Intelligent use of fog devices in edge‐cloud paradigm to assist in E‐polling. (3rd September 2018)

• Record Type: Journal Article
• Authors: Khalid, Adnan; Shahbaz, Muhammad; Khan, Imran Ahmed
• Other Names: Shojafar Mohammad guestEditor.; Pooranian Zahra guestEditor.; Sookhak Mehdi guestEditor.; Buyya Rajkumar guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 8(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-09-03
• Subjects: cloud computing -- computer networks -- data communication -- distributed computing -- edge computing -- E‐electoral system -- fog computing -- grid computing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4957
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Internet is playing a vital role in providing information to billions of people around the world. It is the fastest way to communicate information worldwide. Over the years, information technology has provided solutions to many problems in different domains of life. With the advent of cloud computing and the internet of things (IOT), most of these domains are now employing cloud networks to save resources and money. Cloud provides a platform to store and process information with minimal resources and investment. Due to the vast geographical distribution and large distances between the cloud networks and the end user nodes, transmission of data over cloud networks provides problems of latency and delay that can cause major hazards in applications that require real‐time processing. In order to analyze the issues related to slow/no response from Clouds, a case study based on a mock E‐poll held under the Election commission of Pakistan is analyzed, and a remedy to the latency experienced during the polling process has been suggested. It involves the use of the newly proposed Fog Computing Paradigm to allow fast processing of real‐time data. This paper provides a solution to latency issues in cloud networks by proposing an intelligent way of identifying which data can be processed on close‐proximity Fog networks and which can be sent to the cloud for bulk processing. The aim is to reduce the time consumed for real‐time processing over the traditional cloud setup.