Decision-Aware Semantic State Synchronization in Compute-First Networking

TL;DR

SenseCFN introduces a decision-aware semantic state synchronization framework for Compute-First Networking, significantly reducing update overhead while maintaining high task success rates by focusing on decision-impacting state changes.

Contribution

It proposes a lightweight semantic state representation and SDI metric to trigger updates only when they impact offloading decisions, improving efficiency over traditional methods.

Findings

Achieves up to 99.6% task success rate in high load scenarios.

Reduces update frequency by 70-96% compared to baseline methods.

Outperforms existing approaches by over 25% in decision accuracy.

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.