An Optimal Scheduling Method in IoT-Fog-Cloud Network Using Combination of Aquila Optimizer and African Vultures Optimization

被引：22

作者：

Liu, Qing ^{[1
]}

Kosarirad, Houman ^{[2
]}

Meisami, Sajad ^{[3
]}

Alnowibet, Khalid A. ^{[4
]}

Hoshyar, Azadeh Noori ^{[5
]}

机构：

[1] Chongqing Creat Vocat Coll, Sch Artificial Intelligence, Chongqing 402160, Peoples R China

[2] Univ Nebraska Lincoln, Durham Sch Architectural Engn & Construct, 122 NH, Lincoln, NE 68588 USA

[3] Illinois Inst Technol, Dept Comp Sci, Chicago, IL 60616 USA

[4] King Saud Univ, Coll Sci, Stat & Operat Res Dept, Riyadh 11451, Saudi Arabia

[5] Federat Univ Australia, Inst Innovat Sci & Sustainabil, Brisbane, Qld 4000, Australia

来源：

PROCESSES | 2023年 / 11卷 / 04期

关键词：

Aquila Optimizer; African Vultures Optimization Algorithm; task scheduling; fog computing; cloud computing; Internet of Things; OBJECTIVE DEPLOYMENT OPTIMIZATION; RESOURCE-ALLOCATION; INTERNET; THINGS;

D O I：

10.3390/pr11041162

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Today, fog and cloud computing environments can be used to further develop the Internet of Things (IoT). In such environments, task scheduling is very efficient for executing user requests, and the optimal scheduling of IoT task requests increases the productivity of the IoT-fog-cloud system. In this paper, a hybrid meta-heuristic (MH) algorithm is developed to schedule the IoT requests in IoT-fog-cloud networks using the Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) called AO_AVOA. In AO_AVOA, the exploration phase of AVOA is improved by using AO operators to obtain the best solution during the process of finding the optimal scheduling solution. A comparison between AO_AVOA and methods of AVOA, AO, Firefly Algorithm (FA), particle swarm optimization (PSO), and Harris Hawks Optimization (HHO) according to performance metrics such as makespan and throughput shows the high ability of AO_AVOA to solve the scheduling problem in IoT-fog-cloud networks.

引用

页数：18

共 58 条

[21] Harris hawks optimization: Algorithm and applications [J].

Heidari, Ali Asghar ;

Mirjalili, Seyedali ;

Faris, Hossam ;

Aljarah, Ibrahim ;

Mafarja, Majdi ;

Chen, Huiling .

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2019, 97 :849-872

[22] HunterPlus: AI based energy-efficient task scheduling for cloud-fog computing environments [J].

Iftikhar, Sundas ;

Ahmad, Mirza Mohammad Mufleh ;

Tuli, Shreshth ;

Chowdhury, Deepraj ;

Xu, Minxian ;

Gill, Sukhpal Singh ;

Uhlig, Steve .

INTERNET OF THINGS, 2023, 21

[23]

Jacob L., 2014, INT J RES APPL SCI E, V2, P53

[24] An improved discrete harris hawk optimization algorithm for efficient workflow scheduling in multi-fog computing [J].

Javaheri, Danial ;

Gorgin, Saeid ;

Lee, Jeong-A. ;

Masdari, Mohammad .

SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS, 2022, 36

[25] GA-Based Customer-Conscious Resource Allocation and Task Scheduling in Multi-cloud Computing [J].

Jena, Tamanna ;

Mohanty, J. R. .

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2018, 43 (08) :4115-4130

[26]

Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968

[27] Optimal caching scheme in D2D networks with multiple robot helpers [J].

Lin, Yu ;

Song, Hui ;

Ke, Feng ;

Yan, Weizhao ;

Liu, Zhikai ;

Cai, Faming .

COMPUTER COMMUNICATIONS, 2022, 181 :132-142

[28] A scheduling algorithm based on reinforcement learning for heterogeneous environments [J].

Lin, Ziniu ;

Li, Chen ;

Tian, Lihua ;

Zhang, Bin .

APPLIED SOFT COMPUTING, 2022, 130

[29] A survey and classification of the workload forecasting methods in cloud computing [J].

Masdari, Mohammad ;

Khoshnevis, Afsane .

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2020, 23 (04) :2399-2424

[30]

Meisami S, 2021, Arxiv, DOI arXiv:2109.14812

← 1 2 3 4 5 6 →