Abstract
The smart manufacturing industry (Industry 4.0) uses the Internet of Things (IoT) devices referred to as Industrial IoT (IIoT) to automate the industrial environment. These IIoT devices generate a massive amount of data called big data. Using fog computing architecture for processing this extensive data will reduce the service time and the service cost for the IIoT applications. The primary challenge is to design better service placement strategies to deploy the IIoT service requests on the fog nodes to minimize service costs and ensure the Quality of Service (QoS) of IIoT applications. Hence, the placement of IIoT services on the fog nodes can be considered as NP-hard problem. In this work, the meta-heuristic-based hybrid algorithms, namely: MGAPSO and EGAPSO, are developed by combining the GA & PSO and Elitism-based GA (EGA) & PSO, respectively. Further, carried out experiments on the two-level fog computing framework developed using docker and containers on 1.4 GHz, 64-bit quad-core processor devices. Experimental results demonstrate that the proposed hybrid EGAPSO algorithm minimizes service time, service cost, and energy consumption and ensures the IIoT applications’ QoS compared to other proposed and state-of-the-art service placement strategies considered for the performance evaluation.
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References
Alavi, A.H., Jiao, P., Buttlar, W.G., Lajnef, N.: Internet of things-enabled smart cities: state-of-the-art and future trends. Measurement 129, 589–606 (2018)
Evans, D.: The internet of things: how the next evolution of the internet is changing everything. CISCO White Paper 1(2011), 1–11 (2011)
Varshney, P., Simmhan, Y.: Demystifying fog computing: characterizing architectures, applications and abstractions. In: 2017 IEEE 1st international conference on fog and edge computing (ICFEC), pp. 115–124. IEEE, Piscataway (2017)
Maenhaut, P.J., Volckaert, B., Ongenae, V., De Turck, F.: Resource management in a containerized cloud: status and challenges. J. Netw. Syst. Manage. 28(2), 197–246 (2020)
Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., Kong, J., Jue, J.P.: All one needs to know about fog computing and related edge computing paradigms: a complete survey. J. Syst. Archit. 98, 289–330 (2019)
Aazam, M., Zeadally, S., Harras, K.A.: Deploying fog computing in industrial internet of things and industry 4.0. IEEE Trans. Ind. Inform. 14(10), 4674–4682 (2018)
Goethals, T., De Turck, F., Volckaert, B.: Self-organizing fog support services for responsive edge computing. J. Netw. Syst. Manage. 29(2), 1–33 (2021)
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pp. 13–16. ACM (2012)
Mahmud, R., Kotagiri, R., Buyya, R.: Fog computing: a taxonomy, survey and future directions. In: Internet of everything, pp. 103–130. Springer, Singapore (2018)
Simmhan, Y.: Iot analytics across edge and cloud platforms. IEEE IoT Newsletter (2017)
Gu, L., Zeng, D., Guo, S., Barnawi, A., Xiang, Y.: Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans. Emerg. Top. Comput. 5(1), 108–119 (2017)
Aazam, M., Huh, E.N.: Fog computing and smart gateway based communication for cloud of things. In: Future internet of things and cloud (FiCloud), 2014 international conference on, pp. 464–470. IEEE (2014)
Andrade, E., Nogueira, B., de Farias Junior, I., Araújo, D.: Performance and availability trade-offs in fog-cloud IoT environments. J. Netw. Syst. Manage. 29(1), 1–27 (2021)
Yin, L., Luo, J., Luo, H.: Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing. IEEE Trans. Ind. Inform. 14(10), 4712–4721 (2018)
Mishra, S.K., Putha, D., Rodrigues, J.J., Sahoo, B., Dutkiewicz, E.: Sustainable service allocation using metaheuristic technique in fog server for industrial applications. IEEE Trans. Ind. Inform. 14(10), 4497–4506 (2018)
