文献类型：期刊文献

英文题名：Dynamic Checkpointing for Heterogeneous IoT Devices Through Self-Referencing

作者：Wang, Nan[1]; Wang, Ziyi[1]; Lu, Lijun[1]; Ma, Zhiyuan[2]; Chao, Qun[3]

机构：[1] East China University of Science and Technology, School of Information Science and Engineering, Shanghai, China; [2] University of Shanghai for Science and Technology, Institute of Machine Intelligence, Shanghai, China; [3] Shanghai Jiao Tong University, School of Mechanical Engineering, Shanghai, China

年份：2024

起止页码：343

外文期刊名：Proceedings - 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

收录：EI(收录号：20251218077697)

语种：英文

外文关键词：Problem solving

摘要：Failure recovery is one of the most essential problems in Internet of Things (IoT) systems, and the conventional snapshot method is an effective way to solve this problem. However, snapshot methods lack specialized designs for heterogeneous IoT devices, and when implemented in edge devices, serious system interruptions occur and performance is impacted. To address these problems, a dynamic checkpointing strategy is proposed for IoT systems that consist of heterogeneous devices. Firstly, an anomaly detection network for snapshots (i.e., ADSnet) that combines long short-term memory networks with multilayer convolutional networks is used to learn the multidimensional features of system resource usage. Secondly, ADSnet is tuned during deployment to learn the behaviors of target devices, so that ADSnet can report the anomalies of target devices in the near future. Finally, a dynamic checkpointing strategy is proposed to dynamically create snapshots on the basis of the anomaly detection results. The experimental results show that the proposed ADSnet achieves 97.73% accuracy in detecting anomalies in the target device; furthermore, our proposed dynamic checkpointing strategy reduces 25.4% snapshots than that created by the recently proposed ResCheck. ? 2024 IEEE.