Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm
Main Article Content
Abstract
The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. Existing research used metaheuristic algorithm to solve tak scheduling problem, however, must of the existing metaheuristics used suffers from falling into local mina due to their inefficiency to avoid unfeasible region in the solution search space. Therefore, there is a dire need for an efficient metaheuristic algorithm for task scheduling. This study proposed an FPA-ISFLA task scheduling model using hybrid flower pollination and improved shuffled frog leaping algorithms. The simulation results indicate that the FPA-ISFLA algorithm is superior to the PSO algorithm in terms of makespan time, resource utilization, and execution cost reduction, especially with an increasing number of tasks.
Received 31/10/2023
Revised 10/02/2024
Accepted 12/02/2024
Published 25/02/2024
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Park J, Chung K. Resource prediction‐based edge collaboration scheme for improving qoe. Sensors. 2021;21(24):1–15. https://doi.org/10.3390/s21248500.
Mayyahi MAAL, Seno SAH. A Security and Privacy Aware Computing Approach on Data Sharing in Cloud Environment. Baghdad Sci J. 2022;19(6):1572–1580. https://doi.org/10.21123/bsj.2022.7077.
Goel G, Tiwari R, Koundal D, Upadhyay S. Analysis ofResource Scheduling algorithms for optimization in IoTFog- Cloud System. CEUR Workshop Proc. 2021;305(8):1–8.
Abo-Alsabeh R, Daham HA, Salhi A. A Heuristic Approach to the Consecutive Ones Submatrix Problem. Baghdad Sci J. 2023;20(1):189–95. https://doi.org/10.21123/bsj.2022.6373.
Jayasena KPN, Thisarasinghe BS. Optimized task scheduling on fog computing environment using meta heuristic algorithms. In: Proceedings - 4th IEEE International Conference on Smart Cloud, SmartCloud 2019 and 3rd International Symposium on Reinforcement Learning, ISRL 2019. Institute of Electrical and Electronics Engineers Inc.; 2019.23(2): 53–8. https://doi.org/10.1109/SmartCloud.2019.00019.
Gabi D, Dankolo NM, Muslim AA, Abraham A, Joda MU, Zainal A, et al. Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme. Neural Comput Appl . 2022; 12(3): 1-21. https://doi.org/10.1007/s00521-022-07260-y
Orive A, Agirre A, Truong HL, Sarachaga I, Marcos M. Quality of Service Aware Orchestration for Cloud-Edge Continuum Applications. Sensors. 2022; 23(1): 1-21. https://doi.org/10.3390/s22051755
Jiang X, Sha T, Liu D, Chen J, Chen C, Huang K. Flexible and Dynamic Scheduling of Mixed-Criticality Systems. Sensors. 2022; 18(4): 1-18. https://doi.org/10.3390/s22197528
Kaur N, Kumar A, Kumar R. A systematic review on task scheduling in Fog computing: Taxonomy, tools, challenges, and future directions. Concurr Comput Pract Exp. 2021;33(21): 1-18. https://doi.org/10.1002/cpe.6432
Swarup S, Shakshuki EM, Yasar A. Task scheduling in cloud using deep reinforcement learning. Procedia Comput Sci. 2021;18(4): 42–51. https://doi.org/10.1016/j.procs.2021.03.016
Yang X, Rahmani N. Task scheduling mechanisms in fog computing: review, trends, and perspectives. Kybernetes. 2021;50(1):22–38. https://doi.org/10.1108/K-10-2019-0666.