Santos, L., Cunha, B., Fé, I., Vieira, M., Silva, F.A.: Data processing on edge and cloud: a performability evaluation and sensitivity analysis. J. Netw. Syst. Manage. 29(3), 1–24 (2021)
Guerrero, C., Lera, I., Juiz, C.: Resource optimization of container orchestration: a case study in multi-cloud microservices-based applications. J. Supercomput. 74(7), 2956–2983 (2018)
Tang, H., Li, C., Bai, J., Tang, J., Luo, Y.: Dynamic resource allocation strategy for latency-critical and computation-intensive applications in cloud-edge environment. Comput. Commun. 134, 70–82 (2019)
Brogi, A., Forti, S., Guerrero, C., Lera, I.: How to place your apps in the fog-state of the art and open challenges. (2019). arXiv preprint arXiv:1901.05717
Salaht, F.A., Desprez, F., Lebre, A.: An overview of service placement problem in fog and edge computing. ACM Comput. Surv. (CSUR) 53(3), 1–35 (2020)
Rakshith, G., Rahul, M., Sanjay, G., Natesha, B., Reddy, G.R.M.: Resource provisioning framework for IoT applications in fog computing environment. In: 2018 IEEE international conference on advanced networks and telecommunications systems (ANTS), pp. 1–6. IEEE (2018)
Yang, Y., Zhao, S., Zhang, W., Chen, Y., Luo, X., Wang, J.: Debts: delay energy balanced task scheduling in homogeneous fog networks. IEEE Internet Things J. 5(3), 2094–2106 (2018)
Jia, B., Hu, H., Zeng, Y., Xu, T., Yang, Y.: Double-matching resource allocation strategy in fog computing networks based on cost efficiency. J. Commun. Netw. 20(3), 237–246 (2018)
Canali, C., Lancellotti, R.: Gasp: genetic algorithms for service placement in fog computing systems. Algorithms 12(10), 201 (2019)
Rezazadeh, Z., Rahbari, D., Nickray, M.: Optimized module placement in IoT applications based on fog computing. In: Electrical engineering (ICEE), Iranian conference on, pp. 1553–1558. IEEE (2018)
Yadav, V., Natesha, B., Guddeti, R.M.R.: Ga-pso: Service allocation in fog computing environment using hybrid bio-inspired algorithm. In: TENCON 2019-2019 IEEE region 10 conference (TENCON), pp. 1280–1285. IEEE (2019)
Natesha, B., Guddeti, R.M.R.: Adopting elitism-based genetic algorithm for minimizing multi-objective problems of IoT service placement in fog computing environment. J. Netw. Comput. Appl. 178, 102972 (2021). https://doi.org/10.1016/j.jnca.2020.102972
Goudarzi, M., Wu, H., Palaniswami, M.S., Buyya, R.: An application placement technique for concurrent IoT applications in edge and fog computing environments. IEEE Trans. Mob. Comput. (2020). https://doi.org/10.1109/TMC.2020.2967041
Chekired, D.A., Khoukhi, L., Mouftah, H.T.: Industrial IoT data scheduling based on hierarchical fog computing: a key for enabling smart factory. IEEE Trans. Ind. Inform. 14(10), 4590–4602 (2018)
Forti, S., Pagiaro, A., Brogi, A.: Simulating fogdirector application management. Simul. Model. Pract. Theory 101, 102021 (2020)
Moallemi, R., Bozorgchenani, A., Tarchi, D.: An evolutionary-based algorithm for smart-living applications placement in fog networks. In: 2019 IEEE Globecom workshops (GC Wkshps), pp. 1–6. IEEE (2019)
Adnan, M., Lu, Y., Jones, A., Cheng, F.T.: Application of the fog computing paradigm to additive manufacturing process monitoring and control. SSRN (2021). https://doi.org/10.2139/ssrn.3785854
Liu, B., Xu, X., Qi, L., Ni, Q., Dou, W.: Task scheduling with precedence and placement constraints for resource utilization improvement in multi-user MEC environment. J. Syst. Archit. 114, 101970 (2020)
Verba, N., Chao, K.M., Lewandowski, J., Shah, N., James, A., Tian, F.: Modeling industry 4.0 based fog computing environments for application analysis and deployment. Future Gener. Comput. Syst. 91, 48–60 (2019)
