Dependency- and Layer-Aware Microservice Workflow Offloading and Service Image Caching for Edge Environments

2026-09-23

AI Summary

This study addresses the challenges of complex solution spaces and long-range dependencies in the joint optimization of microservice workflow offloading and image caching within edge computing. To this end, it proposes a dependency- and hierarchy-aware collaborative optimization framework that pioneers a joint mechanism for workflow offloading and image caching, enabling efficient edge resource scheduling and cache decision-making by capturing inter-task dependencies. The primary contributions include the construction and open-sourcing of a real-world image layer dataset alongside its corresponding codebase. Experimental results demonstrate that the proposed approach reduces task completion time by 22.38% while significantly improving image cache hit rates.

Abstract

The microservice architecture has been applied broadly in many mainstream computing environments. As one of the most prevalent environments, edge computing also widely employs microservices to handle diverse requests and tasks. In practical scenarios, microservices usually constitute workflows that are built based on service dependencies to execute tasks. This offers an opportunity to explore the offloading technology to better harness resources and accelerate task execution. Unfortunately, this problem has not been paid attention to by existing research, and thus some valuable resources (e.g., microservice image cache) remain obscure. To fill this gap, we innovatively study the problem of joint optimization for microservice workflow offloading and service image caching in edge. This problem is challenging due to several issues such as the complexity of its solution space, intricate relationships among shared image layers, long-range dependencies between workflow tasks, and the absence of a real-world collection of service images. To address these issues, this paper proposes an innovative dependency- and layer-aware workflow offloading and image caching framework for microservices. We collected a real-world collection of microservice image layer data and published both this collection and experimental codes on GitHub. Extensive results demonstrate that our framework achieves superior performances, for example, a 22. 38\% reduction in average task completion time compared to baselines and significantly-increased image hit rates.

Problem

• Microservice Workflow
• Workflow Offloading
• Service Image Caching
• Edge Computing
• Joint Optimization

Innovation

• Microservice Workflow Offloading
• Service Image Caching
• Edge Computing
• Joint Optimization
• Layer-Aware Framework