Abstract
The fog computing model has emerged as a viable infrastructure for serving IoT applications in recent years. In the fog ecosystem, it is essential to manage resources for different workloads due to the high volume and rapid growth of requests. Therefore, a challenge faced in this area is dynamic and efficient resource auto-scaling because fog resources must be allocated to requests efficiently. More fog resources than needed leads to “Over-Provisioning”, and fewer fog resources leads to the “Under-provisioning” issue. To this end, an effective deep learning-based resource auto-scaling mechanism has been proposed to manage the number of resources needed to handle dynamic workloads in a fog environment. The simulation results indicated that the proposed solution reduces cost, network usage, and delay violation and increases CPU utilization compared with existing resource auto-scaling mechanisms.
Similar content being viewed by others
References
Fersi, G.: Fog computing and Internet of Things in one building block: a survey and an overview of interacting technologies. Clust. Comput. (2021). https://doi.org/10.1007/s10586-021-03286-4
Puri, V., Priyadarshini, I., Kumar, R., Van Le, C.: Smart contract based policies for the Internet of Things. Clust. Comput. (2021). https://doi.org/10.1007/s10586-020-03216-w
Atlam, H.F., Walters, R.J., Wills, G.B.: Fog computing and the Internet of Things: a review. Big Data Cogn. Comput. 2(2), 10 (2018)
Liu, Y., Zhang, J., Zhan, J.: Privacy protection for fog computing and the Internet of Things data based on blockchain. Clust. Comput. 24, 1–15 (2020)
Shahidinejad, A., Ghobaei-Arani, M., Masdari, M.: Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Clust. Comput. 24(1), 319–342 (2021)
Puliafito, C., Mingozzi, E., Longo, F., Puliafito, A., Rana, O.: Fog computing for the Internet of Things: a survey. ACM Trans. Internet Technol. 19(2), 1–41 (2019)
Jyoti, A., Shrimali, M.: Dynamic provisioning of resources based on load balancing and service broker policy in cloud computing. Clust. Comput. 23(1), 377–395 (2020)
Mahmud, R., Kotagiri, R., Buyya, R.: Fog computing: a taxonomy, survey and future directions. In: Internet of Everything, pp. 103–130. Springer, Singapore (2018)
Aslanpour, M.S., Gill, S.S., Toosi, A.N.: Performance evaluation metrics for cloud, fog and edge computing: a review, taxonomy, benchmarks and standards for future research. Internet Things 12, 100273 (2020)
Ayoubi, M., Ramezanpour, M., Khorsand, R.: An autonomous IoT service placement methodology in fog computing. Software: Practice and Experience, 51(5), 1097-1120, (2021)
Manasrah, A.M., Gupta, B.B.: An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Clust. Comput. 22(1), 1639–1653 (2019)
Ghobaei-Arani, M., Souri, A., Rahmanian, A.A.: Resource management approaches in fog computing: a comprehensive review. J. Grid Comput. 18, 1–42 (2019)
Pournaras, E., Yadhunathan, S., Diaconescu, A.: Holarchic structures for decentralized deep learning: a performance analysis. Clust. Comput. 23(1), 19–240 (2020)
Elshawi, R., Wahab, A., Barnawi, A., Sakr, S.: DLBench: a comprehensive experimental evaluation of deep learning frameworks. Clust. Comput. (2021). https://doi.org/10.1007/s10586-021-03240-4
Cheon, H., Ryu, J., Ryou, J., Park, C.Y., Han, Y.S.: ARED: automata-based runtime estimation for distributed systems using deep learning. Clust. Comput. (2021). https://doi.org/10.1007/s10586-021-03272-w
Gupta, B.B., Agrawal, D.P., Yamaguchi, S.: Deep learning models for human centered computing in fog and mobile edge networks. J. Ambient Intell. Humaniz. Comput. 10, 2907–2911 (2019)
Naha, R.K., Garg, S., Chan, A., Battula, S.K.: Deadline-based dynamic resource allocation and provisioning algorithms in fog–cloud environment. Future Gener. Comput. Syst. 104, 131–141 (2020)
Baghban, H., Huang, C.Y., Hsu, C.H.: Resource provisioning towards OPEX optimization in horizontal edge federation. Comput. Commun. 158, 39–50 (2020)
Madan, N., Malik, A.W., Rahman, A.U., Ravana, S.D.: On-demand resource provisioning for vehicular networks using flying fog. Veh. Commun. 25, 100252 (2020)
