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Effective Load Balancing Strategy (ELBS) for Real-Time Fog Computing Environment Using Fuzzy and Probabilistic Neural Networks

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Abstract

Fog computing (FC) is an extension of cloud computing, however, it utilizes the resources close to the edge of the network. FC is a valuable choice to support real time applications such as healthcare, industrial systems, and intelligent traffic signs. However, Fog is a new emerging computing paradigm and still needs standardization in many issues especially in load balancing. This paper presents a new Effective Load Balancing Strategy (ELBS) for FC environment, which is suitable for Healthcare applications. ELBS tries to achieve effective load balancing in Fog environment via real-time scheduling as well as caching algorithms. It introduces several rules to accomplish reliable interconnections among fog servers. Moreover, the proposed ELBS guarantees a suitable interconnection among fog servers and both cloud and dew layer servers. ELBS is composed of five modules namely: (i) Priority Assigning Strategy (PAS), (ii) Data Searching Algorithm (DSA), (iii) External Data Requesting Algorithm (EDRA), (iv) Server Requesting Algorithm (SRA), and (v) Probabilistic Neural Network based Matchmaking Algorithm (PMA). PAS assigns a priority to each incoming Process (P) by considering three predefined parameters, which are; Predefined Priority (PP), Deadline Time (DT), and Task Size (TS). All those parameters are the inputs to a fuzzy inference system to assign the process priority. DSA is an algorithm to provide the required data for each arrived process in its fog region. EDRA is an algorithm used to search for the required data for each process in the neighbor servers. SRA is responsible for searching for the FS with the required capabilities for the incoming process. ELBS uses PMA to assign the process to the most appropriate server. It also defines a perfect methodology for a reliable connectivity among nodes. ELBS has been implemented and compared against recent load balancing techniques using iFogSim. Experimental results have shown that ELBS outperforms recent load balancing techniques as it achieves the lowest Average Turnaround Time and Failure Rate. Accordingly, ELBS is a suitable strategy to achieve load balancing in fog environment as it guarantees a reliable execution for real time applications.

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References

  1. Varghese, B., Buyya, R.: Next generation cloud computing: new trends and research directions. Future Gener. Comput. Syst. 79, 849–861 (2018)

    Article  Google Scholar 

  2. Tao, M., Ota, K., Dong, M.: DSARP: dependable scheduling with active replica placement for workflow applications in cloud computing. IEEE Trans. Cloud Comput. (2016). https://doi.org/10.1109/tcc.(2016).2628374

    Google Scholar 

  3. Buyya, R., Singh Gill, S.: Sustainable cloud computing: foundations and future directions. Bus. Technol. Dig. Transform. Strateg. Cut. Consort. 21(6), 1–5 (2018)

    Google Scholar 

  4. Zanoon, N., Al-Haj, A., Khwaldeh, S.M.: Cloud computing and big data is there a relation between the two: a study. Int. J. Appl. Eng. Res. 12(17), 6970–6982 (2017)

    Google Scholar 

  5. Dar, A.R., Ravindran, D.: A comprehensive study on cloud computing. In: Conference: Conference: 1st International Conference on Recent Developments in Science, Humanities & Management-2018 Organized By: Amar Singh College, Cluster University, Gogji Bagh, Srinagar, At Aamir Singh College, vol. 4 (2018)

  6. Li, X., Jiang, X., Garraghan, P., Wu, Z.: Holistic energy and failure aware workload scheduling in Cloud datacenters. Future Gener. Comput. Syst. 78, 887–900 (2018)

    Article  Google Scholar 

  7. Singh, S., Chana, I., Buyya, R.: STAR: SLA-aware autonomic management of cloud resources. IEEE Trans. Cloud Comput. 4, 1–6 (2017)

    Article  Google Scholar 

  8. Park, S., Hwang, M., Lee, S., Park, Y.B.: A generic software development process refined from best practices for cloud computing. Sustainability 7, 5321–5344 (2015)

