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Trust Management Techniques and their Challenges in Cloud Computing: A Review


Affiliations
1 Department of Computer Science and Application, MD University, Rohtak (Haryana),, India
 

.Cloud computing is a way to handle tasks like development, production, and maintenance done on the web. This domain is evolving. It uses a pay-per-use system like an electric bill and can be used to run virtual machines. Customers are rapidly adopting and shifting the companies that provide such services due to the presence of numerous service providers. It is also customizable as per users’ requirements but poses several security risks. It is dynamic and can be updated to meet the needs of both the client and the service provider. It is a significant feature of such distributed computing platforms. However, this undermines trust and credibility and generates security, protection, individuality, and authenticity problems. Consequently, selecting an appropriate service provider is the most critical test in the cloud environment. The Trust system is an essential part of how QoS and feedback ratings are judged to evaluate the service. Even so, the executive's plan for observing and evaluating QoS still needs to get past several tests. This paper examines the current impediments to trust in the existing trust framework. This report includes a systematic review of various high-quality articles published on trust management between 2010 and July 2022. To do this, some strategies for managing trust are put into four groups: SLA, suggestion, feedback, and prediction. This article also compares the pros and cons, evaluation methods, tools, and simulation settings of different management models.

Keywords

False Rating, Subjectivity, Cloud Environment, Service Level Agreement, Reputation System, Quality of Service (QoS).
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  • P. Varalakshmi, T. Judgi, and D. Balaji, “Trust Management Model Based on Malicious Filtered Feedback in Cloud,” Commun. Comput. Inf. Sci., vol. 804, pp. 178–187, 2018, doi: 10.1007/978-981-10-8603- 8_15.

  • T. H. Noor, Q. Z. Sheng, S. Zeadally, and J. Yu, “Trust management of services in cloud environments,” ACM Comput. Surv., vol. 46, no. 1, pp. 1–30, 2013, doi: 10.1145/2522968.2522980.

  • S. Soltani, M. Asadi, D. Gašević, M. Hatala, and E. Bagheri, “Automated planning for feature model configuration based on functional and non-functional requirements,” in Proceedings of the 16th International Software Product Line Conference-Volume 1, pp. 56– 65,2012, doi: 10.1145/2362536.2362548

  • X. Yang, S. Wang, B. Yang, C. Ma, and L. Kang, “A service satisfaction-based trust evaluation model for cloud manufacturing,” Int. J. Comput. Integr. Manuf., vol. 32, no. 6, pp. 533–545, 2019, doi: 10.1080/0951192X.2019.1575982.

  • E. Kristiani, C. T. Yang, Y. T. Wang, and C. Y. Huang, “Implementation of an edge computing architecture using openstack and kubernetes,” Lect. Notes Electr. Eng., vol. 514, pp. 675–685, 2019, doi: 10.1007/978-981-13-1056-0_66.

  • G.Aghaee, G. Mehran, and M. Ramin, “A new multi ‑ level trust management framework ( MLTM ) for solving the invalidity and sparse problems of user feedback ratings in cloud environments,” J. Supercomput., no. 0123456789, 2020, doi: 10.1007/s11227-020-03348- 1.

  • P. Varalakshmi, T. Judgi, and D. Balaji, “Trust Management Model Based on Malicious Filtered Feedback in Cloud,” Commun. Comput. Inf. Sci., vol. 804, pp. 178–187, 2018, doi: 10.1007/978-981-10-8603- 8_15.

  • M. R. Thanka, P. Uma Maheswari, and E. B. Edwin, “An improved efficient: Artificial Bee Colony algorithm for security and QoS aware scheduling in cloud computing environment,” Cluster Comput., vol. 22, no. 5, pp. 10905–10913, 2019.

  • H. Kurdi, A. Alfaries, A. A. S. Alkharji, M. Addegaither, L. Altoaimy, and S. Hassan, “A lightweight trust management algorithm based on subjective logic for interconnected cloud computing environments,” J. Supercomput., 2018, doi: 10.1007/s11227-018-2669-y.

