nach oben

Cluster Computing

Erschienen in:

26.05.2023

An efficient edge caching approach for SDN-based IoT environments utilizing the moth flame clustering algorithm

verfasst von: Seyedeh Shabnam Jazaeri, Sam Jabbehdari, Parvaneh Asghari, Hamid Haj Seyyed Javadi

Erschienen in: Cluster Computing | Ausgabe 2/2024

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

IoT networks can provide many benefits and opportunities, although their implementation poses challenges. Cloud-only storage of IoT data would be very costly and time-consuming. In this paper, a new scheme is proposed for caching IoT content on the edge with SDN-based processing capability. The proposed scheme considers a global SDN controller, which coordinates cache decisions across the entire IoT network. The Moth-Flame Optimization-Cluster Head Selection (MFO-CHS) algorithm is used to cluster devices where the selected cluster heads send the IoT data to the edge nodes for caching. In addition, by utilizing edge caching capabilities and using MFO to select and cache the appropriate contents on edge nodes, the proposed Moth-Flame Optimization-Edge Caching (MFO-EC) algorithm can provide data with lower latency on upcoming requests. Caching can also help ensure reliability and availability since intermittent connections and power limitations affect IoT devices. Caching decisions regarding IoT characteristics are not made intelligently in the default caching scheme for maximizing device longevity and managing the possibility that content producers may become unreachable. This scheme considers several metrics, and the proposed moth-flame optimization (MFO) algorithms, MFO-CHS, and MFO-EC algorithms, which are nature-inspired paradigms for the edge caching problem called “MFO-SDN-EC”, Moth-Flame Optimization-Software-defined Networking -Edge Caching, are used to select the best options regarding considered criteria and improve the QoS in the SDN based IoT environment. Based on simulations, our proposed caching method can reduce energy consumption by 45%, decrease the average response time by 49%, and also increase cache-hit rates. Furthermore, our results demonstrate our algorithm’s superiority over several current approaches in terms of assessment measures.

Vorheriger Artikel Profit-maximization spectrum sharing in opportunistic duopoly market under dynamic spectrum pricing and QoS constraints

Nächster Artikel InPlaceKV: in-place update scheme for SSD-based KV storage systems under update-intensive Worklaods

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

Zhang, Z., Wei, X., Lung, C.-H., Zhao, Y.: “iCache: an intelligent caching scheme for dynamic network environments in ICN-based IoT networks. IEEE Internet of Things J. 10(2), 1787–1799 (2022)CrossRef

Jazaeri, S.S., Jabbehdari, S., Asghari, P., Haj, S., Javadi, H.: Edge computing in SDN-IoT networks: a systematic review of issues, challenges and solutions. Cluster Comput. 24(4), 3187–3228 (2021). https://doi.org/10.1007/s10586-021-03311-6CrossRef

Asghari, P., Rahmani, A.M., Javadi, H.H.S.: Internet of things applications: A systematic review. J. Comput. Networks. (2019). https://doi.org/10.1016/j.comnet.2018.12.008CrossRef

Asghari, P., Rahmani, A.M., Javadi, H.H.S.: Service composition approaches in IoT: a systematic review. J. Netw. Comput. Appl. (2018). https://doi.org/10.1016/j.jnca.2018.07.013CrossRef

Jazaeri, S., Berangi, R.: Survey present and future visions of internet of things (IoT). Sci. J. Res. 8(7), 8–14 (2016). https://doi.org/10.7537/marsrsj080716.02CrossRef

Chen, B., Liu, L., Sun, M., Ma, H.: IoTCache: toward data-driven network caching for internet of things. IEEE Internet of Things J. 6(6), 10064–10076 (2019). https://doi.org/10.1109/jiot.2019.2935442CrossRef

Li, C., Qianqian, C., Luo, Y.: Low-latency edge cooperation caching based on base station cooperation in SDN based MEC. Expert Syst. Appl. (2022). https://doi.org/10.1016/j.eswa.2021.116252CrossRef

Singh, T., Saxena, N., Khurana, M., Singh, D., Abdalla, M., Alshazly, H.: “Data clustering using moth-flame optimization algorithm. Sensors 21(12), 4086 (2021). https://doi.org/10.3390/s21124086CrossRef

Amadeo, M., Campolo, C., Ruggeri, G., Molinaro, A.: Edge caching in IoT smart environments: benefits, challenges, and research perspectives toward 6G. IoT Edge Solut. Cognit. Build. (2022). https://doi.org/10.1007/978-3-031-15160-6_3CrossRef

10.