Gabi D, Ismail AS, Zainal A, Zakaria Z, Abraham A, Dankolo NM. Cloud customers service selection scheme based on improved conventional cat swarm optimization. Neural Comput Appl. 2020;32(18):17–38. https://doi.org/10.1007/s00521-020-04834-6
Abdullahi M, Ngadi MA, Dishing SI, Abdulhamid SM, Ahmad BI eel. An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in cloud computing environment. J Netw Comput Appl. 2019;133(July 2018):60–74. https://doi.org/10.1016/j.jnca.2019.02.005
R SK, Lakshmi J. QoS aware FaaS for Heterogeneous Edge-Cloud continuum; QoS aware FaaS for Heterogeneous Edge-Cloud continuum. 2022 IEEE 15th Int Conf Cloud Comput. 2022; 70-80. https://doi.org/10.1109/CLOUD55607.2022.00023
Chellapraba B, Manohari D, Periyakaruppan K, Kavitha MS. Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment. CSSE.2023; 45(1): 483-495. https://doi.org/10.32604/csse.2023.029854
Attiya I, Abualigah L, Alshathri S, Elsadek D, Elaziz MA. Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling. Mathematics. 2022;10(11): 18-94. https://doi.org/10.3390/math10111894
Attiya I, Abualigah L, Elsadek D, Chelloug SA, Abd Elaziz M. An Intelligent Chimp Optimizer for Scheduling of IoT Application Tasks in Fog Computing. Mathematics. 2022;10(7):1–18. https://doi.org/10.3390/math10071100
Karaja M, Chaabani A, Azzouz A, Ben Said L. Efficient bi-level multi objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment. Appl Intell. 2022; 53: 9009–9037. https://doi.org/10.1007/s10489-022-03942-1
Brogi A, Forti S. QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J. 2017;4(5):85–92. https://doi.org/10.1109/JIOT.2017.2701408
Baresi L, Mendonça DF, Garriga M, Guinea S, Quattrocchi G. A unified model for the mobile-edge-cloud continuum. ACM Trans Internet Technol. 2019;19(2): 1–21.https://doi.org/10.1145/3226644
Abdel-Basset M, El-Shahat D, Elhoseny M, Song H. Energy-Aware Metaheuristic Algorithm for Industrial-Internet-of-Things Task Scheduling Problems in Fog Computing Applications. IEEE Internet Things J. 2021;8(16):12638–49. https://doi.org/10.1109/JIOT.2020.3012617
Yang XS. Flower Pollination Algorithm for Global Optimization. 2013; 74(45): 240-249..
Pavlyukevich I. Levy flights, non-local search and simulated annealing. J Comput Phys. 2007;226(2):30–44. https://doi.org/10.1016/j.jcp.2007.06.008
Tang D, Zhao J, Yang J, Liu Z, Cai Y. An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications. Comput Intell Neurosci. 2021;202(1): 1-21. https://doi.org/10.1155/2021/8928182
Bhattacharjee KK, Sarmah SP. Shuffled frog leaping algorithm and its application to 0/1 knapsack problem. Appl Soft Comput J. 2014;19(2):52–63. http://dx.doi.org/10.1016/j.asoc.2014.02.010
Wang Z, Zhang D, Wang B, Chen W. Research on improved strategy of shuffled frog leaping algorithm. Proc - 2019 34rd Youth Acad Annu Conf Chinese Assoc Autom YAC 2019. 2019;2(6): 1–8. https://doi.org/10.1109/YAC.2019.8787721
Jaballah S, Rouis K, Abdallah F Ben, Tahar JBH. An improved Shuffled Frog Leaping Algorithm with a fast search strategy for optimization problems. Proc - 2014 IEEE 10th Int Conf Intell Comput Commun Process ICCP 2014. 2014;23(7): 23-27. http://dx.doi.org/10.1109/ICCP.2014.6936975
Liping Z, Weiwei W, Yi H, Yefeng X, Yixian C. Application of Shuffled Frog Leaping Algorithm to an Uncapacitated SLLS Problem. AASRI Procedia . 2012;1(2): 26–31. http://dx.doi.org/10.1016/j.aasri.2012.06.035
Nabi S, Ahmad M, Ibrahim M, Hamam H. AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing. Sensors. 2022;22(3):1–22. https://doi.org/10.3390/s22030920