This paper presents iDynamics, a configurable emulation framework that exposes these dynamics as controllable experimental factors while running real microservice code on a Kubernetes-based cloud-edge cluster. iDynamics comprises three modular components. The Graph Dynamics Analyzer reconstructs application call graphs from service-mesh telemetry and quantifies bidirectional traffic between upstream-downstream microservice pairs. The Networking Dynamics Manager injects and measures realistic cross-node delay and bandwidth patterns via Linux traffic control primitives and distributed agents. The Scheduling Policy Extender offers a pluggable interface and utility library for implementing and evaluating arbitrary scheduling policies, expressed as pod placement and migration strategies. We use iDynamics to implement two representative policies -- a call-graph-aware policy and a hybrid policy that jointly considers traffic and latency -- as case studies demonstrating how the framework can be used to study SLA compliance under dynamic conditions. Experiments on a real cloud-edge cluster, running the DeathStarBench Social Network microservices, show that iDynamics can accurately emulate targeted network conditions, generate diverse call-graph and traffic patterns, and help quantify how different scheduling policies mitigate SLA violations under controllable and repeatable dynamics.
翻译:本文提出iDynamics,一种可配置的仿真框架,可在基于Kubernetes的云边集群上运行真实微服务代码的同时,将这些动态作为可控实验因素进行暴露。iDynamics包含三个模块化组件:图动态分析器从服务网格遥测数据中重构应用调用图,并量化上下游微服务对之间的双向流量;网络动态管理器通过Linux流量控制原语和分布式代理注入并测量真实的跨节点延迟与带宽模式;调度策略扩展器提供可插拔接口和实用程序库,用于实现和评估任意调度策略,这些策略以Pod放置和迁移策略的形式表达。我们使用iDynamics实现了两种代表性策略——一种基于调用图感知的策略和一种同时考虑流量与延迟的混合策略——作为案例研究,展示了该框架如何用于研究动态条件下的服务等级协议(SLA)合规性。在运行DeathStarBench社交网络微服务的真实云边集群上的实验表明,iDynamics能够准确仿真目标网络条件,生成多样化的调用图与流量模式,并有助于量化不同调度策略在可控且可重复的动态下如何减少SLA违规。