Resource provisioning in collaborative fog computing for multiple delay‐sensitive users. (1st June 2021)

• Record Type: Journal Article
• Authors: Lu, Shuaibing; Wu, Jie; Wang, Ning; Duan, Yubin; Liu, Haiming; Zhang, Jiayue; Fang, Juan
• Is Part Of: Software, practice & experience. Volume 53:Number 2(2023)
• Page Start: 243
• Page End: 262
• Publication Date: 2021-06-01
• Subjects: collaborative fog computing -- cost‐efficiency -- delay‐sensitive users -- resource provisioning; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.3000
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: Fog computing is an emerging paradigm that supplies storage, computation, and networking resources between traditional cloud data centers and end devices. This article focuses on the resource provisioning problem in collaborative fog computing for multiple delay‐sensitive users. Our goal is to implement a resource provisioning strategy for network operators to minimize the total monetary cost by considering the deadline and capacity constraints. Two scenarios are considered: unlimited‐processor fog nodes (UPFN) and limited‐processor fog nodes (LPFN). In either scenario, we prove that the resource provisioning problem is NP‐hard. First, we consider the UPFN scenario that the processors of fog nodes are unlimited and users' requests can be ideally processed in parallel. Two algorithms are proposed which greedily delete fog nodes based on the local or global collaborative influences until there is no feasible provisioning to guarantee the deadline of users. Then we extend the resource provisioning problem to a more realistic and complicated scenario LPFN in which the scheduling delay cannot be ignored. Two types of tasks are considered. One is the arbitrarily divided tasks, and a near‐optimal solution bounded by 8 3 OPT + ϵ 2 8 m α has been found. m is the number of fog nodes, and α is the upper bound on the Lipschitz constant of the delay function. Another one is the application‐driven tasks, and we propose a heuristic algorithm. Extensive experiments validate the efficiency of the proposed algorithms.