    Article  Google Scholar 

  9. Hua, P., Dhelima, S., Ninga, H., Qiud, T.: Survey on fog computing: architecture, key technologies, applications and open issues. J. Netw. Comput. Appl. 98, 27–42 (2017)

    Article  Google Scholar 

  10. Atlam, H.F., Walters, R.J., Wills, G.B.: Fog computing and the internet of things: a review. Big Data Cogn. Comput. 2, 10 (2018)

    Article  Google Scholar 

  11. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Proceedings of the MCC Workshop on Mobile Cloud Computing, ACM, USA, pp. 13–16 (2012)

  12. Euclides, N., Gustavo, C., Fernando, A.: An algorithm to optimise the load distribution of fog environments. In: 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC), Banff (2017)

  13. Fan, Q., Ansari, N.: Towards workload balancing in fog computing empowered IoT. IEEE Trans. Netw. Sci. Eng. 6, 3–4 (2018)

  14. Gupta, H., Dastjerdi, A.V., Ghosh, S.K., Buyya, R.: iFogSim: a toolkit for modeling and simulation of resource management techniques in internet of things, edge and fog computing environments, vol. 47, pp. 1275–1296. Wiley, Hoboken. https://github.com/harshitgupta1337/fogsim

  15. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Workshop on Mobile cloud computing. ACM (2012)

  16. Deng, R., Lu, R., Lai, C., Luan, T.H.: Towards power consumption delay trade off by workload allocation in cloud-fog computing. In: Proceedings of IEEE International Conference on Communications (ICC), pp. 3909–3914 (2015)

  17. Tentori, M., Favela, J.: Activity-aware computing in mobile collaborative working environments. In: Proceedings of 13th International Conference on Groupware: Design, Implementation, and Use (CRIWG), Berlin, Germany, pp. 337–353 (2007)

  18. Vaquero, L.M., Rodero-Merino, L.: Finding your way in the fog. ACM SIGCOMM Comput. Commun. Rev. 44(5), 27–32 (2014)

    Article  Google Scholar 

  19. Cao, Y., Chen, S., Hou, P., Brown, D.: FAST: a fog computing assisted distributed analytics system to monitor fall for stroke mitigation. In: Proceedings of IEEE International Conference on Network Architecture Storage (NAS), pp. 2–11 (2015)

  20. Xu, K., Li, Y., Ren, F.: An energy-efficient compressive sensing framework incorporating online dictionary learning for long-term wireless health monitoring. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, pp. 804–808 (2016)

  21. Gia, T.N., Jiang, M., Rahmani, A.-M., Westerlund, T., Liljeberg, P., Tenhunen, H.: Fog computing in healthcare internet of things: a case study on ECG feature extraction. In: Proceedings of IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), pp. 356–363 (2015)

  22. Yannuzzi, M., Milito, R., Serral-Gracia, R., Montero, D., Nemirovsky, M.: Key ingredients in an IoT recipe: fog computing, cloud computing, and more fog computing. In: Proceedings of 19th international workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), pp. 325–329 (2014)

  23. Ghanbari, Shamsollah, Othman, Mohamed: A priority based job scheduling algorithm in cloud computing. Procedia Eng. 50, 778–785 (2012)

    Article  Google Scholar 

  24. Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In: Workshop on mobile cloud computing. ACM (2012)

  25. Henzinger, T.A., Singh, A.V., Singh, V., Wies, T.: Static scheduling in clouds (June 2011). See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/260300984

  26. Casavant, T., Kuhl, J.: A taxonomy of scheduling in general purpose distributed computing systems. IEEE Trans. Softw. Eng. 14(3), 141–154 (1988)

    Article  Google Scholar 

  27. Arora, M., Das, S.K., Biswas, R.: A decentralized scheduling and load balancing algorithm for heterogeneous grid environments. In: Proceedings of international conference on parallel processing workshop (ICPPW’02), Vancouver, British Columbia Canada, pp. 400–505 (2002)

