Latency-aware scheduling for data-oriented service requests in collaborative IoT-edge-cloud networks

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 : 2024-09-21 DOI:10.1016/j.future.2024.107538

Mengyu Sun , Shuo Quan , Xuliang Wang , Zhilan Huang

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引用次数: 0

Abstract

Edge computing facilitates the collaboration of physical devices at the network edge to support nearby computing requests, in order to reduce long-distance sensory data transmission from Internet of Things (IoT) devices to the remote cloud. An IoT-edge-cloud network is constructed, where sensory data collected by IoT devices is aggregated to the physically adjacent edge nodes and is transmitted between these edge nodes for achieving task processing, and the cloud acts as a central controller with global scheduling, considering the latency sensitivity of service requests and capacity limitation of physical devices. These service requests are decomposed into multiple data-oriented tasks with certain logical relations, and the satisfaction of service requests is implemented in such a collaborative IoT-edge-cloud network. In this setting, a data-oriented task scheduling mechanism is presented through considering data aggregation, data transmission and task processing in a latency-efficient and energy-saving fashion, which is formulated as a constrained objective optimization problem. We develop an improved Genetic Algorithm-based Task Scheduling (iGATS) approach, where task scheduling decisions are regarded as chromosome codings, fitness function and genetic operators are designed to solve the formulated problem. Simulation experiments are evaluated, and numerical results show that our iGATS outperforms other baseline techniques for reducing response latency, improving temporal satisfaction of service requests, and maintaining load-balancing across the whole network.

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协作式物联网边缘云网络中面向数据的服务请求的延迟感知调度

边缘计算可促进网络边缘物理设备之间的协作，以支持附近的计算请求，从而减少从物联网（IoT）设备到远程云的长距离感知数据传输。我们构建了一个 "物联网-边缘-云 "网络，将物联网设备收集到的感知数据汇聚到物理上相邻的边缘节点，并在这些边缘节点之间传输，以实现任务处理，而云则作为中央控制器，考虑到服务请求的延迟敏感性和物理设备的容量限制，进行全局调度。这些服务请求被分解成具有一定逻辑关系的多个面向数据的任务，服务请求的满足就是在这样一个物联网-边缘-云协作网络中实现的。在这种情况下，我们提出了一种面向数据的任务调度机制，该机制通过考虑数据聚合、数据传输和任务处理，实现了时延高效和节能，并将其表述为一个有约束的目标优化问题。我们开发了一种改进的基于遗传算法的任务调度（iGATS）方法，将任务调度决策视为染色体编码，并设计了适合度函数和遗传算子来解决所提出的问题。仿真实验和数值结果表明，iGATS 在减少响应延迟、提高服务请求的时间满意度和保持整个网络的负载平衡方面优于其他基准技术。

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.