nach oben

Cluster Computing

Erschienen in:

15.06.2021

A cost-efficient auto-scaling mechanism for IoT applications in fog computing environment: a deep learning-based approach

verfasst von: Masoumeh Etemadi, Mostafa Ghobaei-Arani, Ali Shahidinejad

Erschienen in: Cluster Computing | Ausgabe 4/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel Design and evaluation of a joint profit and interference-aware VMs consolidation in IaaS cloud datacenter

Nächster Artikel An efficient energy-aware approach for dynamic VM consolidation on cloud platforms

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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/s10586-021-03286-4CrossRef

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-wCrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

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)CrossRef

10.

Ayoubi, M., Ramezanpour, M., Khorsand, R.: An autonomous IoT service placement methodology in fog computing. Software: Practice and Experience, 51(5), 1097-1120, (2021)

11.

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)CrossRef

12.

Ghobaei-Arani, M., Souri, A., Rahmanian, A.A.: Resource management approaches in fog computing: a comprehensive review. J. Grid Comput. 18, 1–42 (2019)CrossRef

13.

Pournaras, E., Yadhunathan, S., Diaconescu, A.: Holarchic structures for decentralized deep learning: a performance analysis. Clust. Comput. 23(1), 19–240 (2020)CrossRef

14.

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/s10586-021-03240-4CrossRef

15.

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-wCrossRef

16.

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)CrossRef

17.

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)CrossRef

18.

Baghban, H., Huang, C.Y., Hsu, C.H.: Resource provisioning towards OPEX optimization in horizontal edge federation. Comput. Commun. 158, 39–50 (2020)CrossRef

19.

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)

20.

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)CrossRef

21.

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)CrossRef

22.

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)CrossRef

23.

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)CrossRef

24.

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

25.

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)CrossRef

26.

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

27.

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)CrossRef

28.

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)CrossRef

29.

El Kafhali, S., Salah, K.: Efficient and dynamic scaling of fog nodes for IoT devices. J. Supercomput. 73, 5261–5284 (2017)CrossRef

30.

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)CrossRef

31.

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.2020.1818294CrossRef

32.

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)CrossRef

33.

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.2021.01.207CrossRef

34.

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)

35.

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)

36.

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)CrossRef

37.

http://iot.ee.surrey.ac.uk:8080/datasets.html 2014

38.

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)

39.

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)

Titel: A cost-efficient auto-scaling mechanism for IoT applications in fog computing environment: a deep learning-based approach
verfasst von: Masoumeh Etemadi
Mostafa Ghobaei-Arani
Ali Shahidinejad
Publikationsdatum: 15.06.2021
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 4/2021
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-021-03307-2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2021

Online multi-job mapping for photonic network on a chip design using partial migrations

montage: NVM-based scalable synchronization framework for crash-consistent file systems

Energy-efficient cloud data center with fair service level agreement for green computing

Enhancing link prediction in dynamic networks using content aggregation

VMS-MCSA: virtual machine scheduling using modified clonal selection algorithm

HDS: optimizing data migration and parity update to realize RAID-6 scaling for HDP

Premium Partner