ContinuumBench：云-边缘连续体中跨评估机制的联合自动缩放与放置基准测试
ContinuumBench: Benchmarking Joint Autoscaling and Placement Across Evaluation Regimes in the Cloud-Edge Continuum
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中文总结 AI 辅助
ContinuumBench基准测试通过完成感知记账和统一协议，在容量受限机制下评估九个控制器，发现弹性容量和自动缩放策略决定完成率，放置质量仅在容量耗尽时起作用。
中文摘要 AI 辅助
云-边缘控制器协调服务放置、副本缩放和资源预热，以将端到端延迟保持在应用程序截止时间内。但评估常常掩盖所报告收益的来源：放置和缩放被分开研究；工作负载、连接性和校准假设仍然隐含；基于已完成任务的指标隐藏了未完成的工作。我们提出了ContinuumBench，一个控制这些因素的基准测试。其完成感知的记账方式将延迟、未完成和丢弃的任务视为截止时间错过。一个通用协议在声明的机制和压力源下比较仅放置和可缩放控制器。基于ECLYPSE模拟器构建，ContinuumBench添加了到达、工作节点弹性、间歇性传输、缓冲和故障以闭合控制回路。我们在四个场景和两种机制下评估了九个控制器。所研究的机制是容量受限的：弹性容量而非放置复杂性驱动完成率，一旦容量充足，自动缩放策略的选择决定了有多少工作按时到达。没有重新定位时，放置重新规划没有可测量的效果，而无成本迁移定义了观察到的例外。因此，可缩放控制器接近过度供应的参考，而仅放置控制器随负载而退化；且只有在容量耗尽时，放置质量才区分控制器。最后，记账选择本身改变了报告的结果：仅完成和完成感知评分可能对控制器进行不同排名。
英文摘要
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.
发表机构
- Institute of Information Science and Technologies “Alessandro Faedo” (ISTI), National Research Council of Italy (CNR)（意大利国家研究委员会信息科学与技术研究所（ISTi））
- Department of Computer Science, University of Pisa（比萨大学计算机科学系）
- Department of AI, Data and Decision Sciences, LUISS University（LUISS大学人工智能、数据与决策科学系）
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