  • S. S. Manvi and G. Krishna Shyam, “Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey,” J. Netw. Comput. Appl., vol. 41, no. 1, pp. 424–440, 2014, doi: 10.1016/j.jnca.2013.10.004.

  • E. Kristiani, C. T. Yang, Y. T. Wang, and C. Y. Huang, “Implementation of an edge computing architecture using openstack and kubernetes,” Lect. Notes Electr. Eng., vol. 514, pp. 675–685, 2019, doi: 10.1007/978-981-13-1056-0_66.

  • https://en.wikipedia.org/wiki/Cloud_computing

  • S. S. Manvi and G. Krishna Shyam, “Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey,” J. Netw. Comput. Appl., vol. 41, no. 1, pp. 424–440, 2014, doi: 10.1016/j.jnca.2013.10.004.

  • Machhi, Sandip, and G. B. Jethava. "Feedback based trust management for cloud environment." In Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies, pp. 1-5. 2016

  • Siani Pearson. Privacy, security and trust in cloud computing. In Privacy and security for cloud computing, pages 3–42. Springer, 2013.

  • Talal H Noor, Quan Z Sheng, SheraliZeadally, and Jian Yu. Trust management of services in cloud environments: Obstacles and solutions. ACM Computing Surveys (CSUR), 46(1):1–30, 2013.

  • R Thirukkumaran et al. Survey: Security and trust management in internet of things. In 2018 IEEE global conference on wireless computing and networking (GCWCN), pages 131–134. IEEE, 2018.

  • J. Huang and M. S. Fox, “An ontology of trust - Formal semantics and transitivity,” ACM Int. Conf. Proceeding Ser., no. January, pp. 259– 270, 2006, doi: 10.1145/1151454.1151499.

  • V. K. Damera, A. Nagesh, and M. Nagaratna, “Trust evaluation models for cloud computing,” Int. J. Sci. Technol. Res., vol. 9, no. 2, pp. 1964– 1971, 2020.

  • M. Chiregi and N. J. Navimipour, “A new method for trust and reputation evaluation in the cloud environments using the recommendations of opinion leader’s entities and removing the effect of troll entities,” Comput. Human Behav., vol. 60, pp. 280–292, Jul. 2016, doi: 10.1016/j.chb.2016.02.029.

  • R. Nagarajan, R. Thirunavukarasu, and S. Shanmugam, “A FuzzyBased Intelligent Cloud Broker with MapReduce Framework to Evaluate the Trust Level of Cloud Services Using Customer Feedback,” Int. J. Fuzzy Syst., vol. 20, no. 1, pp. 339–347, 2018, doi: 10.1007/s40815-017-0347-5

  • Q. Duan, “Cloud service performance evaluation: status, challenges, and opportunities – a survey from the system modeling perspective,” Digit. Commun. Networks, vol. 3, no. 2, pp. 101–111, 2017, doi: 10.1016/j.dcan.2016.12.002.

  • M. Tang, X. Dai, J. Liu, and J. Chen, “Towards a trust evaluation middleware for cloud service selection,” Futur. Gener. Comput. Syst., vol. 74, pp. 302–312, 2017, doi: 10.1016/j.future.2016.01.009.

  • M. B. Smithamol and S. Rajeswari, “TMM: Trust Management Middleware for Cloud Service Selection by Prioritization,” J. Netw. Syst. Manag., vol. 27, no. 1, pp. 66–92, 2019, doi: 10.1007/s10922- 018-9457-0.

  • W. Fan and H. Perros, “A Reliability-based Trust Management Mechanism for Cloud Services,” 2013, doi: 10.1109/TrustCom.2013.194.

  • S. K. Garg, S. Versteeg, and R. Buyya, “SMICloud : A Framework for Comparing and Ranking Cloud Services,” no. Vm, 2011, doi: 10.1109/UCC.2011.36.

  • N. Yadav and M. S. Goraya, “Two-way Ranking Based Service Mapping in Cloud Environment,” Futur. Gener. Comput. Syst., 2017, doi: 10.1016/j.future.2017.11.027.

  • Y. Wang, J. Wen, X. Wang, B. Tao, and W. Zhou, “A cloud service trust evaluation model based on combining weights and gray correlation analysis,” Secur. Commun. Networks, vol. 2019, 2019, doi: 10.1155/2019/2437062.

