Online Adaptive Computation Reuse in Collaborative Edge Computing: A Two-Timescale Approach

2026-10-08

AI Summary

This study addresses the challenges of redundant computation for similar tasks and cache update overhead in collaborative edge computing. To this end, it proposes a two-timescale adaptive computation reuse algorithm that jointly optimizes response time and caching costs. Methodologically, the approach decouples frame-level caching decisions from slot-level scheduling and introduces a marginal storage efficiency metric to achieve near-optimal cache management. The resulting optimization problem is efficiently solved by integrating projected gradient descent, backtracking line search, and bisection methods. Experimental evaluations demonstrate that the proposed system significantly outperforms existing baseline schemes across diverse network load scenarios.

Abstract

Collaborative Edge Computing (CEC) is an efficient computing paradigm that enables neighboring edge servers to share computational resources with each other. Although CEC can enhance resource utilization, it still suffers from duplicate computations because nearby end-users often offload tasks with similar inputs. To improve system efficiency, the computation results of previously executed tasks can be cached and reused by subsequent tasks. However, time-varying task popularity and arrival rates require caching and scheduling decisions to adapt to demand changes, while frequent cache updates incur additional costs. To address this issue, this paper develops a two-timescale computation reuse algorithm for CEC networks. We formulate an optimization problem that jointly considers weighted response time and cache update cost, with result caching decisions determined at the frame level and workload scheduling, cache searching, and computational resource allocation adjusted at the slot level. Using recent workload observations, we construct a frame-level surrogate problem and decompose it into a caching subproblem and a scheduling subproblem. For the caching subproblem, we introduce marginal storage efficiency and incorporate cache update costs into a bisectionbased algorithm. Under bounded normalized marginal sensitivity, the algorithm achieves a near-optimal objective value for the single-BS caching subproblem when individual result sizes are small relative to the cache capacity and the relaxed solution is sufficiently accurate. For the scheduling subproblem, we utilize projected gradient descent and backtracking with warm starts across consecutive slots. Numerical results demonstrate consistent performance gains over benchmark schemes across diverse network and workload settings.

Problem

• Collaborative Edge Computing
• Computation Reuse
• Result Caching
• Cache Update Cost
• Two-Timescale Optimization

Innovation

• Collaborative Edge Computing
• Computation Reuse
• Two-Timescale Optimization
• Marginal Storage Efficiency
• Projected Gradient Descent