Low-cost cloud computing solution for geo-information processing

Cloud computing has emerged as a leading computing paradigm, with an increasing number of geographic information (geo-information) processing tasks now running on clouds. For this reason, geographic information system/remote sensing (GIS/RS) researchers rent more public clouds or establish more private clouds. However, a large proportion of these clouds are found to be underutilized, since users do not deal with big data every day. The low usage of cloud resources violates the original intention of cloud computing, which is to save resources by improving usage. In this work, a low-cost cloud computing solution was proposed for geo-information processing, especially for temporary processing tasks. The proposed solution adopted a hosted architecture and can be realized based on ordinary computers in a common GIS/RS laboratory. The usefulness and effectiveness of the proposed solution was demonstrated by using big data simplification as a case study. Compared to commercial public clouds and dedicated private clouds, the proposed solution is more low-cost and resource-saving, and is more suitable for GIS/RS applications.