  • G. Obulaporam, N. Somu, G. R. ManiIyer Ramani, A. K. Boopathy, and S. S. Vathula Sankaran, “GCRITICPA: A CRITIC and grey relational analysis based service ranking approach for cloud service selection,” in International Conference on Intelligent Information Technologies, 2018, pp. 3–16.

  • N. N. a. C. V. V. a. B. M. A. a. o. Kumbhar, “The Comprehensive Approach for Data Security in Cloud,” International Journal of Computer Applications},, vol. 39, 2012.

  • R. R. Kumar and C. Kumar, “A multi criteria decision making method for cloud service selection and ranking,” Int. J. Ambient Comput. Intell., vol. 9, no. 3, pp. 1–14, 2018.

  • M. Jouini and L. B. A. Rabai, “A security framework for secure cloud computing environments,” in Cloud security: Concepts, methodologies, tools, and applications, IGI Global, 2019, pp. 249–263., DOI: 10.4018/978-1-5225-8176-5.ch011.

  • F. Nadeem, “A Unified Framework for User-Preferred Multi-Level Ranking of Cloud Computing Services Based on Usability and Quality of Service Evaluation,” IEEE Access, vol. 8, pp. 180054–180066, 2020.

  • E. Kristiani, C.-T. Yang, Y. T. Wang, and C.-Y. Huang, “Implementation of an edge computing architecture using openstack and kubernetes,” in International Conference on Information Science and Applications, 2018, pp. 675–685.

  • S. R. a. M. M. Sheikh Mahbub Habib, “Towards a trust management systemfor cloud computing,” p. 933–939, 2011.

  • S. Kaushik and C. Gandhi, “Multi-level Trust Agreement in Cloud Environment,” 2019 12th Int. Conf. Contemp. Comput. IC3 2019, pp. 1–5, 2019, doi:10.1109/IC3.2019.8844933.

  • A C. Qu, R. Buyya, A cloud trust evaluation system using hierarchical fuzzy inference system for service selection, in: Proc. - Int. Conf. Adv. Inf. Netw. Appl. AINA, no. May, 2014, pp. 850–857. doi:10.1109/AINA.2014.104.

  • A. K. Jaithunbi, S. Sabena, and L. SaiRamesh, “Trustevaluation of public cloud service providers using genetic algorithm with intelligent rules,” Wirel. Pers. Commun., vol. 121, no. 4, pp. 3281–3295, 2021.

  • M. B. Chhetri, Q. B. Vo, R. Kowalczyk, Policy-based automation of SLA establishment for cloud computing services, in: Proc. - 12th IEEE/ACM Int. Symp. Clust. Cloud Grid Comput. CCGrid 2012, IEEE, 2012, pp. 164–171. doi:10.1109/CCGrid.2012.116.

  • W. Jiang, G. Wang, M. Z. A. Bhuiyan, and J. Wu, “Understanding graph-based trust evaluation in online social networks: Methodologies and challenges,” Acm Comput. Surv., vol. 49, no. 1, pp. 1–35, 2016.

  • Ali-Eldin, A. M. T. (2022). A hybrid trust computing approach for IoT using social similarity and machine learning. PLoS ONE, 17(7 July) doi:10.1371/journal.pone.0265658.

  • Ali-Eldin, A. M. T. (2018). Trust prediction in online social rating networks. Ain Shams Engineering Journal, 9(4), 3103-3112. doi:10.1016/j.asej.2018.03.005

  • Hallappanavar, V. L., & Birje, M. N. (2022). Prediction of quality of service of fog nodes for service recommendation in fog computing based on the trustworthiness of users. Journal of Reliable Intelligent Environments, 8(2), 193-210. doi:10.1007/s40860-021-00149-y.

  • Sivabalaselvamani, D., Vidhyasree, S., Pavithra, P., Soundarya, G., &Preethika, M. (2019). Books and movies recommendation and rating prediction based on collaborative filtering networks. International Journal of Advanced Science and Technology, 29(5), 705-714.

