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Feedback-based fuzzy resource management in IoT using fog computing

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Abstract

The goal of Internet of Things (IoT) is to make “things” (wearable devices, smart cameras, sensors and smart home appliances) connect to internet. Large storage is required to store huge volume of data that is generated, data processing need to be carried out between IoT devices and the massive number of applications. This process can be made effectively with the help of cloud computing technology. Resources can be effectively utilized with the help of cloud, and IoT plays a significant role in managing the tasks that are to be offloaded to the cloud. The performance of the application is to be enhanced by providing Quality of Service (QoS) and the performance is evaluated in terms of QoS parameters like Power utilization, Makespan and Execution Time. The tasks are allocated based on priority. Fog computing paradigm is used in the proposed model to decrease the makespan of time. The projected mechanism is tested and compared with different present systems and is shown that proposed methodology produced effective results.

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References

  1. Amith K (2000) Artificial intelligence and soft computing behavioral and cognitive modeling of the human brain. CRC Press, Boca Raton

    Google Scholar 

  2. Bhadani A, Chaudhary S (2010) Performance evaluation of web servers using centralload balancing policy over virtual machines on cloud. In: Proceedings of the third annual ACM bangalore conference (COMPUTE), January 2010

  3. Jha S, Kumar R, Chatterjee JM, Khari M (2019) Collaborative handshaking approaches between internet of computing and internet of things towards a smart world: a review from 2009–2017. Telecommun Syst 70(4):617–634

    Article  Google Scholar 

  4. Chang C, Srirama SN, Mass J (2015) A middleware for discovering proximity-based service-oriented industrial internet of things. In: 2015 IEEE international conference on services computing (SCC), IEEE, New York, NY, USA, pp 130–137

  5. Bohn RB, Messina J, Liu F, Tong J, Mao J (2011) NIST Cloud computing reference architecture. In Services (SERVICES), 2011 IEEE conference, pp 594–596

  6. Silva BN, Khan M, Han K (2018) Load balancing integrated least slack time-based appliance scheduling for smart home energy management. Sensors 18:685

    Article  Google Scholar 

  7. Giang NK, Blackstock M, Lea R, Leung V (2015) Developing IoT applications in the fog: a distributed dataflow approach. In: 2015 5th international conference on the internet of things (IOT), IEEE, Seoul, South Korea, pp 155–162

  8. Buyya R, Ranjan R, Calheiros RN (2009) Modeling simulation of scalable cloud computing environments and the cloudSim toolkit challenges and opportunities. In: Proceedings of the 7th high performance computing and simulation conference (HPCS 2009, ISBN: 978-1-4244-4907-1, IEEE Press, New York, USA), Leipzig, Germany, June-2009, pp 21–24

  9. Mallikarjuna B, Shahjad M, Dohare A, Tulika (2019) Master slave scheduling architecture for data processing on internet of things. Int J Innov Technol Explor Eng (IJITEE), vol 8(5). Published By: Blue Eyes Intelligence Engineering and Sciences Publication, March 2019, pp 556–559, ISSN: 2278-3079

  10. Mallikarjuna B, Venkata Krishna P (2015) OLB: a nature inspired approach for load balancing in cloud computing. Cybern Inf Technol 15(4):138–148

    Google Scholar 

  11. Dhinesh Babu LD, Venkata Krishna P (2013) Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput 13(5):2292–2303

    Article  Google Scholar 

  12. Roy S, Chatterjee S, Das AK, Chattopadhyay S, Kumari S, Jo M (2018) Chaotic map-based anonymous user authentication scheme with user biometrics and fuzzy extractor for crowdsourcing Internet of Things. IEEE Intern Things J 5(4):2884–2895

    Article  Google Scholar 

  13. Tekriwal N, Madhumita P, Krishna V (2013) Integration of safety and smartness using cloud services—an insight to future innovations and advances in computer. In: Information, systems sciences, and engineering. Lecture notes in electrical engineering, Springer, Berlin, vol 152, pp 293–303

  14. Ojha T, Bera S, Misra S, Raghuwanshi NS (2014) Dynamic duty scheduling for green sensor-cloud applications. In: 2014 IEEE 6th international conference on cloud computing technology and science 2014 Dec 15. IEEE, pp 841–846

  15. Reddy TS, Raju DN, Kumar PR, Kumar SR (2018) Power aware-based workflow model of grid computing using ant-based heuristic approach. In: Big data analytics 2018. Springer, Singapore, pp 175–184

  16. Mishra SK, Puthal D, Sahoo B, Jena SK, Obaidat MS (2018) An adaptive task allocation technique for green cloud computing. J Supercomput 74(1):370–385

    Article  Google Scholar 

  17. Stanojevic R, Shorten R (2009) Load balancing vs. distributed rate limiting: a unifying framework for cloud control. In: Proceedings of IEEE ICC, Dresden, Germany, pp 1–6

  18. Hu Y, Blake R, Emerson D (1998) An optimal migration algorithm for dynamic load balancing. Concurr Pract Exp 10:467–483

    Article  Google Scholar 

  19. Zhang Z, Zhang X (2010) A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation. In: Proceedings of 2nd international conference on industrial mechatronics and automation (ICIMA), Wuhan, China, May 2010, pp 240–243

  20. Zhang Q, Yang LT, Castiglione A, Chen Z, Li P (2019) Secure weighted possibilistic c-means algorithm on cloud for clustering big data. Inf Sci 1(479):515–525

    Article  Google Scholar 

  21. Chen H, Wang, F, Helian, N, Akanmu, G (2013) User-priority guided Min–Min scheduling algorithm for load balancing in cloud computing. In: IEEE Parallel Computing Technologies (PARCOMPTECH), Feb-2013, pp 1–8

  22. Mallikarjuna B, Shahjad M, Dohare A, Tulika (2019) Feed forward approach for data processing in IoT over cloud. Int J Innov Technol Explor Eng (IJITEE), vol 8(5). Published By: Blue Eyes Intelligence Engineering & Sciences Publication, March 2019, pp: 899–903, ISSN: 2278-3079

  23. Ai L, Tang M, Fidge C (2010) QoS-oriented resource allocation and scheduling of multiple composite web services in a hybrid cloud using a random-key genetic algorithm Australian. J Intell Inf Process Syst 12(1):29–34

    Google Scholar 

  24. Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41:23–50

    Article  Google Scholar 

  25. Mallikarjuna B, Venkata Krishna P (2018) Nature inspired approach for load balancing of tasks in cloud computing using equal time allocation policy. Int J Innov Technol Explor Eng (IJITEE), vol. 8(2S2). Published By: Blue Eyes Intelligence Engineering & Sciences Publication, December 2018, pp 46–50, ISSN: 2278-3079

  26. Mallikarjuna B, Venkata Krishna P (2018) Nature inspired bee colony optimization model for improving for improving load balancing in cloud computing. Int J Innov Technol Explor Eng (IJITEE), vol 8(2S2). Published By: Blue Eyes Intelligence Engineering & Sciences Publication, December 2018, pp 51–55, ISSN: 2278-3079

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Arunkumar Reddy, D., Venkata Krishna, P. Feedback-based fuzzy resource management in IoT using fog computing. Evol. Intel. 14, 669–681 (2021). https://doi.org/10.1007/s12065-020-00377-w

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