Dynamic Resource Provisioning With Fault Tolerance for Data-Intensive Meteorological Workflows in Cloud

Cloud computing is a formidable paradigm to provide resources for handling the services from Industrial Internet of Things (IIoT), such as meteorological industry. Generally, the meteorological services, with complex interdependent logics, are modeled as workflows. When any of the computing nodes fo...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial informatics 2020-09, Vol.16 (9), p.6172-6181
Main Authors: Xu, Xiaolong, Mo, Ruichao, Dai, Fei, Lin, Wenmin, Wan, Shaohua, Dou, Wanchun
Format: Article
Language:English
Subjects:
Cloud computing
Computational modeling
Data loss
Dynamic scheduling
Enlargement
Fault tolerance
Fault tolerant systems
Genetic algorithms
Industrial applications
Internet of Things
Load balancing
Meteorological services
meteorological workflow
Mirrors
Network topologies
Nodes
nondominated sorting genetic algorithm (NSGA)-II
Performance degradation
Provisioning
Resource allocation
Sorting algorithms
Task analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Cloud computing is a formidable paradigm to provide resources for handling the services from Industrial Internet of Things (IIoT), such as meteorological industry. Generally, the meteorological services, with complex interdependent logics, are modeled as workflows. When any of the computing nodes for hosting the meteorological workflows fail, all sorts of consequences (e.g., data loss, makespan enlargement, performance degradation, etc.) could arise. Thus recovering the failed tasks as well as optimizing the makespan and the load balance of the computing nodes is still a critical challenge. To address this challenge, a dynamic resource provisioning method (DRPM) with fault tolerance for the data-intensive meteorological workflows is proposed in this article. Technically, the Virtual Layer 2 (VL2) network topology is exploited to build meteorological cloud infrastructure. Then, the nondominated sorting genetic algorithm II (NSGA-II) is employed to minimize the makespan and improve the load balance. Finally, comprehensive experimental analysis of DRPM are proceeded.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2019.2959258