  • Selvaraj, A., & Sundararajan, S. (2017). Evidence-based trust evaluation system for cloud services using fuzzy logic. International Journal of Fuzzy Systems, 19(2), 329-337. doi:10.1007/s40815-016- 0146-4.

  • Pandey, S., & Daniel, A. K. (2016). Fuzzy logic-based cloud service trustworthiness model. Paper presented at the Proceedings of 2nd IEEE International Conference on Engineering and Technology, ICE TECH 2016, 73-78. doi:10.1109/ICETECH.2016.7569215.

  • Wu, Z., & Zhou, Y. (2016). Customized cloud service trustworthiness evaluation and comparison using fuzzy neural networks. Paper presented at the Proceedings - International Computer Software and Applications Conference, 1 433-442. doi:10.1109/COMPSAC.2016.86.

  • Wang, J., Wang, M., Zhang, Z., & Zhu, H. (2022). Towards A trust evaluation framework against malicious behaviors of industrial IoT. IEEE Internet of Things Journal, 1-1. doi:10.1109/JIOT.2022.3179428.

  • Siadat, S., Rahmani, A. M., &Navid, H. (2017). Identifying fake feedback in cloud trust management systems using feedback evaluation component and bayesian game model. Journal of Supercomputing, 73(6), 2682-2704. doi:10.1007/s11227-016-1950-1.

  • D. a. P. V. a. P. A. a. S. A. a. S. S. Grimaldi, “A Feedback-Control Approach for Resource Management in Public Clouds,” 2015.

  • H. Kurdi et al., “A lightweight trust management algorithm based on subjective logic for interconnected cloud computing environments,” J. Supercomput., vol. 75, no. 7, pp. 3534–3554, 2019,doi: 10.1007/s11227-018-2669-y.

  • A. M. Mohammed, E. I. Morsy, and F. A. Omara,“Trust model for cloud service consumers,” in 2018 International Conference on Innovative Trends in Computer Engineering (ITCE), 2018, pp. 122– 129, doi: 10.1109/ITCE.2018.8316610.

  • H. Kurdi, A. Alfaries, A. A. S. Alkharji, M. Addegaither, L. Altoaimy, and S. Hassan, “A lightweight trust management algorithm based on subjective logic for interconnected cloud computing environments,” J. Supercomput., 2018, DOI: 10.1007/s11227-018-2669-y.

  • P. Varalakshmi, T. Judgi, and D. Balaji, “Trust Management Model Based on Malicious Filtered Feedback in Cloud,” Commun. Comput. Inf. Sci., vol. 804, pp. 178–187, 2018, doi: 10.1007/978-981-10-8603- 8_15.


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  • Trust Management Techniques and their Challenges in Cloud Computing: A Review

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Authors

Pooja Goyal
Department of Computer Science and Application, MD University, Rohtak (Haryana),, India
Sukhvinder Singh Deora
Department of Computer Science and Application, MD University, Rohtak (Haryana),, India

Abstract


.Cloud computing is a way to handle tasks like development, production, and maintenance done on the web. This domain is evolving. It uses a pay-per-use system like an electric bill and can be used to run virtual machines. Customers are rapidly adopting and shifting the companies that provide such services due to the presence of numerous service providers. It is also customizable as per users’ requirements but poses several security risks. It is dynamic and can be updated to meet the needs of both the client and the service provider. It is a significant feature of such distributed computing platforms. However, this undermines trust and credibility and generates security, protection, individuality, and authenticity problems. Consequently, selecting an appropriate service provider is the most critical test in the cloud environment. The Trust system is an essential part of how QoS and feedback ratings are judged to evaluate the service. Even so, the executive's plan for observing and evaluating QoS still needs to get past several tests. This paper examines the current impediments to trust in the existing trust framework. This report includes a systematic review of various high-quality articles published on trust management between 2010 and July 2022. To do this, some strategies for managing trust are put into four groups: SLA, suggestion, feedback, and prediction. This article also compares the pros and cons, evaluation methods, tools, and simulation settings of different management models.

Keywords


False Rating, Subjectivity, Cloud Environment, Service Level Agreement, Reputation System, Quality of Service (QoS).

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F217708