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A New Energy Aware Task Consolidation Scheme for Geospatial Big Data Application in Mist Computing Environment

The Internet of Spatial Things (IoST) are expanding rapidly for geospatial big data applications in today's digital world. A large volume of geospatial data is produced between IoST and the mist assisted cloud environment. Mist Computing is a leveraging technology that stands extremely nearer to the edge of the network where geospatial big data are processing and then transferring to the cloud storage system via the fog nodes. By implementing this mist assisted cloud infrastructure, it has the greatest capabilities of reducing latency and traffic, respectively. Because of this unique model, it has a high requirement in smart geospatial healthcare applications, smart cities, geospatial location analytics and intelligent transportation system. Microcomputers are used at the mist layer in the mist assisted cloud network. Task allocation to the microcomputers in the mist servers is an NP-hard problem. The present research paper suggests a new energy aware task consolidation scheme by reducing the unused microcomputers in the mist layer of mist assisted cloud computing environment. It employs the metaphor-less Rao-1 algorithm and studies the behaviour of the algorithm with earlier evolutionary algorithms. It also reveals better performance in terms of maximizing CPU utilization.