Abbasi, M., Yaghoobikia, M., Rafiee, M., Jolfaei, A., Khosravi, M.R.: Efficient resource management and workload allocation in fog-cloud computing paradigm in IoT using learning classifier systems. Comput. Commun. 153, 217–228 (2020)
Peralta, G., Garrido, P., Bilbao, J., Agüero, R., Crespo, P.M.: Fog to cloud and network coded based architecture: minimizing data download time for smart mobility. Simul. Model. Pract. Theory 101, 102034 (2019)
Alwasel, K., Jha, D.N., Habeeb, F., Demirbaga, U., Rana, O., Baker, T., Dustdar, S., Villari, M., James, P., Solaiman, E., et al.: IoTSim-Osmosis: a framework for modelling and simulating IoT applications over an edge-cloud continuum. J. Syst. Archit. 116, 101956 (2020)
Pop, P., Zarrin, B., Barzegaran, M., Schulte, S., Punnekkat, S., Ruh, J., Steiner, W.: The FORA fog computing platform for industrial IoT. Inf. Syst. 98, 101727 (2021)
Skarlat, O., Nardelli, M., Schulte, S., Dustdar, S.: Towards qos-aware fog service placement. In: Fog and edge computing (ICFEC), 2017 IEEE 1st international conference on, pp. 89–96. IEEE (2017)
Mahmoud, M.M., Rodrigues, J.J., Saleem, K., Al-Muhtadi, J., Kumar, N., Korotaev, V.: Towards energy-aware fog-enabled cloud of things for healthcare. Comput. Electr. Eng. 67, 58–69 (2018)
Kim, W.S., Chung, S.H.: User-participatory fog computing architecture and its management schemes for improving feasibility. IEEE Access 6, 20262–20278 (2018)
Deng, R., Lu, R., Lai, C., Luan, T.H., Liang, H.: Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J. 3(6), 1171–1181 (2016)
Taneja, M., Davy, A.: Resource aware placement of IoT application modules in fog-cloud computing paradigm. In: Integrated network and service management (IM), 2017 IFIP/IEEE symposium on, pp. 1222–1228. IEEE (2017)
Brogi, A., Forti, S.: QoS-aware deployment of IoT applications through the fog. IEEE Internet Things J. 4(5), 1185–1192 (2017)
Liu, W., Huang, G., Zheng, A., Liu, J.: Research on the optimization of IIoT data processing latency. Comput. Commun. 151, 290–298 (2020)
Bozorgchenani, A., Disabato, S., Tarchi, D., Roveri, M.: An energy harvesting solution for computation offloading in fog computing networks. Comput. Commun. 160, 577–587 (2020)
Shekhar, S., Chhokra, A., Sun, H., Gokhale, A., Dubey, A., Koutsoukos, X., Karsai, G.: URMILA: dynamically trading-off fog and edge resources for performance and mobility-aware IoT services. J. Syst. Archit. 107, 101710 (2020)
Donassolo, B., Fajjari, I., Legrand, A., Mertikopoulos, P.: Fog based framework for IoT service provisioning. In: 2019 16th IEEE annual consumer communications & networking conference (CCNC), pp. 1–6. IEEE (2019)
Mahmud, R., Buyya, R.: Modelling and simulation of fog and edge computing environments using ifogsim toolkit. Fog and edge computing: principles and paradigms, pp. 1–35. Wiley Hoboken, NJ (2019)
Perera, C., Member, C.H.L., Jayawardena, S., Chen, M.: Context-aware computing in the internet of things: a survey on internet of things from industrial market perspective. (2015). arXiv preprint arXiv:1502.00164
Acknowledgements
This work has been supported by the Visvesvaraya Ph.D. Scheme for Electronics and IT (Media Lab Asia), the department of MeitY, Government of India. This work has been carried out at the Department of Information Technology, NITK Surathkal, Mangalore, India.
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Natesha, B.V., Guddeti, R.M.R. Meta-heuristic Based Hybrid Service Placement Strategies for Two-Level Fog Computing Architecture. J Netw Syst Manage 30, 47 (2022). https://doi.org/10.1007/s10922-022-09660-w
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DOI: https://doi.org/10.1007/s10922-022-09660-w