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Efficient Information Updates in Compute-First Networking via Reinforcement Learning with Joint AoI and VoI

Jianpeng Qi, Chao Liu, Chengxiang Xu, Rui Wang, Junyu Dong, Yanwei Yu

TL;DR

The paper addresses timely and resource-aware updates in compute-first networking by introducing the AVA metric, which jointly captures information freshness via AoI concepts and semantic value via VoI-like content consistency. It models the update decision as a discrete-time MDP and solves it with Proximal Policy Optimization (PPO), using a state that includes actual and observed capacity, AoI at query times, and update delays. Key contributions include the AVA metric, MDP formulation, theoretical guarantees (boundedness, regularity, stationary optimal policy), and extensive simulations showing update-rate reductions of up to 98% with maintained decision accuracy. The approach has practical impact for reducing communication overhead in edge-enabled CFN while preserving service quality, and opens avenues for extending to multi-source CFN and richer queuing models.

Abstract

Timely and efficient dissemination of service information is critical in compute-first networking systems, where user requests arrive dynamically and computing resources are constrained. In such systems, the access point (AP) plays a key role in forwarding user requests to a server based on its latest received service information. This paper considers a single-source, single-destination system and introduces an Age-and-Value-Aware (AVA) metric that jointly captures both the timeliness and the task relevance of service information. Unlike traditional freshness-based metrics, AVA explicitly incorporates variations in server-side service capacity and AP forwarding decisions, allowing more context-aware update evaluation. Building upon AVA, we propose a reinforcement learning-based update policy that learns to selectively transmit service information updates to the AP. It aims to maximize overall task success while minimizing unnecessary communications. Extensive simulations under diverse user request patterns and varying service capacities demonstrate that AVA reduces the update frequency by over 90% on average compared to baselines, with reductions reaching 98% in certain configurations. Crucially, this reduction is achieved without compromising the accuracy of task execution or the quality of decision making.

Efficient Information Updates in Compute-First Networking via Reinforcement Learning with Joint AoI and VoI

TL;DR

The paper addresses timely and resource-aware updates in compute-first networking by introducing the AVA metric, which jointly captures information freshness via AoI concepts and semantic value via VoI-like content consistency. It models the update decision as a discrete-time MDP and solves it with Proximal Policy Optimization (PPO), using a state that includes actual and observed capacity, AoI at query times, and update delays. Key contributions include the AVA metric, MDP formulation, theoretical guarantees (boundedness, regularity, stationary optimal policy), and extensive simulations showing update-rate reductions of up to 98% with maintained decision accuracy. The approach has practical impact for reducing communication overhead in edge-enabled CFN while preserving service quality, and opens avenues for extending to multi-source CFN and richer queuing models.

Abstract

Timely and efficient dissemination of service information is critical in compute-first networking systems, where user requests arrive dynamically and computing resources are constrained. In such systems, the access point (AP) plays a key role in forwarding user requests to a server based on its latest received service information. This paper considers a single-source, single-destination system and introduces an Age-and-Value-Aware (AVA) metric that jointly captures both the timeliness and the task relevance of service information. Unlike traditional freshness-based metrics, AVA explicitly incorporates variations in server-side service capacity and AP forwarding decisions, allowing more context-aware update evaluation. Building upon AVA, we propose a reinforcement learning-based update policy that learns to selectively transmit service information updates to the AP. It aims to maximize overall task success while minimizing unnecessary communications. Extensive simulations under diverse user request patterns and varying service capacities demonstrate that AVA reduces the update frequency by over 90% on average compared to baselines, with reductions reaching 98% in certain configurations. Crucially, this reduction is achieved without compromising the accuracy of task execution or the quality of decision making.
Paper Structure (30 sections, 4 theorems, 14 equations, 10 figures, 2 tables)

This paper contains 30 sections, 4 theorems, 14 equations, 10 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Information Effectiveness Metrics
  4. Update Decision Optimization
  5. System Model and Problem Formulation
  6. The Stages of Service Information Consumption
  7. Key Observations
  8. System Entities and Communication
  9. The AVA Metric
  10. Problem Formulation
  11. MDP Formulation and Solutions
  12. State Space $\mathcal{S}$
  13. Action Space $\mathcal{A}$
  14. Transition Dynamics $\mathcal{P}$
  15. Reward $\mathcal{R}$ and Objective
  16. ...and 15 more sections

Key Result

Proposition 1

For all $t\in\mathbb{N}$, $-1\le\delta(t) =\phi(\Upsilon_i)\,\mathcal{R}(c_t,\hat{c}_t) \le 1.$

Figures (10)

  • Figure 1: Scenarios of compute-aware task offloading
  • Figure 2: Illustration of the information-consumption stages
  • Figure 3: Timeline of system status and key decision events.
  • Figure 4: The implemented simulation environment
  • Figure 5: Update rate under deterministic distributed user access patterns
  • ...and 5 more figures

Theorems & Definitions (10)

  • Definition 1: System state
  • Definition 2: Service information
  • Proposition 1: Boundedness and Normalization
  • proof
  • Proposition 2: Monotonicity
  • proof
  • Lemma 1: Positive Recurrence
  • proof : Proof idea
  • Theorem 1
  • proof