Abstract
The growth of the Internet of Things (IoT) and its application in various fields has resulted in the generation of significant amounts of data for processing. The Hybrid Cloud–Fog architecture makes it possible to send latency-sensitive tasks to Fog resources because of its proximity to IoT devices. More complex tasks should be sent to the Cloud data center because of its computational power and storage capacity. The scheduling of requests to optimize latency and energy consumption is a major challenge and an NP-hard problem in Cloud–Fog computing. In this study, first, we define a new multi-objective function to make a good trade-off between latency and energy. Then, to optimize this multi-objective function, we propose a Multi Objectives Cuckoo Search algorithm (MOCS) algorithm, improved by Boltzmann function, to reduce the latency and energy consumption. The simulation result proves that the improved MOCS algorithm is more effective in optimizing latency and energy consumption compared to the state-of-the-art algorithms. Using Evaluation Criteria indicates that the improved MOCS, based on the Boltzmann function, enhances its exploration and exploitation capabilities.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
Enquiries about data availability should be directed to the authors.
References
Abbasi M, Mohammadi Pasand E, Khosravi M (2020) Workload allocation in IoT-Fog-Cloud architecture using a multi-objective genetic algorithm. J Grid Comput 43(56):1–18. https://doi.org/10.1007/s10723-020-09507-1
Aburukba RO, Landolsi T, Omer D (2021) A heuristic scheduling approach for fog-cloud computing environment with stationary iot devices. J Netw Comput Appl 180(102):994
Akbarpour A, Pourreza-Bilondi M, Zeynali M (2020) Compression of novel meta-heuristic algorithms for multi-objective optimization of water resources system. Amirkabir J Civ Eng 52(8):1–14. https://doi.org/10.22060/ceej
Al Ridhawi I, Aloqaily M, Kotb Y et al (2018) A collaborative mobile edge computing and user solution for service composition in 5G systems. Trans Emerg Telecommun Technol 29(11):3446. https://doi.org/10.1002/ett.3446
Back T (1994) Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. In: Proceedings of the first IEEE conference on evolutionary computation. IEEE World Congress on computational intelligence, vol 1, pp 57–62. https://doi.org/10.1109/ICEC.1994.350042
Bala A, Ismail I, Ibrahim R et al (2019) Prediction using cuckoo search optimized echo state network. IEEE Trans Ind Inf 44(11):9769–9778. https://doi.org/10.1007/s13369-019-04008-0
Bitam S, Zeadally S, Mellouk A (2018) Fog computing job scheduling optimization based on bees swarm. Enterprise Inf Syst 12(4):373–397. https://doi.org/10.1080/17517575.2017.1304579
Brown CT, Liebovitch LS, Glendon R (2007) Lévy flights in Dobe Ju/’Hoansi foraging patterns. Human Ecolo 38(1):129–138. https://doi.org/10.1007/s10745-006-9083-4
Chang KD, Chen CY, Chen JL et al (2011) Internet of things and Cloud computing for future internet. Springer 223(04):82–97. https://doi.org/10.9734/cjast/2020/v39i3431039
D’Angelo G, Palmieri F (2023) A co-evolutionary genetic algorithm for robust and balanced controller placement in software-defined networks. J Netw Comput Appl 212(103):583. https://doi.org/10.1016/j.jnca.2023.103583
D’Angelo G, Della-Morte D, Pastore D et al (2023) Identifying patterns in multiple biomarkers to diagnose diabetic foot using an explainable genetic programming-based approach. Future Gener Comput Syst 140:138–150. https://doi.org/10.1016/j.future.2022.10.019
Ebrahimi Mood S, Rashedi E, Javidi MM (2015) New functions for mass calculation in gravitational search algorithm. J Comput Secur 2(3):233–246. https://jcomsec.ui.ac.ir/article_21888.html
Gandomi AH, Yang XS, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35. https://doi.org/10.1007/s00366-011-0241-y
Hoseiny F, Azizi S, Dabiri S (2020) Using the power of two choices for real-time task scheduling in Fog-Cloud computing. In: 2020 4th International Conference on Smart City, Internet of Things and Applications (SCIOT), pp 18–23, https://doi.org/10.1109/SCIOT50840.2020.9250197
Hosseini Shirvani M, Ramzanpoor Y (2023) Multi-objective QoS-aware optimization for deployment of IoT applications on cloud and fog computing infrastructure. Neural Comput Appl 35:1–46. https://doi.org/10.1007/s00521-023-08759-8
Javanmardi S, Shojafar M, Persico V et al (2021) FPFTS: a joint Fuzzy Particle swarm optimization mobility-aware approach to Fog Task Scheduling algorithm for internet of things devices. Softw Pract Exp 51(12):2519–2539. https://doi.org/10.1002/spe.2867
