A job scheduling algorithm for delay and performance optimization in fog computing. (26th November 2019)

• Record Type: Journal Article
• Authors: Jamil, Bushra; Shojafar, Mohammad; Ahmed, Israr; Ullah, Atta; Munir, Kashif; Ijaz, Humaira
• Is Part Of: Concurrency and computation. Volume 32:Number 7(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-26
• Subjects: delay -- energy consumption -- fog computing -- Internet of Everything (IoE) -- job scheduling -- sensors; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5581
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Due to an ever‐increasing number of Internet of Everything (IoE) devices, massive amounts of data are produced daily. Cloud computing offers storage, processing, and analysis services for handling of such large quantities of data. The increased latency and bandwidth consumption is not acceptable to real‐time applications like online gaming, smart health, video surveillance, etc. Fog computing has emerged to overcome the increase in latency and bandwidth consumption in Cloud computing. Fog Computing provides storage, processing, networking, and analytical services at the edge of a network. As Fog Computing is still in its infancy, its significant challenges include resource‐allocation and job‐scheduling. The Fog devices at the edge of the network are resource‐constrained. Therefore, it is important to decide the assignment and scheduling of a job on a Fog node. An efficient job scheduling algorithm can reduce energy consumption and response time of an application request. In this paper, we propose a novel Fog computing scheduler that supports service‐provisioning for Internet of Everything, which optimizes delay and network usage. We present a case study to optimally schedule the requests of Internet of Everything devices on Fog devices and efficiently address their demands on available resources on every Fog device. We consider delay and energy consumption as performance metrics and evaluate the proposed scheduling algorithm using iFogSim in comparison with existing approaches. The results show that the delay and network usage of the proposed scheduler improve by 32% and 16%, respectively, in comparison with FCFS approach.