Optimal Geospatial Query Placement in Cloud

Computing resources requirements are increasing with the massive generation of geospatial queries. These queries extract information from a large volume of spatial data. Placement of geospatial queries in virtual machines with minimum resource and energy wastage is a big challenge. Getting query results from mobile locations within a specific time duration is also a major concern. In this work, a bi-objective optimization problem has been formulated to minimize the energy consumption of cloud servers and service processing time. To solve the problem, a crow search based bio-inspired heuristic has been proposed. The proposed algorithm has been compared with traditional First Fit and Best Fit algorithms through simulation, and the obtained results are significantly better than the traditional techniques.