具备容量感知边缘准入的预测鲁棒服务部署

中文摘要 AI 辅助

边缘平台将可执行服务实例部署在靠近用户的位置以降低请求服务成本，但每个实例会产生一次性部署成本，且仅在有限的生存时间（TTL）内有效。由此产生的在线决策同时具有预测敏感性和容量耦合性：乐观预测可能会浪费部署成本，而延迟决策则会错过其旨在服务的突发请求。我们在常见的TTL成本模型下研究该问题，并提出CAPSUM，这是一种具备弹性专业化的容量感知准入策略CAPSUM-E。在本地弹性设置中，每个节点服务轨迹恰好是一个可变价格的Bahncard实例。这种归约使CAPSUM-E继承了PFSUM的紧预测误差依赖比率，包括对于β>0的2/(1+β)一致性和1/β鲁棒性。一种感知重定向的变体保留了相同的本地部署调度。对于有限容量节点，CAPSUM结合了规模缩放的盈亏平衡测试、依赖利用率的影子价格以及证据密度驱逐；我们证明了其容量可行性、规模不变性，并且在弹性配置下与CAPSUM-E完全一致。我们实现了一个精确的本地离线动态规划，并与直接的常见模型基线以及EDP-A、OREO和uEDC-L的已记录源派生适配器进行比较。实验涵盖受控预测误差、三种合成需求机制、对公共Globus Compute轨迹的因果预测器，以及联合扩展到1024个节点和10000个服务的情况。在常见模型下，相对于合成机制中最佳的源派生适配器，CAPSUM将归一化成本降低了33.7%-42.9%，在采样轨迹上降低了45.5%。

英文摘要

Edge platforms instantiate executable services close to users to reduce request-serving cost, but each instance incurs a one-time deployment cost and remains useful only for a finite time-to-live (TTL). The resulting online decision is both prediction-sensitive and capacity-coupled: an optimistic forecast can waste deployment cost, whereas a delayed decision misses the burst it is intended to serve. We study this problem under a common TTL cost model and propose CAPSUM, a capacity-aware admission policy with an elastic specialization, CAPSUM-E. In the local elastic setting, every node-service trace is exactly a variable-price Bahncard instance. This reduction lets CAPSUM-E inherit PFSUM's tight prediction-error-dependent ratio, including $2/(1+β)$ consistency and $1/β$ robustness for $β>0$. A redirect-aware variant preserves the same local deployment schedule. For finite-capacity nodes, CAPSUM combines size-scaled break-even tests, a utilization-dependent shadow price, and evidence-density eviction; we prove capacity feasibility, scale invariance, and exact agreement with CAPSUM-E under an elastic configuration. We implement an exact local offline dynamic program and compare against direct common-model baselines and documented source-derived adapters for EDP-A, OREO, and uEDC-L. Experiments cover controlled prediction error, three synthetic demand regimes, a causal predictor on a public Globus Compute trace, and joint scaling to 1,024 nodes and 10,000 services. Under the common model, CAPSUM reduces normalized cost by 33.7-42.9% relative to the best source-derived adapter across the synthetic regimes and by 45.5% on the sampled trace.

发表机构

• School of Software Technology, Zhejiang University（浙江大学软件学院）
• Faculty of Computing, Harbin Institute of Technology（哈尔滨理工大学计算学部）
• College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）

机构由 AI 辅助整理，请以论文原文为准。