Abstract
The spread of the sensors and industrial systems has fostered widespread real-time data processing applications. Massive vector field data (MVFD) are generated by vast distributed sensors and are characterized by high distribution, high velocity, and high volume. As a result, computing such kind of data on centralized cloud faces unprecedented challenges, especially on the processing delay due to the distance between the data source and the cloud. Taking advantages of data source proximity and vast distribution, edge computing is ideal for timely computing on MVFD. Therefore, we are motivated to propose an edge computing based MVFD processing framework. In particular, we notice that the high volume feature of MVFD results in high data transmission delay. To solve this problem, we invent Data Fluidization Schedule (DFS) in our framework to reduce the data block volume and the latency on Input/Output (I/O). We evaluated the efficiency of our framework in a practical application on massive wind field data processing for cyclone recognition. The high efficiency our framework was verified by the fact that it significantly outperformed classical big data processing frameworks Spark and MapReduce.
Highlights
IntroductionThe fluid presents different states in the space
The space occupied by fluid motion is called the flow field
We propose an edge computing empowered massive vector field data (MVFD) processing framework
Summary
The fluid presents different states in the space These states can be collected by sensors and expressed as vector field data. With the continuous expansion and growth of data, the real-time application of massive vector field data (MVFD) is gradually facing various challenges. Targeting at the above challenges, in this paper, we are motivated to investigate how to efficiently use the edge computing resources to process MVFD. We propose an edge computing empowered MVFD processing framework. We invent Data Fluidization Schedule (DFS) strategy, aiming at fine-scale data partitioning, dataflow transmission and computing scheduling so as to ensure the real-time MVFD processing. The rest of paper is organized as follows: Section 2 introduces the related studies on massive vector data processing and edge computing.
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