Classified scheduling algorithm of big data under cloud computing

Zhang, Yan

doi:10.1080/1206212x.2017.1397343

Cited by 7 publications

(1 citation statement)

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“…Different file system copy placement strategies are different. For example, HDFS file systems in IoT clusters use rack-aware data copy placement strategies [42], [43]. When the client uploads a file, the file system first places the data block on the node where the client is located, and then uses Pipline to store the second copy on other nodes in the same rack as the node where the client is located.…”

Section: Data Block Placement Methods a Rack-aware Based Date Plmentioning

confidence: 99%

Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling Optimization Algorithm

Sun

et al. 2020

IEEE Access

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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. INDEX TERMS Sensing-cloud computing, Internet of Things, data scheduling, optimization algorithm.

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Section: Data Block Placement Methods a Rack-aware Based Date Plmentioning

confidence: 99%