Optimization Policy for Different Arrival Modes in IoT Assisted Geospatial Fog Computing Environment

Internet of Things (IoT) has revolutionized our everyday life through efficient resource utilization by minimizing the human efforts involved to enable quicker outcomes and enhanced data collection. In order to do so, IoT devices generate a huge number of tasks which can be broadly categorized as storage-intensive tasks and computationally intensive tasks. Sometimes havoc data related to computational tasks are generated and for faster execution more number of nodes need to be allocated for these types of tasks. As a result of this, Resource Management Strategy in adaptive fog are undertaken which provisions the required resources dynamically to the end-user requests for enhanced benefits. For improving the run time performance of geospatial fog applications, two technical issues must be addressed. The first one is the load balancing of requests among the available resources and second is the provisioning of the resources which optimally adjusts the resources to adapt the time-varying workload. In this paper, we propose a fog framework to process geospatial applications in automation for both kind of tasks such as storage and computational tasks. Various performance measures have also been illustrated and the optimal cost policy is depicted comprising of numerical results.