Recent Developments in Parallel and Distributed Computing for Remotely Sensed Big Data Processing

This article gives a survey of state-of-the-art methods for processing remotely sensed big data and thoroughly investigates existing parallel implementations on diverse popular high-performance computing platforms. The pros/cons of these approaches are discussed in terms of capability, scalability,...

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Vydáno v:Proceedings of the IEEE Ročník 109; číslo 8; s. 1282 - 1305
Hlavní autoři: Wu, Zebin, Sun, Jin, Zhang, Yi, Wei, Zhihui, Chanussot, Jocelyn
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Témata:
Algorithms
Big Data
Cloud computing
Computer networks
Data processing
Distributed computing
Distributed databases
Distributed processing
Engineering Sciences
parallel and distributed processing
Parallel processing
Processor scheduling
Remote sensing
Scheduling
Sensors
Signal and Image processing
task scheduling
parallel and distributed processing
remote sensing
Big data
task scheduling
cloud computing
ISSN:0018-9219, 1558-2256
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Shrnutí:This article gives a survey of state-of-the-art methods for processing remotely sensed big data and thoroughly investigates existing parallel implementations on diverse popular high-performance computing platforms. The pros/cons of these approaches are discussed in terms of capability, scalability, reliability, and ease of use. Among existing distributed computing platforms, cloud computing is currently the most promising solution to efficient and scalable processing of remotely sensed big data due to its advanced capabilities for high-performance and service-oriented computing. We further provide an in-depth analysis of state-of-the-art cloud implementations that seek for exploiting the parallelism of distributed processing of remotely sensed big data. In particular, we study a series of scheduling algorithms (GSs) aimed at distributing the computation load across multiple cloud computing resources in an optimized manner. We conduct a thorough review of different GSs and reveal the significance of employing scheduling strategies to fully exploit parallelism during the remotely sensed big data processing flow. We present a case study on large-scale remote sensing datasets to evaluate the parallel and distributed approaches and algorithms. Evaluation results demonstrate the advanced capabilities of cloud computing in processing remotely sensed big data and the improvements in computational efficiency obtained by employing scheduling strategies.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2021.3087029