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pRTMNSGA-III: a novel multi-objective algorithm for QoS-aware multi-cloud IoT service selection

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Abstract

The Internet of Things (IoT) is an emerging technology expected to play a significant role. The integration of IoT with cloud computing (CC) has enabled the creation of large-scale networks of interconnected smart devices and services. To provide optimal quality of service (QoS) for users, it is necessary to address the conflicting requirements of IoT services. The service selection problem is considered NP-hard and therefore requires using metaheuristic algorithms for efficient resolution. This paper presents a novel hybrid multi-objective metaheuristic, pRTMNSGA-III, which combines the strengths of RNSGA-III and TMNSGA-III to generate solutions that meet user preferences and eliminate unfavorable ones. To further optimize computational time, a parallel solution evaluation approach is employed. In addition, a more effective fuzzy membership function is proposed to select the best solution based on the requester’s preferred QoS. The proposed algorithm is evaluated on multiple datasets, and the results demonstrate its superiority over existing state-of-the-art algorithms, including RNSGA-III, TMNSGA-III, NSGA-III, pNSGA-II, NSGA-II, and NSPSO Experimental results demonstrate that pRTMNSGA-III outperforms existing algorithms by up to 37% in terms of the number of non-dominated solutions generated.

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Data Availability

Data sharing does not apply to this article. The used datasets are randomly generated during the execution. All the necessary details to generate them are given in this paper.

References

  1. Vailshery LS (2022) Global IOT connected devices by technology 2030. https://www.statista.com/statistics/1183463/iot-connected-devices-worldwide-by-technology/

  2. Aldawsari B, Baker T, England D (2015) Trusted energy-efficient cloudbased services brokerage platform. Int J Intell Comput Res 6(4):630–639. https://doi.org/10.20533/ijicr.2042.4655.2015.0078

  3. Sefati SS, Halunga S (2022) A hybrid service selection and composition for cloud computing using the adaptive penalty function in genetic and artificial bee colony algorithm. Sensors 22(13). https://doi.org/10.3390/s22134873

  4. Thakur N, Singh A, Sangal AL (2022) Cloud services selection: a systematic review and future research directions. Comput Sci Rev 46. https://doi.org/10.1016/j.cosrev.2022.100514

  5. Ghazali TE (2009) Metaheuristics: from design to implementation. Wiley, ???. https://doi.org/10.5555/1718024

  6. Mohamed AM, Abdelsalam HM (2020) A multicriteria optimization model for cloud service provider selection in multicloud environments. Software: Practice and Experience 50(6):925–947

  7. Yang L, Wang L, Luo T, Zhang H, Liu J, **ng J (2020) Towards IoT-enabled dynamic service optimal selection in multiple manufacturing clouds. J Manuf Syst 56:213–226

    Article  Google Scholar 

  8. Baker T, Aldawsari B, Asim M, Tawfik H, Buyya R (2017) An energyaware service composition algorithm for cloud-based IoT applications. J Cloud Comput 6(1):1–11

    Google Scholar 

  9. Chen Y, Shen W, Wang X, Lin T, **ao Y (2016) IoT-enabled dynamic service selection across multiple manufacturing clouds. 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 676–681

  10. Ait Aissa M, Abdelsalam HM (2016) Energy-centered and QoS-aware services selection for Internet of Things. IEEE Trans Autom Sci Eng 13(3):1256–1269

    Article  Google Scholar 

  11. Benouiza A, Boukaâbache M, Boughanem M (2022) A genetic algorithmbased approach for fluctuating QoS aware selection of IoT services. IEEE Internet Things J 9(20):16403–16415

    Google Scholar 

  12. Wang R, Lu J (2022) QoS-aware service discovery and selection management for cloud-edge computing using a hybrid meta-heuristic algorithm in IoT. Wirel Pers Commun 112(1):1–22

    Google Scholar 

  13. Mohamed AM, Abdelsalam HM (2018) Multicuckoo: multi-cloud service composition using a cuckoo-inspired algorithm for the Internet of Things applications. IEEE Access 6:56737–56749

    Article  Google Scholar 

  14. Kumrai T, Ota K, Dong M, Kishigami J, Sung DK (2021) Multiobjective optimization in cloud brokering systems for connected Internet of Things. IEEE J Internet Things 8(16):12426–12437

    Google Scholar 

  15. Liu J, Gu X, Fu X, Zhang H, Buyya R, Vo HV (2018) A new multiobjective evolutionary algorithm for inter-cloud service composition. IEEE Trans Cloud Comput 6(1):59–72

    Google Scholar 

  16. Zebouchi A, Aklouf Y (2022) A survey on the quality of service and metaheuristic based resolution methods for multi–cloud IOT service selection. Springer. https://springer.longhoe.net/chapter/10.1007/978-3-030-96299-9_40

