Content-centric data and computation offloading in AI-supported fog networks for next generation IoT

作     者：Kok, Ibrahim Ozdemir, Suat 

作者机构：Pamukkale Univ Dept Comp Engn Denizli Turkey Hacettepe Univ Dept Artificial Intelligence Engn Ankara Turkey 

出 版 物：《PERVASIVE AND MOBILE COMPUTING》 (普适与移动计算)

年 卷 期：2022年第85卷

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Scientific and Technological Research Council of Turkey [118E212] 

主　　题：Next Generation Internet of Things (NGIoT) Deep reinforcement learning FogOrch Fog computing Data offloading Computation offloading 

摘      要：Fog Computing (FC) based IoT applications are encountering a bottleneck in the data management and resource optimization due to the dynamic IoT topologies, resource-limited devices, resource diversity, mismatching service quality, and complicated service offering environments. Existing problems and emerging demands of FC based IoT applications are hard to be met by traditional IP-based Internet model. Therefore, in this paper, we focus on the Content-Centric Network (CCN) model to provide more efficient, flexible, and reliable data and resource management for fog-based IoT systems. We first propose a Deep Reinforcement Learning (DRL) algorithm that jointly considers the content type and status of fog servers for content-centric data and computation offloading. Then, we introduce a novel virtual layer called FogOrch that orchestrates the management and performance requirements of fog layer resources in an efficient manner via the proposed DRL agent. To show the feasibility of FogOrch, we develop a content-centric data offloading scheme (DRLOS) based on the DRL algorithm running on FogOrch. Through extensive simulations, we evaluate the performance of DRLOS in terms of total reward, computational workload, computation cost, and delay. The results show that the proposed DRLOS is superior to existing benchmark offloading schemes.(c) 2022 Elsevier B.V. All rights reserved.