Abstract
A solution that proactively analyzes the shape of the operator graph of a task based cloud application is studied in this paper. Based on the analysis of the execution graph and operator metadata, the nodes of the execution graph are properly clustered so that highly connected operators are scheduled on the same or nearby computing resources. Two graph partitioning algorithms are studied, implemented and compared. The graph partitioning efficiency is visually analyzed and compared by using existing graph visualization software.
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Bica, M., Gorgan, D. (2018). Data Locality Aware Algorithm for Task Execution on Distributed, Cloud Based Environments. In: Barolli, L., Terzo, O. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Advances in Intelligent Systems and Computing, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-61566-0_51
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DOI: https://doi.org/10.1007/978-3-319-61566-0_51
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