Zhang, Z., Lung, C.-H., Lambadaris, I., St-Hilaire, M.: “IoT Data Lifetime-Based Cooperative Caching Scheme for ICN-IoT Networks,“ in IEEE International Conference on Communications (ICC), (2018). https://doi.org/10.1109/icc.2018.8422100, 2018

11.

Jazaeri, S.S., Taghdiri, E.: “Distributed Edge Computing in SDN-IoT Network,“ JNACS, vol. 5, no. 2, p. (2022). https://publisher.resbee.org/jnacs/archive/v5i2/a3.html#,

12.

Ruggeri, G., Amadeo, M., Campolo, C., Molinaro, A., Iera, A.: Caching Popular transient IoT contents in an SDN-based Edge infrastructure. IEEE Trans. Netw. Serv. Manage. 18(3), 3432–3447 (2021). https://doi.org/10.1109/tnsm.2021.3056891CrossRef

13.

Baccour, E., Erbad, A., Mohamed, A., Bilal, K., Guizani, M.: “Proactive Video Chunks Caching and Processing for Latency and Cost Minimization in Edge Networks,“ in IEEE Wireless Communications and Networking Conference (WCNC), (2019). https://doi.org/10.1109/wcnc.2019.8885906,

14.

Wu, H., Luo, Y., Li, C.: Optimization of heat-based cache replacement in edge computing system. J. Supercomput. 77, 2268–2301 (2020). https://doi.org/10.1007/s11227-020-03356-1CrossRef

15.

Sun, X., Ansari, N.: Dynamic resource caching in the IoT Application Layer for Smart Cities. IEEE Int. Things J. 5(2), 606–613 (2018). https://doi.org/10.1109/jiot.2017.2764418CrossRef

16.

Miwa, T., Kimura, S.: “Cooperative Update Mechanism of Cache Update Method Based on Content Update Dynamic Queries for Named Data Networking,“ in Seventh International Symposium on Computing and Networking Workshops (CANDARW), (2019). https://doi.org/10.1109/candarw.2019.00013,

17.

Vural, S., Wang, N., Navaratnam, P., Tafazolli, R.: Caching transient data in internet content routers. IEEE/ACM Trans. Netw. 25(2), 1048–1061 (2017). https://doi.org/10.1109/tnet.2016.2616359CrossRef

18.

Poderys, J., Artuso, M., Lensbol, O., Christiansen, C.M., H. L., Soler, J.: Caching at the Mobile Edge: A practical implementation. IEEE Access. 6, 8630–8637 (2018). https://doi.org/10.1109/access.2018.2809490CrossRef

19.

Abdullahi, I., Arif, S., Hassan, S.: Survey on caching approaches in Information Centric networking. J. Netw. Comput. Appl. 56, 48–59 (2015). https://doi.org/10.1016/j.jnca.2015.06.011CrossRef

20.

Nkenyereye, L., Nkenyereye, L., Islam, S.M.R., Kerrache, C.A., Abdullah-Al-Wadud, M., Alamri, A.: Software defined Network-Based Multi-Access Edge Framework for Vehicular Networks. IEEE Access. 8, 4220–4234 (2020). https://doi.org/10.1109/ACCESS.2019.2962903CrossRef

21.

Khodaparas, S., Yousefi, S., Benslimane, A.: “A Multi Criteria Cooperative Caching Scheme for Internet of Things,“ IEEE International Conference on Communications (ICC), pp. 1–6, (2019)

22.

Liang, C., Yu, F.R.: “Enhancing mobile edge caching with bandwidth provisioning in software-defined mobile networks,“ in IEEE International Conference on Communications (ICC), (2017)

23.

Kalghoum, A., Gammar, S.M., Saidane, L.A.: Towards a novel cache replacement strategy for named data networking based on software defined networking. Comput. Electr. Eng. 66, 98–113 (2018)CrossRef

24.

Nazari, A., Tavassolian, F., Abbasi, M., Mohammadi, R., Yaryab, P.: “An intelligent SDN-Based clustering approach for optimizing iot power consumption in smart homes. Wireless Commun. Mobile Comput. (2022). https://doi.org/10.1155/2022/878338CrossRef

25.

Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89, 228–249 (2015). https://doi.org/10.1016/j.knosys.2015.07.006CrossRef

26.

Ghobaei-Arani, M., Rahmanian, A.A., Souri, A., Rahmani, A.M.: “A moth-flame optimization algorithm for web service composition in cloud computing: simulation and verification. Softw. Pract. Exp. 48(10), 1865–1892 (2018)CrossRef

27.

Tang, Y., Guo, K., Ma, J., Shen, Y., Chi, T.: “A smart caching mechanism for mobile multimedia in information centric networking with edge computing. Future Gen. Comput. Syst. 91, 590–600 (2018)CrossRef

28.