  28. Xhafa, F., Abraham, A.: Computational models and heuristic methods for grid scheduling problems. Future Gener. Comput. Syst. 26, 608–621 (2010)

    Article  Google Scholar 

  29. Lee, Yun-Han: Improving job scheduling algorithms in a grid environment. Future Gener. Comput. Syst. 27, 991–998 (2011)

    Article  Google Scholar 

  30. Karthikeyan, B., Gopal, S., Venkatesh, S.: Partial discharge pattern classification using composite versions of probabilistic neural network inference engine. Expert Syst. Appl. 34, 1938–1947 (2008)

    Article  Google Scholar 

  31. Venkatesh, S., Gopal, S.: Robust Heteroscedastic Probabilistic Neural Network for multiple source partial discharge pattern recognition—significance of outliers on classification capability. Expert Syst. Appl. 38, 11501–11514 (2011)

    Article  Google Scholar 

  32. Khan, S., Parkinson, S., Qin, Y.: Fog computing security: a review of current applications and security solutions. J. Cloud Comput. Adv. Syst. Appl. 6, 19 (2017)

    Article  Google Scholar 

  33. Verma, M., Bhardawaj, N., Yadav, A.K.: An architecture for load balancing techniques for fog computing environment. Int. J. Comput. Sci. Commun. 6(2), 269–274. www.csjournals.com (2015)

  34. Song, F., Yang Ai, Z., Li, J.: Smart collaborative caching for information-centric IoT in fog computing. Sensors 10, 3–4 (2017)

  35. Yi, S., Qin, Z., Li, Q.: Security and privacy issues of fog computing: a survey. In: International Conference on Wireless Algorithms, Systems and Applications (WASA) (2015)

  36. Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data. ACM (2015)

  37. Satyanarayanan, M., Chen, Z., Ha, K., Hu, W., Richter, W., Pillai, P.: Cloudlets: at the leading edge of mobile-cloud convergence. In: IEEE International Conference on Mobile Computing, Applications and Services (MobiCASE) (2014)

  38. Willis, D.F., Dasgupta, A., Banerjee, S.: Paradrop: a multi-tenant platform for dynamically installed third party services on home gateways. In: ACM SIGCOMM workshop on distributed cloud computing (2014)

  39. Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., Koldehofe, B.: Mobile fog: a programming model for large-scale applications on the internet of things. In: ACM SIGCOMM workshop on Mobile cloud computing (2013)

  40. Ottenwäalder, B., Koldehofe, B., Rothermel, K., Ramachandran, U.: Migcep: operator migration for mobility driven distributed complex event processing. In: Proceedings of the ACM international conference on distributed event-based systems (2013)

  41. Zhu, J., Chan, D.S., Prabhu, M.S.: Improving web sites performance using edge servers in fog computing architecture. In: SOSE. IEEE (2013)

  42. Ha, K., Chen, Z., Hu, W., Richter, W., Pillai, P., Satyanarayanan, M.: Towards wearable cognitive assistance. In: Mobisys. ACM (2014)

  43. Shi, Y., Abhilash, S., Hwang, K.: Cloudlet mesh for securing mobile clouds from intrusions and network attacks. In: The Third IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (2015)

  44. Hong, K., Lillethun, D., Ramachandran, U., Ottenwälder, B., Koldehofe, B.: Opportunistic spatio-temporal event processing for mobile situation awareness. In: Proceedings of the ACM International Conference on Distributed Event-Based Systems (2013)

  45. Cao, Y., Hou, P., Brown, D., Wang, J., Chen, S.: Distributed analytics and edge intelligence: pervasive health monitoring at the era of fog computing. In: Proceedings of the 2015 Workshop on Mobile Big Data. ACM (2015)

  46. Hassan, M.A., Xiao, M., Wei, Q., Chen, S.: Help your mobile applications with fog computing. In: Fog Networking for 5G and IoT Workshop (2015)

