发表机构
Fudan University; Singapore University of Technology and Design; Nanjing University of Posts and Telecommunications; Yonsei Frontier Lab, Yonsei University（复旦大学; 新加坡科技设计大学; 南京邮电大学; 延世大学前沿实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对协同边缘计算中重复计算与缓存更新成本问题，本文提出双时间尺度计算复用算法，联合优化加权响应时间与缓存更新成本，经数值验证性能优于基准方案。
AI 中文摘要
协同边缘计算（CEC）是一种高效的计算范式，它允许相邻边缘服务器相互共享计算资源。尽管CEC可以提高资源利用率，但由于附近终端用户经常卸载具有相似输入的任务，它仍然存在重复计算的问题。为了提高系统效率，先前执行任务的计算结果可以被缓存并供后续任务复用。然而，时变的任务流行度和到达率要求缓存和调度决策能够适应需求变化，而频繁的缓存更新会产生额外成本。为解决该问题，本文为CEC网络提出了一种双时间尺度计算复用算法。我们构建了一个优化问题，该问题联合考虑加权响应时间和缓存更新成本，其中结果缓存决策在帧级别确定，而工作负载调度、缓存搜索和计算资源分配在时隙级别调整。利用近期的工作负载观测，我们构建了帧级代理问题并将其分解为缓存子问题和调度子问题。对于缓存子问题，我们引入了边际存储效率，并将缓存更新成本纳入基于二分法的算法中。在有界归一化边际灵敏度下，当单个结果大小相对于缓存容量较小且松弛解足够准确时，该算法针对单基站（BS）缓存子问题可实现接近最优的目标值。对于调度子问题，我们利用投影梯度下降和跨连续时隙的带热启动的回溯法。数值结果表明，在不同的网络和工作负载设置下，该算法相较于基准方案始终能实现性能提升。
英文摘要
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.
