面向边缘环境的依赖与层感知微服务工作流卸载和服务镜像缓存
Dependency- and Layer-Aware Microservice Workflow Offloading and Service Image Caching for Edge Environments
- School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）
- Yunnan Provincial Key Laboratory of Software Engineering（云南省软件工程重点实验室）
- School of Software Technology, Zhejiang University（浙江大学软件技术学院）
- College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
- School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对边缘环境中的微服务工作流，提出依赖与层感知的卸载和镜像缓存框架，显著降低任务完成时间并提高镜像命中率。
AI中文摘要：
微服务架构已在许多主流计算环境中得到广泛应用。作为最普遍的环境之一，边缘计算也广泛采用微服务来处理各种请求和任务。在实际场景中，微服务通常构成基于服务依赖构建的工作流以执行任务。这为探索卸载技术以更好地利用资源和加速任务执行提供了机会。遗憾的是，现有研究尚未关注这一问题，因此一些有价值的资源（如微服务镜像缓存）仍然未被充分利用。为填补这一空白，我们创新性地研究了边缘环境中微服务工作流卸载与服务镜像缓存的联合优化问题。该问题因解空间的复杂性、共享镜像层之间的错综关系、工作流任务间的长距离依赖以及缺乏真实世界的服务镜像集合等若干问题而具有挑战性。为解决这些问题，本文提出了一种创新的依赖与层感知的工作流卸载和镜像缓存框架，适用于微服务。我们收集了真实世界的微服务镜像层数据，并将该数据集及实验代码发布在GitHub上。大量实验结果表明，我们的框架实现了优越的性能，例如，与基线相比，平均任务完成时间减少了22.38%，镜像命中率显著提高。
英文摘要：
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.
