Multi-node Scheduling Algorithm Based on Clustering Analysis and Data Partitioning in Emergency Management Cloud
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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- 3.Ashton, K.: That ‘Internet of Things’ Thing. RFiD Journal 22, 97–114 (2009)Google Scholar
- 5.Cheng, Y., Qin, C., Rusu, F.: GLADE: big data analytics made easy. In: Proc. of the 28th International Conference on Management of Data, pp. 697–700 (2012)Google Scholar
- 6.Velev, D., Zlateva, P.: Principles of Cloud Computing Application in Emergency Management. In: Proc. of the International Conference on E-business, Management and Economics, pp. 119–123 (2011)Google Scholar
- 8.Zhang, Y., Huang, G., Liu, X.: Integrating resource consumption and allocation for infrastructure resources on-demand. In: Proc. of the 3rd IEEE International Conference on Cloud Computing, pp. 75–82 (2010)Google Scholar
- 9.Budati, K., Sonnek, J., Chandra, A.: Ridge: combining reliability and performance in open grid platforms. In: Proc. of the 16th International Symposium on High Performance Distributed Computing, pp. 55–64 (2007)Google Scholar
- 11.Kim, H., Parashar, M.: CometCloud: An Autonomic Cloud Engine. Cloud Computing: Principles and Paradigms, 275–297 (2011)Google Scholar
- 12.Chen, Q., Hsu, M., Zeller, H.: Experience in Continuous analytics as a Service (CaaaS). In: Proc. of the 14th ACM International Conference on Extending Database Technology, pp. 509–514 (2011)Google Scholar
- 13.Huang, Y.C., Ho, Y.C., Lu, C.H., et al.: A cloud-based accessible architecture for large-scale adl analysis services. In: Proc. of the 4th IEEE International Conference on Cloud Computing, pp. 646–653 (2011)Google Scholar