PQ-Mist: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

Sunil K. Panigrahi, Veena Goswami, Hemant K. Apat, Ganga B. Mund, Himansu Das () and Rabindra K. Barik ()
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Sunil K. Panigrahi: School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India
Veena Goswami: School of Computer Applications, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India
Hemant K. Apat: School of Computer Applications, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India
Ganga B. Mund: School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India
Himansu Das: School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India
Rabindra K. Barik: School of Computer Applications, Kalinga Institute of Industrial Technology, Bhubaneswar 751024, India

Mathematics, 2023, vol. 11, issue 16, 1-21

Abstract: The IoT and cloud environment renders enormous quantities of geospatial information. Fog and mist computing is the scaling technology that handles geospatial data and sends it to the cloud storage system through fog/mist nodes. Installing a mist–cloud–fog system reduces latency and throughput. This mist–cloud–fog system has processed different types of geospatial web services, i.e., web coverage service (WCS), web processing services (WPS), web feature services (WFS), and web map services (WMS). There is an urgent requirement to increase the number of computer devices tailored to deliver high-priority jobs for processing these geospatial web services. This paper proposes a priority-queueing assisted mist–cloud–fog system for efficient resource allocation for high- and low-priority tasks. In this study, WFS is treated as high-priority service, whereas WMS is treated as low-priority service. This system dynamically allocates mist nodes and is determined by the load on the system. In addition to that, the assignment of tasks is determined by priority. Not only does this classify high-priority tasks and low-priority tasks, which helps reduce the amount of delay experienced by high-priority jobs, but it also dynamically allocates mist devices within the network depending on the computation load, which helps reduce the amount of power that is consumed by the network. The findings indicate that the proposed system can achieve a significantly lower delay for higher-priority jobs for more significant rates of task arrival when compared with other related schemes. In addition to this, it offers a technique that is both mathematical and analytical for investigating and assessing the performance of the proposed system. The QoS requirements for each device demand are factored into calculating the number of mist nodes deployed to satisfy those requirements.

Keywords: edge computing; cloud computing; geospatial data; fog computing; mist computing; priority queue; geospatial web services; WMS; WFS (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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