Edge computing enables latency-critical applications to process data close to end devices, yet task heterogeneity and limited resources pose significant challenges to efficient orchestration. This paper presents a measurement-driven, container-based resource management framework for intra-node optimization on a single edge server hosting multiple heterogeneous applications. Extensive profiling experiments are conducted to derive a nonlinear fitting model that characterizes the relationship among CPU/memory allocations and processing latency across diverse workloads, enabling reliable estimation of performance under varying configurations and providing quantitative support for subsequent optimization. Using this model and a queueing-based delay formulation, we formulate a mixed-integer nonlinear programming (MINLP) problem to jointly minimize system latency and power consumption, which is shown to be NP-hard. The problem is decomposed into tractable convex subproblems and solved through a two-stage container-based resource management scheme (CRMS) combining convex optimization and greedy refinement. The proposed scheme achieves polynomial-time complexity and supports quasi-dynamic execution under global resource constraints. Simulation results demonstrate that CRMS reduces latency by over 14\% and improves energy efficiency compared with heuristic and search-based baselines, offering a practical and scalable solution for heterogeneous edge environments with dynamic workload characteristics.

翻译：边缘计算使延迟关键型应用能够在靠近终端设备处处理数据，但任务异构性与资源受限对高效编排提出了重大挑战。本文提出一种基于测量的容器化资源管理框架，用于在承载多个异构应用的单个边缘服务器上实现节点内优化。通过大量性能剖析实验，推导出刻画CPU/内存分配与多样化工作负载处理延迟之间关系的非线性拟合模型，该模型能够可靠估计不同配置下的性能表现，并为后续优化提供量化支撑。基于此模型与排队论延迟公式，我们将系统延迟与功耗联合最小化问题构建为混合整数非线性规划（MINLP）问题，并证明其具有NP难特性。通过将原问题分解为可处理的凸子问题，提出结合凸优化与贪婪精细化策略的两阶段容器资源管理方案（CRMS）。该方案具有多项式时间复杂度，并支持全局资源约束下的准动态执行。仿真结果表明，相较于启发式与基于搜索的基准方法，CRMS能降低超过14%的延迟并提升能效，为具有动态工作负载特征的异构边缘环境提供了实用且可扩展的解决方案。