ADASCALE：动态环境下微服务的自适应扩展与放置框架
ADASCALE: An Adaptive Scaling and Placement Framework for Microservices Under Dynamics
AI总结：
研究云边缘环境下微服务面临的多维度动态问题，提出ADASCALE框架，通过MAPE循环联合扩展和放置微服务副本，经实验验证能满足SLO目标，有效提升延迟和吞吐量。
AI中文摘要：
微服务应用越来越多地部署在云边缘环境中，异构节点和时变节点间延迟放大了放置决策的影响。同时，这些应用面临非平稳流量、根请求操作组合变化以及异构通信模式等问题。现有自动缩放器和网络感知调度器通常只能处理部分动态情况。我们提出了ADASCALE，一个在多维动态下联合扩展和放置微服务副本的自适应框架。它实现了一个MAPE循环，从分布式跟踪和服务网格指标中提取每条边和每个服务的需求，识别混合工作负载下最关键的根操作，计算SLO感知的副本目标，并放置副本以最小化需求加权延迟目标。我们在云边缘Kubernetes集群上使用DeathStarBench社交网络应用进行评估，结果表明ADASCALE始终能满足SLO目标并提高延迟和吞吐量。
英文摘要：
Microservice applications are increasingly deployed across cloud--edge environments, where heterogeneous nodes and time-varying inter-node delays amplify the impact of placement decisions. At the same time, these applications face non-stationary traffic, shifts in the mix of root request operations that exercise different call graphs, and heterogeneous communication modes that determine how network latency and queuing propagate to end-to-end (E2E) performance. Existing autoscalers and network-aware schedulers typically handle only a subset of these dynamics, leading to either compute bottlenecks or inflated cross-node latency and thus SLO violations. We propose ADASCALE, an adaptive framework that jointly scales and places microservice replicas under such multi-dimensional dynamics. ADASCALE implements a Monitor--Analyzer--Planner--Executor (MAPE) loop that extracts per-edge and per-service demand from distributed traces and service-mesh metrics, identifies the most critical root operation under a mixed workload, computes SLO-aware replica targets, and then places replicas to minimize a demand-weighted latency objective given the current inter-node latency matrix. To react quickly to networking perturbations, ADASCALE triggers a reactive placement loop, while a steady-state autoscaling loop handles demand shifts. We evaluate ADASCALE on a cloud--edge Kubernetes cluster using the DeathStarBench Social Network application with three root operations under varying load and workload mixes. Across scenarios, ADASCALE consistently meets SLO targets and improves both latency and throughput: compared with NetMARKS_Scale, it achieves up to 1.56x, 1.93x, and 1.34x lower average response time (for compose-post, read-home-timeline, and read-user-timeline) and up to 2.16x, 1.32x, and 1.36x higher throughput, respectively.
