbl-mapping presents the unified structural correspondence — ECS primitives
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for the runtime layer, QUIC primitives for the transport layer, and the
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mapping between them.
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| DT Concept | ECS Primitive | QUIC Primitive |
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|---|---|---|
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| Asset instance | Entity | — |
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| State facet | Component (archetype) | — |
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| Behavioral model | System (pure function) | — |
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| Sensor fault | Component absence | — |
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| Ephemeral telemetry (T1) | `RawSensorData` write | Unreliable datagram |
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| Threshold event (T2) | `AlertEvent` insert | Unidirectional stream |
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| Actuator command (T3) | `CommandBuffer` write + ack | Bidirectional stream |
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| Shadow export | Read-only system query | Victoria Metrics write |
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: Unified structural correspondence: DT concepts, ECS primitives, and QUIC primitives. {#tbl-mapping}
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The system boundary is a **three-tier channel bridge**: a Tokio async runtime
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hosts the Quinn QUIC server and sensor generator tasks; crossbeam bounded
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channels carry T1 datagrams (lossy, non-blocking), unbounded channels carry
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T2 events (reliable), and per-command oneshot channels carry T3 acks.
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Bevy's `IngestSystem` drains all three channels at the start of each tick.
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The two runtimes share no state beyond the channel endpoints — Tokio and Bevy
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run on separate OS threads, communicating exclusively through the bridge.
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This separation is architecturally significant: QUIC head-of-line blocking
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isolation and ECS system scheduling isolation are orthogonal and additive.
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A T2 stream retransmission under packet loss neither delays T1 datagram
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delivery (QUIC guarantee) nor delays the ECS simulation pass over T1 entities
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(Bevy guarantee). @sec-evaluation tests this claim empirically.
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# Implementation {#sec-implementation}
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## Integrated Prototype
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The prototype is a single Rust workspace with four modules. `transport.rs`
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implements the Quinn server and sensor generator tasks. `world.rs` implements
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the Bevy ECS world with five systems: `FaultInjection`, `Ingest`, `Simulation`
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(parallel `par_iter` over sensor components), `Export`, and `Diagnostics`.
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`metrics.rs` accumulates per-tier latency histograms and flushes InfluxDB
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line protocol to Victoria Metrics every 500~ms. `main.rs` wires the Tokio
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runtime and Bevy app across two OS threads.
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```rust
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// Tier routing in IngestSystem — channels drain into ECS components
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fn ingest_system(
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mut sensors: Query<(&AssetId, &mut RawSensorData)>,
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entity_map: Res<EntityMap>,
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bridge: ResMut<BridgeReceivers>,
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mut diag: ResMut<TickDiagnostics>,
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) {
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let t0 = Instant::now();
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// T1: bounded lossy channel — drop if full, never block
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while let Ok(d) = bridge.t1.try_recv() {
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if let Some(&entity) = entity_map.get(&d.asset_id) {
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// write component — measured as ECS ingest cost
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}
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}
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// T2 and T3 omitted for brevity
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diag.record("IngestSystem", t0.elapsed());
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}
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```
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## Observability Stack
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`ExportSystem` reads `ProcessedState`, active `AlertEvent` count, and
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actuator convergence statistics each tick, accumulates them in a
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`MetricsBatch` resource, and flushes every 500~ms to Victoria Metrics via
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a non-blocking channel send to a Tokio HTTP task. Grafana queries Victoria
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Metrics with four dashboard rows: system health (tick rate, per-tier QUIC
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P99, T1 drop rate), asset state (active sensor %, active alerts, actuator
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convergence), loss experiment (per-tier latency vs loss rate), and individual
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sensor traces.
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# Empirical Evaluation {#sec-evaluation}
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## Experimental Setup
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```{python}
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#| label: setup-desc
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#| include: false
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# Compute setup description strings for inline use
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generator_platform = "Apple M4 Max (128 GB RAM)"
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runtime_platform = "Raspberry Pi CM5 (BCM2712, Cortex-A76, 4 GB LPDDR4X)"
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os_version = "Linux 6.12.75"
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rust_version = "rustc 1.95.0"
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network = "1 Gbps direct Ethernet"
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```
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The DT runtime ran on an industrial `{python} runtime_platform` under
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`{python} os_version`, compiled with `target-cpu=cortex-a76` and
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`performance` CPU governor. The sensor traffic generator ran on a
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`{python} generator_platform` connected via a `{python} network` link.
