review-article

Sustainable task offloading decision using genetic algorithm in sensor mobile edge computing

Authors:

Sheuli Chakraborty,

Kaushik MazumdarAuthors Info & Claims

Volume 34, Issue 4

Pages 1552 - 1568

https://doi.org/10.1016/j.jksuci.2022.02.014

Published: 01 April 2022 Publication History

Abstract

Propelling energy-constrained sensor tasks to edge servers in Sensor Mobile Edge Computing (SMEC) subjugates Mobile Devices' (MDs) resource limitation menace. Most of the existing studies focused only on the offloading issues. However, a task may hinge on some allied tasks executed in the prior edge server in the trajectory of MDs. Task execution accomplishes by the assemblage of dependent data. This study imparts the dynamic selection of edge cloud for offloading tasks and checks the task’s dependencies in a multiuser, multichannel environment. The proposed dynamic edge server selection for the inter-edge dependent task scheme piles up data from multiple allied edge nodes to finish the execution. This paper employs a Genetic Algorithm (GA) based optimization technique in the SMEC environment (GAME) to discern the optimal solution. The performance of our proposal is analyzed and compared with the other offloading policies exerting standard datasets. The result of this study manifests with the depletion of energy consumption and computational delay within the allowable range of transmission latency, despite appraising multiple task dependencies.

References

[1]

Md Aazam, Eui-Nam Huh, E-HAMC: Leveraging Fog Computing for Emergency Alert Service, The Fifth International Workshop on Pervasive Networks for Emergency Management, 978-1-4799-8425-1/15/$31.00, IEEE, 2015.

[2]

N.M. Ahmed, N.-E. Rikli, QoS-based data aggregation and resource allocation algorithm for machine type communication devices in next-generation networks, IEEE Access 9 (2021) 119735–119754.

[3]

A.T. Atieh, The next generation cloud technologies: A review on distributed cloud, fog and edge computing and their opportunities and challenges, Available at: ResearchBerg Rev. Sci. Technol. 1 (1) (2021) 1–15. https://researchberg.com/.

[4]

R. Baidya, S.K. Ghosh, B. Debnath, Analysis of parameters for green computing approach using the analytical hierarchy process, in: In 2015 international conference on energy economics and environment (ICEEE) , 2015, pp. 1–4.

[5]

E. Batista, G. Figueiredo, C. Prazeres, Load balancing between fog and cloud in fog of things based platforms through software-defined networking, J. King Saud Univ. – Comput. Inf. Sci. (2021),.

Digital Library

[6]

A. Boukerche, S. Guan, R.E.D. Grande, Sustainable offloading in mobile cloud computing: Algorithmic design and implementation, ACM Comput. Surv. (CSUR) 52 (1) (2019) 1–37.

[7]

B. Cao, Q. Wei, Z. Lv, J. Zhao, A.K. Singh, Many-objective deployment optimization of edge devices for 5G networks, IEEE Trans. Network Sci. Eng. 7 (4) (2020) 2117–2125.

[8]

S. Chakraborty, K. Mazumdar, D. De, CBLM: cluster based location management for small cell network under stochastic environment, J. Circuits Syst. Comput. (2021),.

[9]

B. Chander, S. Pal, D. De, R. Buyya, Artificial intelligence-based internet of things for industry 5.0, in: Artificial Intelligence-based Internet of Things Systems, Springer, Cham, 2022, pp. 3–45.

[10]

P. Chatzimisios, A.C. Boucouvalas, V. Vitsas, IEEE 802.11 Wireless LANs: performance analysis and protocol refinement, EURASIP J. Wireless Commun. Network 2005 (2005) 576368.

[11]

S. Chen, Q. Li, M. Zhou, A. Abusorrah, Recent advances in collaborative scheduling of computing tasks in an edge computing paradigm, Sensors 21 (3) (2021) 779.

[12]

H. Elazhary, Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions, J. Network Comput. Appl. 128 (2019) 105–140.

[13]

S. Guo, B. Xiao, Y. Yang, Y. Yang, Energy-efficient dynamic offloading and resource scheduling in mobile cloud computing, in: 35th Annual IEEE International Conference on Computer Communications, INFOCOM 2016, San Francisco, CA, USA, April 10–14, 2016, IEEE, 2016, pp. 1–9.

[14]

A. Hazra, M. Adhikari, T. Amgoth, S.N. Srirama, Joint computation offloading and scheduling optimization of IoT applications in fog networks, IEEE Trans. Network Sci. Eng. 7 (4) (2020) 3266–3278.

[15]

Y. Hmimz, T. Chanyour, M. El Ghmary, M.O. Cherkaoui Malki, Bi-objective optimization for multi-task offloading in latency and radio resources constrained mobile edge computing networks, Multimedia Tools Appl. 80 (11) (2021) 17129–17166.

[16]

G.J. Ibrahim, T.A. Rashid, M.O. Akinsolu, An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment, J. Parallel Distrib. Comput. 143 (2020) 77–87.

