I-HARF: Intelligent and Hierarchical Framework for Adaptive Resource Facilitation in Edge-IoT Systems
I-HARF: Intelligent and Hierarchical Framework for Adaptive Resource Facilitation in Edge-IoT Systems
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DOI：
10.1109/jiot.2022.3151667
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发表时间：
2023-03
影响因子：
10.6
通讯作者：
Ismail Alqerm;Jianli Pan
中科院分区：
文献类型：
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作者：
Ismail Alqerm;Jianli Pan
边缘计算被用来促进更紧密的计算、存储和网络资源，以支持各种物联网应用，包括对延迟敏感的应用。预计未来的边缘物联网系统将整合分布在某些机构的多个地理区域的异构物联网设备，其边缘资源需求根据时间和位置而变化。边缘服务器（资源促进者）资源有限，容易出现“异常”情况，例如服务过载、中断和外部攻击；他们可能还必须处理物联网设备在不同区域之间的漫游。这些情况导致需要使用自适应资源促进方案的替代边缘服务器来满足物联网应用的需求。在本文中，我们开发了一种名为 I-HARF 的新型智能分层资源促进框架，该框架适应动态边缘物联网情况，包括奇怪的情况、移动性、应用程序的敏感性以及物联网应用程序基于时间和位置的不同资源需求。 I-HARF通过以下方式实现适应性便利化，整体解决便利化技术障碍：1）采用分层结构，有效地将资源便利化从区内向区间迁移； 2）扩展新颖的区域内和区域间优化模型，以提高边缘服务器和物联网应用的效用； 3）开发一种新颖且独特的演员双批评家和集体演员批评家深度强化学习（DRL）设计，分别智能地促进区域内和区域间的边缘资源。评估结果证明了 I-HARF 能够根据动态边缘物联网情况进行调整的自适应资源促进能力。
Edge computing is being used to facilitate closer computing, storage, and networking resources to support various IoT applications including delay-sensitive ones. It is envisioned that the future Edge-IoT systems will incorporate heterogeneous IoT devices distributed over multiple geographical zones of certain institutions with edge resource demands that vary according to time and location. Edge servers (resource facilitators) are with limited resources and are susceptible to “outlandish” situations, such as service overloading, outage, and external attacks; they may also have to handle the roaming of IoT devices among different zones. These situations induce the need for alternative edge servers using an adaptive resource facilitation scheme to fulfill the demands of the IoT applications. In this article, we develop a novel intelligent and hierarchical resource facilitation framework named I-HARF that adapts to dynamic Edge-IoT situations, including outlandish situations, mobility, application’s sensitivity, and varying resource demand of IoT applications based on time and location. I-HARF achieves an adaptive facilitation and holistically addresses the facilitation technical barriers by: 1) adopting the hierarchical structure which efficiently migrates the resource facilitation from intrazone to interzone levels; 2) extending novel intrazone and interzone optimization models to boost the utilities of the edge servers and the IoT applications; and 3) developing a novel and unique actor dual-critic and collective actor–critic deep reinforcement learning (DRL) designs that intelligently facilitate the edge resources in both intrazone and interzone, respectively. The evaluation results demonstrate I-HARF’s capability enabling adaptive resource facilitation that adjusts according to the dynamic Edge-IoT situations.