Multi-node Scheduling Algorithm Based on Clustering Analysis and Data Partitioning in Emergency Management Cloud
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Real-time processing is a key problem for big data analysis and processing, especially in emergency management. Strongly promoted by the leading industrial companies, cloud computing becomes increasingly popular tool for emergency management, that is emergency management cloud. How to make optimal deployment of emergency management cloud applications is a challenging research problem. The paper proposes a multi-node scheduling algorithm based on clustering analysis and data partitioning in emergency management cloud. First, the presented method divides the cloud nodes into clusters according to the communication cost between different nodes, and then selects a cluster for the big data analysis services. Second, the load balancing theory is used to dispatch big data analysis to these computing nodes in a way to enable synchronized completion at best-effort performance. At last, to improve the real-time of big data analysis, the paper presents a multi-node scheduling algorithm based on game theory to find optimal scheduling strategy for each scheduling node. Experimental results show the effectiveness of our scheduling algorithm for big data analytics in emergency management.
KeywordsBig data multi-node scheduling emergency management cloud
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