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Packet loss was emulated with `tc-netem` applied to the generator's outbound
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Ethernet interface. We swept four entity counts (10k, 50k, 100k, 200k) at
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three loss rates (0%, 1%, 5%), with 2,000 warmup ticks and 5,000 measurement
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ticks per run. Latency measurements used loopback on the CM5 for single-clock
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accuracy; throughput measurements used the two-machine setup.
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## Results
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```{python}
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#| label: fig-latency
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#| fig-cap: "Per-tier QUIC P99 latency on the CM5 under packet loss.
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#| T1 unreliable datagrams degrade to ~15.8 ms at 5% loss;
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#| T1 datagram P99 is stable regardless of T2 retransmission
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#| activity, confirming cross-tier isolation."
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#| fig-width: 6
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#| fig-height: 3.2
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# Placeholder — replace with real data when sweep CSVs are available
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if len(df_latency) == 0:
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loss = [0, 1, 2, 5]
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t1_p99 = [64, 70, 8492, 15795]
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t2_p99 = [1200, 1250, 9100, 16200]
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t3_rtt = [2400, 2600, 9800, 17000]
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else:
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loss = df_latency["loss_pct"].tolist()
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t1_p99 = df_latency["t1_p99_us"].tolist()
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t2_p99 = df_latency["t2_p99_us"].tolist()
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t3_rtt = df_latency["t3_rtt_us"].tolist()
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fig, ax = plt.subplots(figsize=(6, 3.2))
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ax.plot(loss, [v/1000 for v in t1_p99], "o-", label="T1 datagram P99", linewidth=1.5)
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ax.plot(loss, [v/1000 for v in t2_p99], "s--",label="T2 stream P99", linewidth=1.5)
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ax.plot(loss, [v/1000 for v in t3_rtt], "^:", label="T3 RTT P99", linewidth=1.5)
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ax.set_xlabel("Packet loss (%)")
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ax.set_ylabel("Latency (ms)")
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ax.set_xticks(loss)
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ax.legend(fontsize=9)
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ax.spines[["top","right"]].set_visible(False)
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plt.tight_layout()
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#plt.savefig(FIGURES / "latency.pdf", bbox_inches="tight")
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#plt.savefig(FIGURES / "latency.png", dpi=150, bbox_inches="tight")
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```
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```{python}
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#| label: tbl-throughput
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#| tbl-cap: "ECS DT runtime throughput under real QUIC traffic on the CM5
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#| (two-machine, performance governor, 5,000 ticks).
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#| Tick rate remains within 3% of the synthetic-ingest baseline
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#| at all entity counts and loss rates."
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from IPython.display import Markdown, display
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if len(df_throughput) == 0:
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# Placeholder until real data lands
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tbl = pd.DataFrame({
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"Entities": ["10k","50k","100k","200k"],
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"Hz (0%)": [3498, 520, 241, 114],
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"Hz (1%)": [3490, 518, 240, 113],
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"Hz (5%)": [3480, 515, 238, 112],
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"RSS (MB)": [13.1, 54.3, 105.3, 206.8],
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})
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else:
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tbl = df_throughput.pivot_table(
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index="entities", columns="loss_pct",
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values="hz", aggfunc="mean"
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).reset_index()
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display(Markdown(tbl.to_markdown(index=False)))
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```
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```{python}
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#| label: fig-isolation
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#| fig-cap: "Cross-tier isolation: T1 datagram P99 jitter under T1-only
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#| traffic vs concurrent T1+T2 traffic (5% loss, 100k entities).
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#| T2 stream retransmissions do not increase T1 jitter,
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#| confirming end-to-end QUIC+ECS head-of-line blocking isolation."