Santos, J., Wauters, T., Volckaert, B., De Turck, F.: Towards end-to-end resource provisioning in Fog Computing over Low Power Wide Area Networks. J. Netw. Comput. Appl. 175, 102915 (2021)
Lu, S., Wu, J., Duan, Y., Wang, N., Fang, J.: Towards cost-efficient resource provisioning with multiple mobile users in fog computing. J. Parallel Distrib. Comput. 146, 96–106 (2020)
Nguyen, N.D., Phan, L.A., Park, D.H., Kim, S., Kim, T.: ElasticFog: elastic resource provisioning in container-based fog computing. IEEE Access 8, 183879–183890 (2020)
Porkodi, V., Singh, A.R., Sait, A.R.W., Shankar, K., Yang, E., Seo, C., Joshi, G.P.: Resource provisioning for cyber–physical–social system in cloud–fog–edge computing using optimal flower pollination algorithm. IEEE Access 8, 105311–105319 (2020)
Naha, R.K., Garg, S., Battula, S.K., Amin, M.B., Georgakopoulos, D.: Multiple Linear Regression-Based Energy-Aware Resource Allocation in the Fog Computing Environment. arXiv preprint (2021). arXiv:2103.06385
Xu, Z., Zhang, Y., Li, H., Yang, W., Qi, Q.: Dynamic resource provisioning for cyber–physical systems in cloud–fog–edge computing. J Cloud Comput. 9(1), 1–16 (2020)
Mahmud, R., Toosi, A.N.: Con-Pi: A Distributed Container-Based Edge and Fog Computing Framework for Raspberry Pis. arXiv preprint (2021). arXiv:2101.03533
Etemadi, M., Ghobaei-Arani, M., Shahidinejad, A.: Resource provisioning for IoT services in the fog computing environment: an autonomic approach. Comput. Commun. 161, 109–131 (2020)
Tseng, F.-H., Tsai, M.-S., Tseng, C.-W., Yang, Y.-T., Liu, C.-C., Chou, L.-D.: A lightweight auto-scaling mechanism for fog computing in industrial applications. IEEE Trans. Ind. Inform. 14(10), 1–8 (2018)
El Kafhali, S., Salah, K.: Efficient and dynamic scaling of fog nodes for IoT devices. J. Supercomput. 73, 5261–5284 (2017)
Peng, L., Dhaini, A.R., Ho, P.H.: Toward integrated Cloud-Fog networks for efficient IoT provisioning: key challenges and solutions. Future Gener. Comput. Syst. 88, 606–613 (2018)
Etemadi, M., Ghobaei-Arani, M., Shahidinejad, A.: A learning-based resource provisioning approach in the fog computing environment. J. Exp. Theor. Artif. Intell. (2020). https://doi.org/10.1080/0952813X.2020.1818294
Rabie, A.H., Ali, S.H., Ali, H.A., Saleh, A.I.: A fog based load forecasting strategy for smart grids using big electrical data. Clust. Comput. 22(1), 241–270 (2019)
Radhakrishnan, G., Srinivasan, K., Maheswaran, S., Mohanasundaram, K., Palanikkumar, D., Vidyarthi, A.: A deep-RNN and meta-heuristic feature selection approach for IoT malware detection. Mater. Today Proc. (2021). https://doi.org/10.1016/j.matpr.2021.01.207
Millham, R., Agbehadji, I.E., Yang, H.: Parameter tuning onto recurrent neural network and long short-term memory (RNN-LSTM) network for feature selection in classification of high-dimensional bioinformatics datasets. In: Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing, pp. 21-42. Springer, Singapore (2021)
Alaei, M., Khorsand, R., Ramezanpour, M.: An adaptive fault detector strategy for scientific workflow scheduling based on improved differential evolution algorithm in cloud. Applied Soft Computing, 99, 106895, (2021)
Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., 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. 47(9), 1275–1296 (2017)
Saeedi, S., Khorsand, R., Bidgoli, S. G., & Ramezanpour, M.: Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing. Computers & Industrial Engineering, 147, 106649, (2020)
Paknejad, P., Khorsand, R., Ramezanpour, M.: Chaotic improved PICEA-g-based multi-objective optimization for workflow scheduling in cloud environment. Future Generation Computer Systems, 117, 12-28, (2021)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Etemadi, M., Ghobaei-Arani, M. & Shahidinejad, A. A cost-efficient auto-scaling mechanism for IoT applications in fog computing environment: a deep learning-based approach. Cluster Comput 24, 3277–3292 (2021). https://doi.org/10.1007/s10586-021-03307-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-021-03307-2