Abstract
The pervasiveness of ubiquitously connected smart devices are the main factors in shaping the computing. With the advent of Internet of things (IoTs), massive amount of data is being generated from different sources. The centralized architecture of cloud has become inefficient for the services provision to IoT enabled applications. For better support and services, fog layer is introduced in order to manage the IoT applications demands like latency, responsiveness, deadlines, resource availability and access time etc. of the fog nodes. However, there are some issues related to resource management and fog nodes allocation to the requesting application based on user expectations in the fog layer that need to be addressed. In this paper, we have proposed a Framework, based on Model Driven Software Engineering (MDSE) that practices Machine Learning algorithms and places fog enabled IoT applications at a most suitable fog node. MDSE is meant to develop software by exploiting the problem at domain model level. It is the abstract representation of knowledge that enhances productivity by maximization of compatibility between the systems. The proposed framework is a meta-model that prioritizes the placement requests of applications based on their required expectations and calculates the abilities of the fog nodes for different application placement requests. Rules based machine learning methods are used to create rules based on user’s requirements metrics and then results are optimized to get requesting device placement in the fog layer. At the end, a case study is conducted that uses fuzzy logic for application mapping and shows how the actual application placement will be done by the framework. The proposed meta-model reduces complexity and provides flexibility to make further enhancements according to the user’s requirement to use any of the Machine Learning approaches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of Things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surveys Tuts. 17(4), 2347–2376 (2015)
Lin, J., et al.: A survey on Internet of Things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017)
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)
Stojmenovic, I., Wen, S.: The fog computing paradigm: scenarios and security issues. In: FedCSIS. IEEE (2014)
Qin, B., et al.: Design and application of fog computing model based on big data. In: 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT), pp. 93–97. IEEE, March 2019
Wang, P., Liu, S., Ye, F. and Chen, X.: A fog-based architecture and programming model for iot applications in the smart grid (2018). arXiv preprint arXiv:1804.01239
Dang, T.D., Hoang, D.: A data protection model for fog computing. In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), pp. 32–38. IEEE, May 2017
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. Networks 20(3), 237–246 (2018)
Mohamed, N., Al-Jaroodi, J., Jawhar, I., Noura, H., Mahmoud, S.: UAVFog: a UAV-based fog computing for Internet of Things. In: 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 1–8. IEEE, August 2017
Yao, H., Bai, C., Xiong, M., Zeng, D., Fu, Z.: Heterogeneous cloudlet deployment and user-cloudlet association toward cost effective fog computing. Concurr. Comput. Pract. Experience (CCPE) 29(16), e3975 (2017)
Minh, Q.T., et al.: Toward service placement on fog computing landscape. In: 2017 4th NAFOSTED Conference on Information and Computer Science. IEEE (2017)
Mahmud, R., Srirama, S.N., Ramamohanarao, K., Buyya, R.: Quality of experience (QoE)-aware placement of applications in fog computing environments. J. Parallel Distrib. Comput. 132, 190–203 (2019)
Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., Leitner, P.: Optimized IoT service placement in the fog. SOCA 11(4), 427–443 (2017). https://doi.org/10.1007/s11761-017-0219-8
Bitam, S., Zeadally, S., Mellouk, A.: Fog computing job scheduling optimization based on bees swarm. Enterp. Inf. Syst. (EIS) 12(4), 373–397 (2017)
Kabirzadeh, S., Rahbari, D., Nickray, M.: A hyper heuristic algorithm for scheduling of fog networks. Algorithms 19, 20 (2017)
Sun, Y., Lin, F., Xu, H.: Multi-objective optimization of resource scheduling in fog computing using an improved NSGA-II. Wirel. Pers. Commun. 102(2), 1369–1385 (2018)
Cardellini, V., et al.: On QoS-aware scheduling of data stream applications over fog computing infrastructures. In: 2015 IEEE Symposium on Computers and Communication (ISCC). IEEE (2015)
Rasheed, Y., et al.: A model-driven approach for creating storyboards of web based user interfaces. In: Proceedings of the 2019 7th International Conference on Computer and Communications Management. ACM (2019)
Khan, J.A., Westphal, C., Ghamri-Doudane, Y.: Offloading content with self-organizing mobile fogs. In: 2017 29th International Teletraffic Congress (ITC 29). IEEE (2017)
Li, C., Zhuang, H., Wang, Q., Zhou, X.: SSLB: selfsimilarity-based load balancing for large-scale fog computing. Arab. J. Sci. Eng. 43(12), 7487–7498 (2018)
He, X., Ren, Z., Shi, C., Fang, J.: A novel load balancing strategy of software-defined cloud/fog networking in the internet of vehicles. China Commun. (Chinacom) 13(2), 140–149 (2016)
Rasheed, Y., Azam, F., Anwar, M.W.: A novel framework and tool for multi-purpose modeling of physical infrastructures. In: 12th (ICCMS 2020), Brisbane Australia (2020)
Anglano, C., Canonico, M., Guazzone, M.: Profit-aware resource management for edge computing systems. In: Proceedings of the 1st International Workshop on Edge Systems, Analytics and Networking. ACM (2018)
Bonomi, F., Milito, R., Zhu, J., et al.: Fog computing and its role in the Internet of Things. In: Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16. ACM (2012)
Yin, Y.: Research and implementation of embedded intelligent gateway based on Internet of Things. Beijing University of Technology (2013)
Anwar, M.W., Rashid, M., Azam, F., Kashif, M., Butt, W.H.: A model-driven framework for design and verification of embedded systems through System Verilog. Des. Autom. Embed. Syst. 4, 179–223 (2019). https://doi.org/10.1007/s10617-019-09229-y
Xu, J., Ren, S.: Online learning for offloading and auto scaling in renewable-powered mobile edge computing. In: Global Communications Conference (GLOBECOM), IEEE. IEEE (2016)
Anwar, M.W., Rashid, M., Azam, F., Naeem, A., Kashif, M., Butt, W.H.: A unified model-based framework for the simplified execution of static and dynamic assertion-based verification. IEEE Access 8, 104407–104431 (2020)
Sood, S.K., Singh, K.D.: SNA based resource optimization in optical network using fog and cloud computing. Opt. Switch Netw. 33(3), 114–121 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Arif, M., Azam, F., Anwar, M.W., Rasheed, Y. (2020). A Model-Driven Framework for Optimum Application Placement in Fog Computing Using a Machine Learning Based Approach. In: Lopata, A., Butkienė, R., Gudonienė, D., Sukackė, V. (eds) Information and Software Technologies. ICIST 2020. Communications in Computer and Information Science, vol 1283. Springer, Cham. https://doi.org/10.1007/978-3-030-59506-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-59506-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59505-0
Online ISBN: 978-3-030-59506-7
eBook Packages: Computer ScienceComputer Science (R0)