Squeezing Edge Performance: A Sensitivity-Aware Container Management for Heterogeneous Tasks

TL;DR

This paper introduces a measurement-driven, container-based resource management framework for edge computing that optimizes latency and power consumption for heterogeneous tasks, using a nonlinear performance model and a two-stage optimization scheme.

Contribution

It presents a novel, scalable resource management scheme combining nonlinear modeling and convex optimization for intra-node edge computing with heterogeneous workloads.

Findings

Reduces latency by over 14% compared to baselines.

Improves energy efficiency in edge environments.

Supports quasi-dynamic execution under resource constraints.

Abstract

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.