Abstract
The existing cloud model unable to handle abundant amount of Internet of Things (IoT) services placed by the end users due to its far distant location from end user and centralized nature. The edge and fog computing are the latest technologies for developing smart cities that are becoming popular to efficiently support latency-sensitive IoT application services. But these advanced technologies suffer from limited size and computation capabilities; therefore, optimal placement of services among available resources is an open issue. Therefore, the proposed work target the stated issue and presents a Dynamic and Distributed Service Placement Policy (DD-SPP) considering the edge and fog devices. The model is divided into three main phases, that is, the Service Type Estimator (STE), the Service Dependency Estimator & Resolution (SDER), and Resource Demand Estimator & Scheduling (RDES). STE and RDES modules depend on each other to perform optimal service placement. The results of the implementation showed satisfactory improvement with respect to present state-of-the-art policy where delay is improved by around 41%, energy is improved by 27%, response time by 28% and overall cost by 28%.



Similar content being viewed by others
Data Availability
All data generated or analysed during this study are included in this article.
References
Das R, Inuwa MM. A review on fog computing: issues, characteristics, challenges, and potential applications. Telemat Inform Rep. 2023;10:100049.
Abu-Amssimir N, Al-Haj A. A QoS-aware resource management scheme over fog computing infrastructures in IoT systems. Multimed Tools Appl. 2023;82:28281–300.
Azizi S, Farzin P, Shojafar M, Rana O. A scalable and flexible platform for service placement inmulti-fog and multi-cloud environments. J Supercomput. 2023;80(1):1109–36.
Islam MSU, Kumar A, Yu-Chen H. Context-aware scheduling in fog computing: a survey, taxonomy, challenges and future directions. J Netw Comput Appl. 2021;180: 103008.
Dadashi M, Rajabzadeh A. DAIP: a delay-efficient and availability-aware IoT application placement in fog environments. Computing. 2023;105(9):2007–35.
Xu F, Yin Z, Han G, Li Y, Zhang F, Bi Y. Multi-objective fog node placement strategy based on heuristic algorithms for smart factories. Wirel Netw. 2023;2023:1–18.
Abid M, Saqlain M. Utilizing edge cloud computing and deep learning for enhanced risk assessment in China’s international trade and investment. Int J Knowl Innov Stud. 2023;1(1):1–9.
Goel G, Tiwari R. Resource scheduling techniques for optimal quality of service in fog computing environment: a review. Wirel Pers Commun. 2023;131(1):141–64.
Wang L, Deng X, Gui J, Chen X, Wan S. Microservice-oriented service placement for mobile edge computing in sustainable internet of vehicles. IEEE Trans Intell Transp Syst. 2023;24(9):10012–26.
Vergara J, Botero J, Fletscher L. A comprehensive survey on resource allocation strategies in fog/cloud environments. Sensors. 2023;23(9):4413.
Ogundoyin SO, Kamil IA. Optimal fog node selection based on hybrid particle swarm optimization and firefly algorithm in dynamic fog computing services. Eng Appl Artif Intell. 2023;121:105998.
Gupta S, Singh N. Toward intelligent resource management in dynamic fog computing-based internet of things environment with deep reinforcement learning: a survey. Int J Commun Syst. 2023;36(4):e5411.
Alsemmeari RA, Dahab MY, Alturki B, Alsulami AA, Alsini R. Towards an effective service allocation in fog computing. Sensors. 2023;23(17):7327.
Puttaswamy NG, Murthy AN, Degha H. A comparative review of internet of things model workload distribution techniques in fog computing networks. Inf Dyn Appl. 2024;3(1):100866.
Canali C, Lancellotti R. GASP: genetic algorithms for service placement in fog computing systems. Algorithms. 2019;12(10):201.
Salaht FA, Desprez F, Lebre A, Prud’Homme C, Abderrahim M. Service placement in fog computing using constraint programming. In: 2019 IEEE International Conference on Services Computing (SCC). IEEE; 2019. p. 19–27.
Guerrero C, Lera I, Juiz C. A lightweight decentralized service placement policy for performance optimization in fog computing. J Ambient Intell Humaniz Comput. 2019;10:2435–52.
Ayoubi M, Ramezanpour M, Khorsand R. An autonomous IoT service placement methodology in fog computing. Softw Pract Exp. 2021;51(5):1097–120.
Sarrafzade N, Entezari-Maleki R, Sousa L. A genetic-based approach for service placement in fog computing. J Supercomput. 2022;78(8):10854–75.
Liu C, Wang J, Zhou L, Rezaeipanah A. Solving the multi-objective problem of IoT service placement in fog computing using cuckoo search algorithm. Neural Process Lett. 2022;54(3):1823–54.
Natesha BV, Guddeti RMR. Adopting elitism-based genetic algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment. J Netw Comput Appl. 2021;178:102972.
Ghobaei-Arani M, Shahidinejad A. A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment. Expert Syst Appl. 2022;200: 117012.
Zare M, Sola YE, Hasanpour H. Towards distributed and autonomous IoT service placement in fog computing using asynchronous advantage actor-critic algorithm. J King Saud Univ Comput Inf Sci. 2023;35(1):368–81.
Zhao D, Zou Q, Zadeh MB. A QoS-aware IoT service placement mechanism in fog computing based on open-source development model. J Grid Comput. 2022;20(2):12.
Salimian M, Ghobaei-Arani M, Shahidinejad A. Toward an autonomic approach for internet of things service placement using gray wolf optimization in the fog computing environment. Softw Pract Exp. 2021;51(8):1745–72.
Huang T, Lin W, Xiong C, Pan R, Huang J. An ant colony optimization-based multiobjective service replicas placement strategy for fog computing. IEEE Trans Cybern. 2021;51(11):5595–608.
Zare M, Sola YE, Hasanpour H. An autonomous planning model for solving IoT service placement problem using the imperialist competitive algorithm. J Supercomput. 2023;79(11):12671–90.
Zhang Z, Sun H, Abutuqayqah H. An efficient and autonomous scheme for solving IoT service placement problem using the improved Archimedes optimization algorithm. J King Saud Univ Comput Inf Sci. 2023;35(3):157–75.
Safa’a SS, Alansari I, Hamiaz MK, Ead W, Tarabishi RA, Khater H. iFogRep: an intelligent consistent approach for replication and placement of IoT based on fog computing. Egypt Inform J. 2023;24(2):327–39.
Sopin E, Nikita Z, Ageev K, Shorgin S. Analysis of the response time characteristics of the fog computing enabled real-time mobile applications. In: Internet of Things, smart spaces, and next generation networks and systems: 20th international conference, NEW2AN 2020, and 13th conference, ruSMART 2020, St. Petersburg, Russia, August 26–28, 2020, Proceedings, Part I 20. Springer, 2020. p. 99–109.
Gupta H, Dastjerdi AV, Ghosh SK, Buyya R. iFogSim: a toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Softw Pract Exp. 2017;47(9):1275–96.
Funding
No funding was received for conducting this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
We have no conflict of interest to disclose.
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
Kaur, N., Bhardwaj, V. DD-SPP: Dynamic and Distributed Service Placement Policy for Optimal Scheduling in Fog-Edge Computing. SN COMPUT. SCI. 5, 801 (2024). https://doi.org/10.1007/s42979-024-03175-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-024-03175-8