ContinuumBench: Benchmarking Joint Autoscaling and Placement Across Evaluation Regimes in the Cloud-Edge Continuum

2026-09-08

AI Summary

该研究通过ContinuumBench基准解决了云-边缘环境中服务放置与自动扩展联合评估的问题，采用控制实验方法比较了不同控制器在多种条件下的表现。

Abstract

Cloud-edge controllers coordinate service placement, replica scaling, and resource pre-warming to keep end-to-end latency within application deadlines. But evaluations often obscure the source of a reported gain: placement and scaling are studied separately; workload, connectivity, and calibration assumptions remain implicit; and metrics over completed tasks hide unfinished work. We present ContinuumBench, a benchmark that controls these factors. Its completion-aware accounting treats late, unfinished, and discarded tasks as deadline misses. A common protocol compares placement-only and scale-capable controllers under declared regimes and stressors. Built on the ECLYPSE simulator, ContinuumBench adds arrivals, worker elasticity, intermittent transport, buffering, and failures to close the control loop. We evaluate nine controllers across four scenarios and two regimes. The studied regimes are capacity-bound: elastic capacity, not placement sophistication, drives completion, and once capacity suffices, the choice of autoscaling policy decides how much of that work arrives on time. Placement re-planning has no measurable effect without relocation, while cost-free migration defines the observed exception. Consequently, scale-capable controllers approach an over-provisioned reference while placement-only controllers degrade with load; and placement quality separates controllers only once capacity is exhausted. Finally, the accounting choice itself changes the reported result: completion-only and completion-aware scoring can rank controllers differently.

Problem

• Cloud-Edge Continuum
• Autoscaling
• Service Placement
• Benchmarking
• End-to-End Latency

Innovation

• Completion-aware Accounting
• Joint Autoscaling and Placement
• Cloud-Edge Continuum
• ECLYPSE Simulator