Decision-Aware Semantic State Synchronization in Compute-First Networking

2026-01-03

arXiv.org

AI Summary

This work addresses the trade-off in compute-first networking between state update frequency and task offloading accuracy: frequent updates waste bandwidth, while infrequent updates lead to stale states and degraded performance, particularly under high load. To resolve this, the authors propose SenseCFN, a novel framework that introduces lightweight semantic state representations and a Semantic Deviation Index (SDI) to shift state synchronization from time-driven to decision-impact-driven—triggering updates only when state changes are likely to alter offloading decisions. The framework explicitly models state staleness at access points to jointly optimize synchronization and offloading. Implemented under a centralized training with distributed execution (CTDE) architecture, SenseCFN achieves a 99.6% task success rate under high load, outperforming baselines by over 25%, while reducing state update frequency by 70%–96%.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In Compute-First Networking (CFN), an Access Point (AP) makes task offloading decisions based on resource state information reported by a Service Node (SN). A fundamental challenge arises from the trade-off between update overhead and decision accuracy: Frequent state updates consume limited network resources, while infrequent updates lead to stale state views and degraded task performance, especially under high system load. Existing approaches based on periodic updates or Age of Information (AoI) mainly focus on temporal freshness and often overlook whether a state change is actually relevant to offloading decisions. This paper proposes SenseCFN, a decision-aware state synchronization framework for CFN. Instead of synchronizing raw resource states, SenseCFN focuses on identifying state changes that are likely to alter offloading decisions. To this end, we introduce a lightweight semantic state representation that captures decision-relevant system characteristics, along with a Semantic Deviation Index (SDI) to quantify the impact of state shifts on decision outcomes. Based on SDI, the SN triggers updates only when significant decision-impacting changes are detected. Meanwhile, the AP performs offloading decisions using cached semantic states with explicit awareness of potential staleness. The update and offloading policies are jointly optimized using a centralized training with distributed execution (CTDE) approach. Simulation results show that SenseCFN maintains a task success rate of up to 99.6% in saturation-prone scenarios, outperforming baseline methods by more than 25%, while reducing status update frequency by approximately 70% to 96%. These results indicate that decision-aware state synchronization provides an effective and practical alternative to purely time-based update strategies in CFN.

Problem

• Compute-First Networking
• state synchronization
• task offloading
• decision relevance
• update overhead

Innovation

• decision-aware synchronization
• semantic state representation
• Semantic Deviation Index (SDI)
• Compute-First Networking
• CTDE