Performance Analysis and Measurement of Geospatial Fog-Cloud Computing System for Data Storage, Visualizations and Analysis

Geospatial Cloud and fog computing have experienced significant growth due to advanced Information and Communication Technology (ICT) that enables geospatial web services. These technologies are known for their robust computing infrastructure, which supports various services for smart city, smart health, smart grid, and smart transportation. To meet Quality of Service (QoS) needs, distributed computing technologies like geospatial cloud and fog computing are used together to provide more specific degrees of granularity. Lately, researchers have been investigating the use of fog in geospatial cloud systems to enhance the efficiency of data storage, visualizations and analysis. This research paper enhances the performance of a Geospatial Fog-Cloud system by doing a thorough investigation and evaluation of the performance of models used for data storage, visualizations and analysis. This work provides a compre-hensive system model and utilizes suitable $M$ / $M$ /1 queueing with balking models to analyze the performance. The effectiveness of the proposed analytical queueing model is demonstrated by simulation experiments conducted using MAPLE 18 tool. These tests analyse multiple scenarios and assess the average number of requests in the system, as well as the projected cycle time that a request spends in the system for different parameters.