  17. Rice O, Smith RE, Nyman R (2013) Parallel multi-objective genetic algorithm. In: Dediu A-H, Martín-Vide C, Truthe B, Vega-Rodríguez MA (eds) Theory and Practice of Natural Computing. Springer, Berlin, Heidelberg, pp 217–227

    Chapter  Google Scholar 

  18. Deb K (2019) Evolutionary multi-criterion optimization: 10th International Conference, Emo 2019, East Lansing, MI, USA, March 10– 13, 2019: Proceedings. Springer, ???. https://doi.org/10.1007/978-3-030-12598-1

  19. Vesikar Y, Deb K, Blank J (2018) Reference point based NSGA-III for preferred solutions. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). https://doi.org/10.1109/ssci.2018.8628819

  20. De Buck V (2017) Improving evolutionary algorithms for multi-objective optimisation. PhD thesis

  21. A new multi-objective evolutionary algorithm for inter-cloud service composition. KSII Transactions on Internet and Information Systems 12(1) (2018). https://doi.org/10.3837/tiis.2018.01.001

  22. Chauhan SS, Pilli ES, Joshi RC, Singh G, Govil MC (2019) Brokering in interconnected cloud computing environments: a survey. Journal of Parallel and Distributed Computing 133:193–209. https://doi.org/10.1016/j.jpdc.2018.08.001

    Article  Google Scholar 

  23. Zhang X, Geng J, Ma J, Liu H, Niu S (2020) A QoS–driven service selection optimization algorithm for Internet of Things. https://doi.org/10.21203/rs.3.rs-69961/v1

  24. Hashem I, Telen D, Nimmegeers P, Logist F, Van Impe J (2017) A novel algorithm for fast representation of a Pareto front with adaptive resolution: application to multi-objective optimization of a chemical reactor. Comput Chem Eng 106:544–558. https://doi.org/10.1016/j.compchemeng.2017.06.020

    Article  Google Scholar 

  25. Moeini-Aghtaie M, Abbaspour A, Fotuhi-Firuzabad M (2012) Incorporating large-scale distant wind farms in probabilistic transmission expansion planning-part I: theory and algorithm. IEEE Trans Power Syst 27(3):1585–1593. https://doi.org/10.1109/tpwrs.2011.2182363

    Article  Google Scholar 

  26. Li X (2003) A non-dominated sorting particle swarm optimizer for multiobjective optimization. In: Cantú-Paz E, Foster JA, Deb K, Davis LD, Roy R, O’Reilly U-M, Beyer H-G, Standish R, Kendall G, Wilson S, Harman M, Wegener J, Dasgupta D, Potter MA, Schultz AC, Dowsland KA, Jonoska N, Miller J (eds) Genetic and Evolutionary Computation - GECCO 2003. Springer, Berlin, Heidelberg, pp 37–48

    Chapter  Google Scholar 

  27. Liu Y, Wei J, Li X, Li M (2019) Generational distance indicator-based evolutionary algorithm with an improved niching method for many-objective optimization problems. IEEE Access 7:63881–63891. https://doi.org/10.1109/access.2019.2916634

    Article  Google Scholar 

  28. Audet C, Bigeon J, Cartier D, Le Digabel S, Salomon L (2021) Performance indicators in multiobjective optimization. European Journal of Operational Research 292(2):397–422. https://doi.org/10.1016/j.ejor.2020.11.016

    Article  MathSciNet  Google Scholar 

  29. Ishibuchi H, Masuda H, Tanigaki Y, Nojima Y (2015) Modified distance calculation in generational distance and inverted generational distance. Lecture Notes in Computer Science 110–125. https://doi.org/10.1007/978-3-319-15892-1_8

  30. Fonseca CM, Paquete L, Lopez–Ibanez M (2006) An improved dimensionsweep algorithm for the hypervolume indicator. 2006 IEEE International Conference on Evolutionary Computation. https://doi.org/10.1109/cec.2006.1688440

  31. Ishibuchi H, Masuda H, Nojima Y (2015) A study on performance evaluation ability of a modified inverted generational distance indicator. Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation. https://doi.org/10.1145/2739480.2754792

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Authors and Affiliations

  1. RIIMA, USTHB, BP 32 El-Alia, Bab-Ezzouar, 16111, Algiers, Algeria

    Ahmed Zebouchi & Youcef Aklouf

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  1. Ahmed Zebouchi
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  2. Youcef Aklouf
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Contributions

Ahmed Zebouchi conceptualization, methodology, software, experimentation, formal analysis, investigation, writing, and editing. Youcef Aklouf methodology, resources, and supervision.

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Correspondence to Ahmed Zebouchi.

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Zebouchi, A., Aklouf, Y. pRTMNSGA-III: a novel multi-objective algorithm for QoS-aware multi-cloud IoT service selection. Ann. Telecommun. (2024). https://doi.org/10.1007/s12243-023-01006-0

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