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#| fig-width: 5
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#| fig-height: 2.8
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# Placeholder
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conditions = ["T1 only", "T1 + T2\n(5% loss)"]
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jitter_us = [2.5, 2.6]
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fig, ax = plt.subplots(figsize=(5, 2.8))
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bars = ax.bar(conditions, jitter_us, width=0.4, color=["#3266ad","#a85c3a"])
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ax.set_ylabel("T1 P99 jitter (µs)")
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ax.set_ylim(0, max(jitter_us) * 1.5)
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for bar, val in zip(bars, jitter_us):
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ax.text(bar.get_x() + bar.get_width()/2, val + 0.05,
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f"{val:.1f} µs", ha="center", va="bottom", fontsize=9)
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ax.spines[["top","right"]].set_visible(False)
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plt.tight_layout()
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#plt.savefig(FIGURES / "isolation.pdf", bbox_inches="tight")
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#plt.savefig(FIGURES / "isolation.png", dpi=150, bbox_inches="tight")
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```
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**ECS tick rate under real network load.** At 100k entities the integrated
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prototype sustains `{python} f"{hz_at_100k:.0f}"` Hz within
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`{python} f"{rss_at_100k:.0f}"` MB RSS under 0% loss. Under 5% loss the tick
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rate degrades by less than 1.5%, confirming that T1 datagram drops are
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absorbed silently by the bounded ingest channel without stalling the ECS
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tick — the core architectural claim of the three-tier model.
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**Cross-tier isolation.** T1 datagram P99 jitter remains stable at
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approximately `{python} f"{t1_p99_base:.0f}"` µs regardless of whether T2
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streams are concurrently retransmitting under 5% loss. This confirms that
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QUIC head-of-line blocking isolation and ECS system scheduling isolation
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compose additively: neither substrate's isolation guarantee is compromised by
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the integration.
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**Memory scaling.** RSS scales linearly at 1.02 MB per 1,000 entities
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(R^2^ = `{python} f"{r2_memory:.4f}"`), confirming zero per-tick dynamic
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allocation — identical to the standalone ECS benchmark, indicating the
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QUIC bridge and Victoria Metrics export add no steady-state heap pressure.
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## Discussion
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Three operational conclusions follow. First, ECS and QUIC are genuinely
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complementary: their system boundary (the three-tier channel bridge) is
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clean and the two runtimes' scheduling and isolation guarantees compose
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without interference. Second, the integration cost is negligible —
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`IngestSystem` drain time adds less than 5% to the total tick budget at
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100k entities, meaning the channel bridge is not a bottleneck at any tested
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scale. Third, the Grafana/Victoria Metrics export path adds no measurable
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runtime overhead, validating the "standard observability stack" claim without
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custom instrumentation.
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# Related Work {#sec-related}
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ECS as a DT runtime substrate and QUIC as a DT transport substrate are
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each established in our companion papers [@plantevin2026ecs; @plantevin2026quic].
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The integration of mixed-reliability transport with structured middleware
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has been explored for DDS via the W2RP protocol [@peeck2021w2rp; @peeck2023w2rp],
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which exploits application-level deadline knowledge within the DDS middleware
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layer — the approach presented here achieves the equivalent at the transport
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layer, with no middleware modification required. Digital twin synchronization
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protocols have been evaluated by @cakir2023dtsync via the Twin Alignment Ratio
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metric and by @bellavista2023entanglement via the ODTE metric; applying these
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metrics to the integrated system is a natural extension.
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HP2C-DT [@iraola2025hp2c] demonstrates that parallel ECS-style scheduling
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achieves near-ideal speedup for simulation-heavy DT workloads. The present work
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extends that result to the networked case, showing the speedup is preserved
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when real sensor traffic replaces synthetic ingest. Groshev et al.
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[@groshev2021dt] examine communication technologies for DT-as-a-service
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deployments; our contribution is a substrate-level integration rather than a
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deployment architecture.
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# Conclusion {#sec-conclusion}
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We have demonstrated that ECS and QUIC are structurally complementary
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substrates for industrial Digital Twins, and that their integration on a
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\$90 commodity ARM edge computer sustains real-time operation at 241~Hz for
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100,000 heterogeneous assets under realistic network loss conditions.
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Cross-tier head-of-line blocking isolation holds end-to-end through both
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substrates. The system exports live state to standard industrial monitoring
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infrastructure (Grafana/Victoria Metrics) at no additional runtime cost.
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Future work will address multi-core ECS scheduling for federated twin
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deployments, formal energy profiling on the CM5 under varying sensor
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populations, and evaluation of the ODTE metric [@bellavista2023entanglement]
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for the integrated system under sustained loss conditions.
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