  47. Tanaka, A., Utsunomiya, F., Douseki, T.: Wearable self-powered diaper-shaped urinary-incontinence sensor suppressing response-time variation with 0.3 V start-up converter. IEEE Sensors J 16(10), 3472–3479 (2016)

    Article  Google Scholar 

  48. Zhang, K., Liang, X., Baura, M., Lu, R., Shen, X.: PHDA: a priority based health data aggregation with privacy preservation for cloud assisted WBANs. Inf. Sci. 284, 130–141 (2014)

    Article  MathSciNet  Google Scholar 

  49. Oladimeji, E.A., Chung, L., Jung, H.T., Kim, J.: Managing security and privacy in ubiquitous ehealth information interchange. In: Proceedings of 5th International Conference on Ubiquitous Inforation Management on Communications (ICUIMC), New York, NY, USA, pp. 26:1–26:10 (2011). http://doi.acm.org/10.1145/1968613.1968645

  50. Perera, C., Talagala, D.S., Liu, C.H., Estrella, J.C.: Energy-efficient location and activity-aware on-demand mobile distributed sensing platform for sensing as a service in IoT clouds. IEEE Trans. Comput. Social Syst. 2(4), 171–181 (2015)

    Article  Google Scholar 

  51. Xu, K., Li, Y., Ren, F.: An energy-efficient compressive sensing framework incorporating online dictionary learning for long-term wireless health monitoring. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, pp 804–808 (2016)

  52. Monteiro, A., Dubey, H., Mahler, L., Yang, Q., Mankodiya, K.: Fit: a fog computing device for speech tele-treatments. In: Proceedings of IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–3 (2016)

  53. Hossain, M.S., Muhammad, G.: Cloud-assisted speech and face recognition framework for health monitoring. Mobile Netw Appl. 20(3), 391–399 (2015)

    Article  Google Scholar 

  54. Mei, B., Cheng, W., Cheng, X.: Fog computing based ultraviolet radiation measurement via smartphones. In: Proceedings of 3rd IEEE workshop hot topics web system technology (HotWeb), pp. 79–84 (2015)

  55. Dubey, H., Yang, J., Constant, N., Amiri, A.M., Yang, Q., Makodiya, K.: Fog data: enhancing telehealth big data through fog computing. In: Proceedings of ASE BigData SocialInform. (ASE BD&SI), p. 14 (2015)

  56. Nejati, H., Pomponiu, V., Do, T.-T., Zhou, Y., Iravani, S., Cheung, N.-M.: Smartphone and mobile image processing for assisted living: health monitoring apps powered by advanced mobile imaging algorithms. IEEE Signal Process. Mag. 33(4), 30–48 (2016)

    Article  Google Scholar 

  57. Nager, S.K., Gill, N.S.: Comparative study of RM and EDF scheduling algorithm in real time multiprocessor environment. Int. J. Comput. Sci. Mobile Comput. 6(3), 67–71 (2017)

    Google Scholar 

  58. Das, L., Mohapatra, D.P., Mohapatra, S.: Schedulability analysis for rate-monotonic algorithm in parallel real-time systems. Int. J. Appl. Eng. Res. 12(16), 5681–5689 (2017)

    Google Scholar 

  59. Choi, S., Cho, S., Park, J., Nam, B.: Earliest virtual deadline zero laxity scheduling for improved responsiveness of mobile GPUs. J. Semicond. Technol. Sci. 17(1), 162–166 (2017). https://doi.org/10.5573/JSTS.2017.17.1.162

    Article  Google Scholar 

  60. Shinde, V., Biday, S.C.: Comparison of real time task scheduling algorithms. Int. J. Comput. Appl. 158(6), 37–41 (2017)

    Google Scholar 

  61. Li, Q., Ba, W.: “A group priority earliest deadline first scheduling algorithm. Front. Comput. Sci. 6, 560–567 (2012). https://doi.org/10.1007/s11704-012-1104-4

