Active Data Replica Recovery for Quality-Assurance Big Data Analysis in IC-IoT

• Published in: IEEE access Vol. 7; pp. 106997 - 107005
• Main Authors: Wang, Songyun, Yuan, Jiabin, Li, Xin, Qian, Zhuzhong, Arena, Fabio, You, Ilsun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bandwidth; Big Data; Big data analysis; Data analysis; Data centers; Data recovery; Fatigue limit; IC-IoT; Intelligent transportation systems; Internet of Things; Nonvolatile memory; NVM; QoS improvement; Quality assurance; Quality of service; Recovery time; Robustness; Smart grid
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2932259

QoS-aware big data analysis is critical in Information-Centric Internet of Things (IC-IoT) system to support various applications like smart city, smart grid, smart health, intelligent transportation systems, and so on. The employment of non-volatile memory (NVM) in cloud or edge system provides good opportunity to improve quality of data analysis tasks. However, we have to face the data recovery problem led by NVM failure due to the limited write endurance. In this paper, we investigate the data recovery problem for QoS guarantee and system robustness, followed by proposing a rarity-aware data recovery algorithm. The core idea is to establish the rarity indicator to evaluate the replica distribution and service requirement comprehensively. With this idea, we give the lost replicas with distinguishing priority and eliminate the unnecessary replicas. Then, the data replicas are recovered stage by stage to guarantee QoS and provide system robustness. From our extensive experiments and simulations, it is shown that the proposed algorithm has significant performance improvement on QoS and robustness than the traditional direct data recovery method. Besides, the algorithm gives an acceptable data recovery time.