A Controllable and Realistic Framework for Evaluating Microservice Scheduling in Cloud-Edge Continuum
🤖 AI Summary
In cloud-edge collaborative continuum environments, microservice scheduling faces challenges including complex call-graph dependencies, dynamic inter-node communication latency, and fluctuating bandwidth—conditions that existing evaluation frameworks struggle to reproduce realistically and controllably. To address this, we propose iDynamics, the first modular dynamic simulation framework specifically designed for evaluating cloud-edge microservice scheduling policies. Our approach decouples the modeling of call-graph topology, time-varying network latency, and bandwidth dynamics; enables fine-grained environmental configuration and systematic, multi-policy comparison; and integrates graph dynamical analysis, programmable latency injection, and containerized deployment. Empirical validation on real-world cloud-edge platforms demonstrates that iDynamics significantly enhances the realism, reproducibility, and comparability of scheduling evaluations.
Application Category
📝 Abstract
The transition from traditional architectures to containerized microservices within the cloud-edge computing continuum introduces significant challenges, particularly in the efficient scheduling of microservices under dynamic conditions. Complex and fluctuating call-graph dependencies, varying cross-node communication latencies, and unpredictable bandwidth conditions substantially impact the performance and reliability of deployed microservices. Consequently, accurately evaluating scheduling policies in such dynamic environments remains essential yet challenging due to the lack of realistic and controllable evaluation frameworks. In this paper, we propose iDynamics, a novel evaluation framework designed explicitly to address these challenges. iDynamics provides comprehensive and controllable evaluation capabilities by emulating realistic dynamics, including configurable call-graph topologies, cross-node communication delays, and bandwidth variability. The framework is composed of modular components, such as the Graph Dynamics Analyzer, Networking Dynamics Manager, and Scheduling Policy Extender, enabling fine-grained environmental control and facilitating systematic comparisons of different scheduling strategies. Extensive experiments on a real cloud-edge testbed demonstrate that iDynamics effectively captures diverse dynamic scenarios encountered in microservice deployments, offering a robust solution for evaluating and optimizing policy performance under realistic and controllable conditions.
Problem
Research questions and friction points this paper is trying to address.
Efficient microservice scheduling in dynamic cloud-edge environments
Addressing fluctuating call-graph dependencies and communication latencies
Lack of realistic, controllable frameworks for policy evaluation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Emulates realistic dynamics for microservice scheduling
Modular components enable fine-grained environmental control
Facilitates systematic comparison of scheduling strategies
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