MicroFaaS: Adaptive serverless computing for Internet of Things

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-05-29 DOI:10.1016/j.future.2025.107914

Olgierd Krolik , Tomasz Szydlo

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引用次数: 0

Abstract

Cloud and edge computing solutions, and especially serverless offerings, are promising areas of technology that can provide additional computing resources to Internet of Things (IoT) devices. This research aims to design and evaluate a novel adaptive computations offloading framework for the IoT domain that leverages serverless Function-as-a-Service (FaaS) solutions capabilities to intelligently select the most suitable execution environment to run the computations in. Pretrained cost estimation models are constructed for each function and each environment (FaaS platform) and they are used by offloading strategies on IoT devices to determine the best execution environment for each invocation. Conducted research demonstrate that pretraining of cost estimation models significantly reduces the time required to calibrate the decision-making offloading algorithm on devices. Evaluation results also prove that it is possible to achieve better function execution times by using offloading algorithms that intelligently select the execution environment for each invocation and can adapt themselves quickly to sudden deterioration of network conditions by monitoring the network state.

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MicroFaaS：面向物联网的自适应无服务器计算

云和边缘计算解决方案，特别是无服务器产品，是有前途的技术领域，可以为物联网（IoT）设备提供额外的计算资源。本研究旨在为物联网领域设计和评估一种新的自适应计算卸载框架，该框架利用无服务器功能即服务（FaaS）解决方案功能，智能地选择最合适的执行环境来运行计算。为每个功能和每个环境（FaaS平台）构建预训练的成本估算模型，并通过物联网设备上的卸载策略来确定每个调用的最佳执行环境。进行的研究表明，成本估计模型的预训练显着减少了在设备上校准决策卸载算法所需的时间。评估结果还证明，通过使用卸载算法可以实现更好的函数执行时间，该算法可以智能地为每次调用选择执行环境，并通过监控网络状态快速适应网络条件的突然恶化。

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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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.