Faraji-Mehmandar, M., Jabbehdari, S., Haj, S., Javadi, H.: “A proactive fog service provisioning framework for internet of things applications: an autonomic approach. Trans. Emerg. Telecommun. Technol. 32(11), e4342 (2021)CrossRef

29.

Mohammadi, R., Nazari, A., Nassiri, M., Conti, M.: “An SDN-based framework for QoS routing in internet of underwater things. Telecommun. Syst. Telecommun. Syst. 78(2), 253–266 (2021)CrossRef

30.

Jazaeri, S., Berangi, R.: Study the Challenges of using and development of 5G networks. Sci. J. Rep. Opin. 8(7), 13–19 (2016). https://doi.org/10.7537/marsroj080716.03CrossRef

31.

Khodaparas, S., Benslimane, A., Yousefi, S.: A software-defined caching scheme for the internet of things. Comput. Commun. 158, 178–188 (2020). https://doi.org/10.1016/j.comcom.2020.05.002CrossRef

32.

Sharif, S., Moghaddam, M.H.Y., Seno, S.A.H.: Adaptive cache content placement for software-defined internet of things. Future Generation Computer Systems. 136, 34–48 (2022). https://doi.org/10.1016/j.future.2022.05.019CrossRef

33.

De Masi, G.: “The impact of topology on Internet of Things: A multidisciplinary review,“ in Advances in Science and Engineering Technology International Conferences (ASET), (2018). https://doi.org/10.1109/icaset.2018.8376837,

34.

“OpenFlow: Switch Specification version1.5.1 Open Networking Foundation (ONF),“ 26 March 2015. [Online]. Available: https://opennetworking.org/wp-content/uploads/2014/10/openflow-switch-v1.5.1.pdf

35.

Guanqiang, L., Jinyong, L., Quan, X., Xiaoguang, W., Lei, Y., Fukui, L., Jingnan, L.: “A Method for IoT Device Management and Traffic Scheduling in Distribution Station Area Based on Distributed SDN Architecture,“ in Asian Conference on Frontiers of Power and Energy (ACFPE), (2022). https://doi.org/10.1109/acfpe56003.2022.9952220,

36.

Khan, M.F., Aadil, F., Maqsood, M., Bukhari, S.H.R., Hussain, M., Nam, Y.: Moth flame clustering algorithm for internet of vehicle (MFCA-IoV). IEEE Access 7, 11613–11629 (2019)CrossRef

37.

Alwasel, K., Jha, D.N., Habeeb, F., Demirbaga, U., Rana, O., Baker, T., Dustdar, S., Villari, M., James, P., Solaiman, E., Ranjan, R.: IoTSim-Osmosis: a framework for modeling and simulating iot applications over an edge-cloud continuum. J. Syst. Architect. 116, 101956 (2021). https://doi.org/10.1016/j.sysarc.2020.101956CrossRef

38.

Pfender, J., Valera, A., Seah, W.K.G.: “Performance comparison of caching strategies for information-centric IoT,“ in Proceedings of the 5th ACM Conference on Information-Centric Networking, (2018). https://doi.org/10.1145/3267955.3267966,

39.

Han, G., Liu, L., Jiang, J., Shu, L., Hancke, G.: Analysis of energy-efficient connected target coverage algorithms for industrial wireless sensor networks. IEEE Trans. Industr. Inf. 13(1), 135–143 (2017). https://doi.org/10.1109/tii.2015.2513767CrossRef

40.

Asmat, H., Ullah, F., Zareei, M., Khan, A., Mohamed, E.M.: Energy-efficient centrally controlled caching contents for information-centric internet of things. IEEE Access. 8, 126358–126369 (2020). https://doi.org/10.1109/access.2020.3008193CrossRef

Titel: An efficient edge caching approach for SDN-based IoT environments utilizing the moth flame clustering algorithm
verfasst von: Seyedeh Shabnam Jazaeri
Sam Jabbehdari
Parvaneh Asghari
Hamid Haj Seyyed Javadi
Publikationsdatum: 26.05.2023
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 2/2024
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-023-04023-9

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 2/2024

Human–object interaction recognition based on interactivity detection and multi-feature fusion

CSADE: a delay-sensitive scheduling method based on task admission and delay evaluation on edge–cloud collaboration

A novel sparrow search algorithm with integrates spawning strategy

Self-supervised deep subspace clustering with entropy-norm

Spark-based cooperative coevolution for large scale global optimization

MOM-VMP: multi-objective mayfly optimization algorithm for VM placement supported by principal component analysis (PCA) in cloud data center

Premium Partner