Jeretta HN, Alex K, Joanna P (2019) The internet of things: review and theoretical framework. Expert Syst Appl 133:97–108. https://doi.org/10.1016/j.eswa.2019.05.014
Mageed ZS, Rowaida KI, Mohammed AMS (2020) Unified ontology implementation of Cloud computing for distributed systems. Curr J Appl Sci Technol 39(34):82–97. https://doi.org/10.9734/cjast/2020/v39i3431039
Maher A (2015) IoT, from cloud to Fog computing. cisco blog
Mahmud R, Kotagiri R, Buyya R (2018) Fog computing: a taxonomy, survey and future directions. In: Di Martino B, Li KC, Yang LT, et al (eds) Internet of everything: algorithms, methodologies, technologies and perspectives. Springer, Singapore, pp 103–130, https://doi.org/10.1007/978-981-10-5861-5_5
Marcelo Y, Rodolfo A. M, René S, et al (2014) Key ingredients in an IoT recipe: Fog computing, Cloud computing,and more fog computing. In: 2014 19th IEEE international workshop on computer aided modeling and design of communication links and networks, CAMAD 2014, Athens, Greece, December 1-3. IEEE, pp 325–329. https://doi.org/10.1109/CAMAD.2014.7033259
Meng X, Chang J, Wang X et al (2019) Multi-objective hydropower station operation using an improved cuckoo search algorithm. Energy 168:425–439. https://doi.org/10.1016/j.energy.2018.11.096
Mishra PK, Chaturvedi AK (2023) State-of-the-art and research challenges in task scheduling and resource allocation methods for cloud–fog environment. In: 2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT), IEEE, pp 1–5
Mishra SK, Puthal D, Rodrigues JJPC et al (2018) Sustainable service allocation using a metaheuristic technique in a Fog server for industrial applications. IEEE Trans Ind Inf 14(10):4497–4506. https://doi.org/10.1109/TII.2018.2791619
Pavel A, Subhi RMZ, Hanan S et al (2020) HRM system usingcloud computing for small and medium enterprises (SMEs). Technol Rep Kansai Univ 62(04):82–97. https://doi.org/10.9734/cjast/2020/v39i3431039
Prem Jacob T, Pradeep K (2019) A multi-objective optimal task scheduling in Cloud environment using cuckoo Particle Swarm Optimization. Wirel Personal Commun 109(1):315–331. https://doi.org/10.1007/s11277-019-06566-w
Priyanshu S, Rizwan K (2018) A review paper on Cloud computing. Int J Adv Res Comput Sci Softw Eng 8(6):17–20. https://doi.org/10.23956/ijarcsse.v8i6.711
Raafat OA, Mazin A, Taha L et al (2020) Scheduling internet of things requests to minimize latency in hybrid Fog-Cloud computing. Future Gener Comput Syst 111:539–551. https://doi.org/10.1016/j.future.2019.09.039
Rodríguez-Fdez I, Canosa A, Mucientes M, et al (2015) STAC: A web platform for the comparison of algorithms using statistical tests. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp 1–8. https://doi.org/10.1109/FUZZ-IEEE.2015.7337889
Sadeeq M, Abdulkareem NM, Zeebaree SRM et al (2021) IoT and Cloud computing issues, challenges and opportunities: a review. Qubahan Acad J 1(2):1–7. https://doi.org/10.48161/qaj.v1n2a36
Shafique K, Khawaja BA, Sabir F, et al (2020) Internet of things (IoT) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5G-IoT scenarios. IEEE Access 8:23,022–23,040. https://doi.org/10.1109/ACCESS.2020.2970118
Verma H, Kumar Y (2021) A survey on cuckoo search algorithm for optimization problems. TechRxiv. https://doi.org/10.36227/techrxiv.14199221.v1
Wang J, Li D (2019) Task scheduling based on a hybrid heuristic algorithm for smart production line with fog computing. Sensors 19(5):1023. https://doi.org/10.3390/s19051023
Yang M, Ma H, Wei S, et al (2020) A multi-objective task scheduling method for Fog computing in Cyber-Physical-Social Services. IEEE Access 8:65,085–65,095. https://doi.org/10.1109/ACCESS.2020.2983742
Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624. https://doi.org/10.1016/j.cor.2011.09.026
Yousef A (2019) A Fog computing based architecture for IoT services and applications development. Int J Comput Trends Technol. arXiv:1911.02403
Acknowledgements
The authors appreciate the insightful comments from the referees, which have greatly improved the paper.
Funding
The authors have not disclosed any funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies involving human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
BahraniPour, F., Ebrahimi Mood, S. & Farshi, M. Energy-delay aware request scheduling in hybrid Cloud and Fog computing using improved multi-objective CS algorithm. Soft Comput 28, 4037–4050 (2024). https://doi.org/10.1007/s00500-023-09381-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-023-09381-5