Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling Optimization Algorithm
Zeyu Sun, Zhiguo Lv, Huihui Helen Wang, Zhixian Li, Fuqian Jia, Chunxiao Lai · IEEE Access · 2020
In order to addressing the issues of data matching deviation and load imbalance during the data scheduling process of the Internet of Things, Sensing Cloud Computing in IoT: A Novel Data Scheduling Optimization (SCC-DSO) Algorithm is proposed in this paper. First, according to the processing capacity of the IoT working node, a data placement algorithm is designed to reasonably place the input data of the job. Second, the data scheduling queue is optimized based on the data block storage location information to reduce non-local execution of data scheduling. Furthermore, a data prefetching method is designed to pull the data required for non-local data scheduling in advance, and shorten the waiting time of the task for input data. Finally, simulation experiment evaluated by the task localization execution rate and response time is performed. The effectiveness and stability of the algorithm is verified compared with other algorithms.