Apollo: Modular and Distributed Runtime System for Serverless Function Compositions on Cloud, Edge, and IoT Resources

Fedor Smirnov, Behnaz Pourmohseni, T. Fahringer
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引用次数: 2

Abstract

This paper provides a first presentation of Apollo, a runtime system for serverless function compositions distributed across the cloud-edge-IoT continuum. Apollo's modular design enables a fine-grained decomposition of the runtime implementation(scheduling, data transmission, etc.) of the application, so that each of the numerous implementation decisions can be optimized separately, fully exploiting the potential for the optimization of the overall performance and costs. Apollo features (a) a flexible model of the application and the available resources and (b) an implementation process based on a large set of independent agents. This flexible structure enables distributing not only the processing, but the implementation process itself across a large number of resources, each running an independent Apollo instance. The ability to flexibly determine the placement of implementation actions opens up new optimization opportunities, while at the same time providing access to greater computing power for optimizing challenging decisions such as task scheduling and the placement and routing of data.
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Apollo:模块化和分布式运行时系统,用于云、边缘和物联网资源上的无服务器功能组合
本文首次介绍了Apollo,这是一个分布在云边缘物联网连续体上的无服务器功能组合的运行时系统。Apollo的模块化设计可以对应用程序的运行时实现(调度、数据传输等)进行细粒度的分解,从而可以对众多实现决策中的每一个单独进行优化,充分挖掘整体性能和成本优化的潜力。Apollo的特点是(a)应用程序的灵活模型和可用资源,以及(b)基于大量独立代理的实现过程。这种灵活的结构不仅可以跨大量资源分布处理,还可以跨大量资源分布实现过程本身,每个资源运行一个独立的Apollo实例。灵活地确定实现操作的位置的能力开辟了新的优化机会,同时为优化具有挑战性的决策(如任务调度和数据的放置和路由)提供了更强大的计算能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Session details: Session: Short Papers Expanding Cost-Aware Function Execution with Multidimensional Notions of Cost Session details: Session: Full Papers Motivating High Performance Serverless Workloads SLA-aware Workload Scheduling Using Hybrid Cloud Services
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