iDynamics: A Configurable Emulation Framework for Evaluating Microservice Scheduling Policies under Controllable Cloud-Edge Dynamics

TL;DR

iDynamics introduces a configurable emulation framework for evaluating microservice scheduling policies on Kubernetes-based cloud–edge clusters under controllable dynamics. It integrates a Graph Dynamics Analyzer for online call-graph reconstruction, a Networking Dynamics Manager for fine-grained cross-node delay and bandwidth emulation, and a Scheduling Policy Extender for pluggable policy development. The framework enables case studies with call-graph–aware and hybrid policies, demonstrating reduced SLA violations and stable latency under dynamic workloads and network conditions. Open-source and scalable, iDynamics provides a practical platform to compare arbitrary schedulers with realistic microservice topologies and service meshes, advancing systematic evaluation in cloud–edge environments.

Abstract

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.

Figures (14)

• Figure 1: An envisioned workflow illustrating containerized microservice execution and communication under networking dynamics in a cloud--edge continuum.
• Figure 2: Different request types triggering distinct call-graph structures (application source deathStarBench_ASPLOS19).
• Figure 3: Imbalanced traffic loads among UM--DM pairs under high workload (5k Queries Per Second) scenarios.
• Figure 4: iDynamics framework architecture and working procedure.
• Figure 5: Overview of a service mesh structure consisting of data plane and control plane.