LFPR: A Lazy Fast Predictive Repair Strategy for Mobile Distributed Erasure Coded Cluster

Yu Wu, Duo Liu, Yujuan Tan, Moming Duan, Longpan Luo, Weilve Wang, Xianzhang Chen

Abstract: Mobile distributed erasure coded Internet of Things (IoT) clusters store popular data, reducing communication latency, and ensuring data reliability while requiring low storage overhead. However, it suffers a high repair overhead to ensure data reliability and availability due to mobile device failures or leaving the cluster. Predictive repair is an effective strategy for reducing repair overhead that has gained attention with the development in accurate failure and mobile node movement trajectory prediction technologies in recent years. We propose LFPR, a hybrid lazy fast predictive repair strategy that combines two baseline predictive repair approaches (reconstruction and migration), including LFPRH and LFPRC for a hot and cold data distributed cluster, respectively. LFPRC and LFPRH adopt different mechanisms to determine whether a block should perform predictive repair immediately. The predictive repair mechanisms of LFPR couples migration and reconstruction in parallel to reduce average repair time per block. LFPR significantly reduces average repair time per block via large-scale simulation and local cluster experiments, compared with existing predictive repair solutions, such as FastPR and the two baselines.