    MathSciNet  MATH  Google Scholar 

  62. Liu, A., Chen, K., Liu, Q., Ai, Q., Xie, Y., Chen, A.: Feature selection for motor imagery EEG classification based on firefly algorithm and learning automata. Sensors (Basel) (2017). https://doi.org/10.3390/s17112576

    Google Scholar 

  63. SAIDI, P.: Motor imagery classification using sparse representation of EEG signals. M.S. Amirkabir University of Technology (Tehran Polytechnic) (2012)

  64. Dai, M., Zheng, D., Liu, S.H., Zhang, P.: Transfer kernel common spatial patterns for motor imagery brain-computer interface classification. Hindawi Comput. Math. Methods Med. (2018). https://doi.org/10.1155/2018/9871603

    MATH  Google Scholar 

  65. Wang, H., Zhang, Y., Waytowich, N.R., Krusienski, D.J., Zhou, G., Jin, J., Wang, X., Cichocki, A.: Discriminative feature extraction via multivariate linear regression for SSVEP-based BCI. IEEE Trans. Neural Syst. Rehabil. Eng. 9, 99 (2016). https://doi.org/10.1109/tnsre.2016.2519350

    Google Scholar 

  66. Zhou, G., Zhao, Q.: Linked component analysis from matrices to high-order tensors: applications to biomedical data. In: Proceedings of the IEEE (2016). https://doi.org/10.1109/jproc.2015.2474704

  67. Zhang, Y., Zhou, G., Jin, J.: Sparse Bayesian classification of EEG for brain–computer interface. IEEE Trans. Neural Netw. Learn. Syst. 27(11), 2256–2267 (2016). https://doi.org/10.1109/tnnls.2015.2476656

    Article  MathSciNet  Google Scholar 

  68. Gia, T.N., Jiang, M., Rahmani, A.-M., Westerlund, T., Liljeberg, P., Tenhunen, H.: Fog computing in healthcare Internet of Things: a case study on ECG feature extraction. In: Proceedings of IEEE international conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), pp. 356–363 (2015)

  69. Tentori, M., Favela, J.: Activity-aware computing in mobile collaborative working environments. In: Proceedings of 13th International Conference Groupware: Design, Implementation, and Use (CRIWG), Berlin, Germany, pp. 337–353 (2007)

  70. Masip-Bruin, X., Marín-Tordera, E., Alonso, A., Garcia, J.: Fog-to-cloud computing (F2C): the key technology enabler for dependable ehealth services deployment. In: Proceedings of Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), pp. 1–5 (2016)

  71. https://www.tutorialspoint.com/data_structures_algorithms/merge_sort_algorithm.htm

  72. Das, S., Ghosh, P.K.: Hypertension diagnosis: a comparative study using fuzzy expert system and neuro fuzzy system. In: Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Hyderabad, India, pp. 1–7 (2013)

  73. Saleh, A.I.: An efficient grid-scheduling strategy based on a fuzzy matchmaking approach. Soft Comput. Fusion Found. Methodol. Appl. 17(3), 467–487 (2013)

    Google Scholar 

  74. https://archive.ics.uci.edu/ml/datasets/MHEALTH+Dataset

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Authors and Affiliations

  1. Department of Computer Engineering and Systems, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Fatma M. Talaat, Ahmed I. Saleh & Hesham A. Ali

  2. Communications and Electronics Engineering Department, Delta Higher Institute for Engineering andTechnology, Mansoura, Egypt

    Shereen H. Ali

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  1. Fatma M. Talaat
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  2. Shereen H. Ali
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Talaat, F.M., Ali, S.H., Saleh, A.I. et al. Effective Load Balancing Strategy (ELBS) for Real-Time Fog Computing Environment Using Fuzzy and Probabilistic Neural Networks. J Netw Syst Manage 27, 883–929 (2019). https://doi.org/10.1007/s10922-019-09490-3

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