[17]

Y. Jararweh, A. Doulat, O. AlQudah, E. Ahmed, M. Al-Ayyoub, E. Benkhelifa, The future of mobile cloud computing: integrating cloudlets and mobile edge computing, 23rd International Conference on Telecommunications (ICT)., 2016.

[18]

C.-P. Lee, P. Lin, H.-Y. Chen, A protocol to protocol switching mechanism for energy saving of power-constrained in LTE and NBIoT interworking networks, 2018 IEEE conf on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology, Congress on Cybermatics, 2018.

[19]

L. Leng, J. Li, H. Shi, Y. Zhu, Graph convolutional network-based reinforcement learning for tasks offloading in multi-access edge computing, Multimedia Tools Appl. 80 (19) (2021) 29163–29175.

[20]

Z. Li, Q.i. Zhu, Genetic algorithm-based optimization of offloading and resource allocation in mobile-edge computing, Information 11 (2) (2020) 83.

[21]

Z. Lv, D. Chen, R. Lou, Q. Wang, Intelligent edge computing based on machine learning for smart city, Future Gener. Comput. Syst. 115 (2021) 90–99.

[22]

Y. Mao, C. You, J. Zhang, K. Huang, K.B. Letaief, A survey on mobile edge computing: The communication perspective, IEEE Commun. Surv. Tuts. 19 (4) (2017) 2322–2358.

[23]

H. Mazouzi, K. Boussetta, N. Achir, maximizing mobiles energy saving through tasks optimal offloading placement in two-tier cloud, A theoretical and an experimental study, Comput. Commun. 144 (2019) 132–148.

[24]

F. Mehdi, S.M. Kate, The impact of various carbon reduction policies on green flowshop scheduling, Appl. Energy 249 (2019) 300–315.

[25]

A. Oueis, E. Calvanese-Strinati, A. De Domenico, S. Barbarossa, On the impact of backhaul network on distributed cloud computing, in: Wireless Communications and Networking Conference Workshops, WCNCW, 2014 IEEE, IEEE, 2014, pp. 12–17.

[26]

K. Peng, M. Zhu, Y. Zhang, L. Liu, J. Zhang, V.C.M. Leung, L. Zheng, An energy- and cost-aware computation offloading method for workflow applications in mobile edge computing, J. Wireless Com. Network (2019).

[27]

M.R. Raupach, G. Marland, P. Ciais, C. Le Quere, J.G. Canadell, G. Klepper, C.B. Field, Global and regional drivers of accelerating CO2 emissions, Proc. Natl. Acad. Sci. 104 (24) (2007) 10288–10293.

[28]

L. Ren, Y. Laili, X. Li, X. Wang, Coding-based large-scale task assignment for industrial edge intelligence, IEEE Trans. Network Sci. Eng. 7 (4) (2020) 2286–2297.

[29]

N.-E. Rikli, S. Almogari, Efficient priority schemes for the provision of end-to-end quality of service for multimedia traffic over MPLS VPN networks, J. King Saud Univ.-Comput. Inf. Sci. 25 (1) (2013) 89–98.

[30]

A. Sacco, F. Esposito, G. Marchetto, P. Montuschi, Sustainable task offloading in UAV networks via multi-agent reinforcement learning, IEEE Trans. Veh. Technol. 70 (5) (2021) 5003–5015.

[31]

M.H. Shahid, A.R. Hameed, S. ul Islam, H.A. Khattak, I.U. Din, J.J.P.C. Rodrigues, Energy and delay efficient fog computing using caching mechanism, Comput. Commun. 154 (2020) 534–541.

[32]

W. Shi, G. Pallis, Z. Xu, Edge computing, Proc. IEEE 107 (8) (2019).

[33]

M. Srinivas, L.M. Patnaik, Adaptive probabilities of crossover and mutation in genetic algorithms, IEEE Trans. Syst. Man Cybern. 24 (4) (1994) 656–667.

[34]

I. Tinnirello, G. Bianchi, Y. Xiao, Refinements on IEEE 802.11 distributed coordination function modeling approaches, IEEE Trans. Veh. Technol. 59 (3) (2010) 1055–1067.

[35]

Y. Wind, T.L. Saaty, Marketing applications of the analytic hierarchy process, Manage. Sci. 26 (7) (1980) 641–658.

[36]

J.B. Wong, Q. Zhang, Impact of carbon tax on electricity prices and behaviour, Finan. Res. Lett. 44 (2022) 102098.

[37]

L. Wu, R. Zhang, R. Zhou, D. Wu, An edge computing based data detection scheme for traffic light at intersections, Comput. Commun. 176 (2021) 91–98.

[38]

J. Xu, Z. Hao, X. Sun, Optimal offloading decision strategies and their influence analysis of mobile edge computing, Sensors 19 (14) (2019) 3231.

[39]

Z. Xu, W. Liang, W. Xu, M. Jia, S. Guo, Efficient algorithms for capacitated cloudlet placements, IEEE Trans. Parallel Distrib. Syst. 27 (10) (2016) 2866–2880.

[40]

J. Yan, S. Bi, Y.J. Zhang, M. Tao, Optimal task offloading and resource allocation in mobile-edge computing with inter-user task dependency, IEEE Trans. Wireless Commun. 19 (1) (2020) 235–250.

