Collaborative edge servers cut repeated computing with smart caching
Online Adaptive Computation Reuse in Collaborative Edge Computing: A Two-Timescale Approach
Networking and Internet Architecture
Summary
Computers at the edge often do the same work when many users ask for similar things, wasting time and power. The authors designed a smart system that remembers recent results and decides when to save or update them, so it quickly handles new tasks without repeating work unnecessarily. Their approach uses two timed steps to balance speed and cost, learning from changing demand patterns. Tests show the method helps edge servers work more efficiently under different conditions.
What this means in practice
- •For network operators: Improve edge server efficiency by dynamically caching and reusing computation results to reduce delays and resource waste in task processing.
- •For cloud infrastructure teams: Optimize scheduling and resource allocation to handle fluctuating workloads with reduced overhead from updating cached results in edge computing.
Authors
Xingqiu He, Chaoqun You, Peng Wang, Xiaoyu Liu, Tony Q. S. Quek
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.