[41]

S. Yang, A joint optimization scheme for task offloading and resource allocation based on edge computing in 5G communication networks, Comput. Commun. 160 (2020) 759–768.

[42]

A. Zeroual, M. Amroune, M. Derdour, A. Bentahar, Lightweight deep learning model to secure authentication in Mobile Cloud Computing, J. King Saud Univ. – Comput. Inf. Sci. (2021).

[43]

W. Zhao, X. Wang, S. Jin, W. Yue, Y. Takahashi, An energy efficient task scheduling strategy in a cloud computing system and its performance evaluation using a two-dimensional continuous time Markov chain model, Electronics 8 (2019) 775,.

Cited By

Ren JYang DYuan YLiu HHao BZhang L(2024)An offloading method in new energy recharging based on GT-DQNJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23399046:1(479-492)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-233990
Liu CWang HZhao MLiu JZhao XYuan P(2024)Dependency-aware online task offloading based on deep reinforcement learning for IoVJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00701-013:1Online publication date: 5-Sep-2024
https://dl.acm.org/doi/10.1186/s13677-024-00701-0
Xu JYe X(2024)Moving edge computing scheduling algorithm based on incentive mechanismProceedings of the 2024 International Conference on Cloud Computing and Big Data10.1145/3695080.3695109(167-171)Online publication date: 26-Jul-2024
https://dl.acm.org/doi/10.1145/3695080.3695109
Show More Cited By

Index Terms

Sustainable task offloading decision using genetic algorithm in sensor mobile edge computing

Index terms have been assigned to the content through auto-classification.

Recommendations

Modelling Task Offloading Mobile Edge Computing
ICCDE '22: Proceedings of the 2022 8th International Conference on Computing and Data Engineering

With the rapid growth of mobile devices (such as smart phones and IoT devices) and the upcoming 5G era, it has been considered that edge computing will play a significant role, which together with the Cloud server forms the Mobile Edge Computing (MEC) ...
A Hybrid GRASP-GA based collaborative task offloading technique in fog computing
Abstract
The transfer of cloud-computing capabilities at the network’s edge reduces the service latency and energy shortage problem in Mobile Fog Computing. Task offloading from resource-constraint devices to the edge servers addresses the limitations of ...
A collaborative computation and dependency-aware task offloading method for vehicular edge computing: a reinforcement learning approach
Abstract
Vehicular edge computing (VEC) is emerging as a new computing paradigm to improve the quality of vehicular services and enhance the capabilities of vehicles. It enables performing tasks with low latency by deploying computing and storage resources ...

Comments

Information & Contributors

Information

Published In

cover image Journal of King Saud University - Computer and Information Sciences

Journal of King Saud University - Computer and Information Sciences Volume 34, Issue 4

Apr 2022

613 pages

ISSN:1319-1578

Issue’s Table of Contents

Copyright © 2022.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 April 2022

Author Tags

Qualifiers

Review-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ren JYang DYuan YLiu HHao BZhang L(2024)An offloading method in new energy recharging based on GT-DQNJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23399046:1(479-492)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/JIFS-233990
Liu CWang HZhao MLiu JZhao XYuan P(2024)Dependency-aware online task offloading based on deep reinforcement learning for IoVJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00701-013:1Online publication date: 5-Sep-2024
https://dl.acm.org/doi/10.1186/s13677-024-00701-0
Xu JYe X(2024)Moving edge computing scheduling algorithm based on incentive mechanismProceedings of the 2024 International Conference on Cloud Computing and Big Data10.1145/3695080.3695109(167-171)Online publication date: 26-Jul-2024
https://dl.acm.org/doi/10.1145/3695080.3695109
Mondal AChatterjee PRay N(2024)An Optimal Novel Approach for Dynamic Energy-Efficient Task Offloading in Mobile Edge-Cloud Computing NetworksSN Computer Science10.1007/s42979-024-02992-15:5Online publication date: 15-Jun-2024
https://dl.acm.org/doi/10.1007/s42979-024-02992-1
Khan SJiangbin ZAli H(2024)Soft computing approaches for dynamic multi-objective evaluation of computational offloading: a literature reviewCluster Computing10.1007/s10586-024-04543-y27:9(12459-12481)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10586-024-04543-y
Chakraborty SDe DMazumdar K(2023)DoME: Dew computing based microservice execution in mobile edge using Q-learningApplied Intelligence10.1007/s10489-022-04087-x53:9(10917-10936)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1007/s10489-022-04087-x
Wang BWei J(2023)Particle Swarm Optimization with Genetic Evolution for Task Offloading in Device-Edge-Cloud Collaborative ComputingAdvanced Intelligent Computing Technology and Applications10.1007/978-981-99-4761-4_29(340-350)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1007/978-981-99-4761-4_29
Zhang RShu HNavaei Y(2022)Load Balancing in Edge Computing Using Integer Linear Programming Based Genetic Algorithm and Multilevel Control ApproachWireless Communications & Mobile Computing10.1155/2022/61252462022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/6125246

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents