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EVACON-Rainsnow Computing: An Amalgamation of Cloud and Its Inherited Computing to Encourage End User for Both Localized and Globalized Remote Computing

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Abstract

The demand for cloud enabled computing is rising which motivated the researchers to develop various computings such as mobile cloud computing, edge computing, transparent computing, fog computing, federated cloud etc. This paper discusses different distributed remote computing techniques and its related aspects. It proposes a new computing paradigm for distributed remote computing named as EVACON (Evaporation–Condensation)-Rainsnow Computing. As the name suggests the term EVACON-Rainsnow represents the environmental phenomena of evaporation, condensation, rain, and snow. How these distributed computing is related to this environmental phenomenon is discussed in detail in this manuscript. The proposed work represents the comparative analysis of new computing with the existing computing technologies. It also demonstrates the detailed architecture, feature, and benefits of EVACON-Rainsnow Computing. This paper explains principle, components, working architecture, functionality of different layers, advantages, applications, and challenges involved with proposed computing. In this work, existing SKYR framework for distributed computing of mobile cloudlet-based computing is improved further by incorporating proposed Task-Segregation () and Scalability () algorithms to accommodate federated cloud and dew computing which comprehensively make it best suited for the proposed computing. Working flow and architecture of this improved framework to execute proposed computing and its comparison with the different frameworks is also illustrated in this paper.

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The data and material associated with the manuscript are available from the corresponding author, upon reasonable request.

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The code that support the findings of this study is available from the corresponding author, upon reasonable request.

References

  1. IBM, IBM introduces ready-to-use cloud computing. Retrieved 2007, from https://www-03.ibm.com/press/us/en/pressrelease/22613

  2. Bort, J. (2017) Amazon’s massive cloud business hit over $12 billion in revenue and $3 billion in profit in 2016. Retrieved 2007, from http://www.businessinsider.com/amazons-cloud-businesshits-over-12-billion-in-revenue-2017-2

  3. Zhou, Y., Zhang, D., & Xiong, N. (2017). Post-cloud computing paradigms: A survey and comparison. Journal of Tsinghua Science and Technology, 22(6), 714–732.

    Article  Google Scholar 

  4. Luong, N. C., Wang, P., Niyato, D., Yonggang, W., & Han, Z. (2017). Resource management in cloud networking using economic analysis and pricing models: A survey. Journal of IEEE Communications Surveys & Tutorials, 19(2), 954–1001.

    Article  Google Scholar 

  5. Zhu, Q., Tang, H., Huang, J., & Hou, Y. (2021). Task scheduling for multi-cloud computing subject to security and reliability constraints. Journal of IEEE/CAA Automatica Sinica., 8(4), 848–865.

    Article  MathSciNet  Google Scholar 

  6. Lipsa, S., Dash, R. K., Ivkovic, N., & Cengiz, K. (2023). Task scheduling in cloud computing: A priority-based heuristic approach. Journal of IEEE Access, 11(1), 27111–27126.

    Article  Google Scholar 

  7. Wright, A. (2009). Get smart. Journal of Communication, ACM, 52(1), 15–16.

    Article  Google Scholar 

  8. Kemp, R., Palmer, N., Kielmann, T., Seinstra, F., Drost, N., Maassen, J., & Bal, H. (2009) Eyedentify: Multimedia cyber foraging from a smartphone. In 11th IEEE international symposium on multimedia ISM’09 (pp. 392–399). IEEE.

  9. Huang, D. (2011). Mobile cloud computing. Journal of IEEE COMSOC Multimedia Communications Technical Committee (MMTC), 6(10), 27–31.

    Google Scholar 

  10. Wang, C., Ren, K., Lou, W., & Li, J. (2010). Toward publicly auditable secure cloud data storage services. Journal of IEEE Network, 24(4), 19–24.

    Article  Google Scholar 

  11. Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach, D. A., Burrows, M., Chandra, T., Fikes, A., & Gruber, R. E. (2008). Bigtable: A distributed storage system for structured data. Journal of ACM Transactions of Computer Systems (TOCS), 26(2), 4–10.

    Google Scholar 

  12. Sakr, S., Liu, A., Batista, D. M., & Alomari, M. (2011). A survey of large-scale data management approaches in cloud environments. Journal of IEEE Communication Surveys & Tutorials, 13(3), 311–336.

    Article  Google Scholar 

  13. Fan, X., Cao, J., & Mao, H. (2011). A survey of mobile cloud computing. Journal of ZTE Communications, 9(1), 4–8.

    Google Scholar 

  14. Satyanarayanan, M., Bahl, P., Caceres, R., & Davies, N. (2009). The case for vm-based cloudlets in mobile computing. Journal of IEEE Pervasive Computing, 8(4), 14–23.

    Article  Google Scholar 

  15. Satyanarayanan, M., Chen, Z., Ha, K., Hu, W., Richter, W., & Pillai, P. (2014). Cloudlets: At the leading edge of mobile-cloud convergence. In 6th IEEE international conference on mobile computing, applications and services (MobiCASE) (pp. 1–9).

  16. Verbelen, T., Simoens, P., De Turck, F., & Dhoedt, B. (2012). Cloudlets: Bringing the cloud to the mobile user. In Proceedings of the third ACM workshop on mobile cloud computing and services (pp. 29–36). ACM.

  17. Li, Y., & Wang, W. (2014). Can mobile cloudlets support mobile applications. In Proceedings of IEEE INFOCOM (pp. 1060–1068).

  18. Rawadi, J. M., Artail, H., & Safa, H. (2014). Providing local cloud service to mobile devices with intercloudlet communication. In Proceedings of 17th IEEE Mediterranean electrotechnical conference, Beirut, Lebanon (pp. 134–138).

  19. Artail, A., Frenn, K., Artail, H., & Safa, H. (2015). A framework of mobile cloudlet center based on the use of mobile devices as cloudlets. In Proceedings of 29th IEEE international conference on advanced information networking and applications (pp. 777–784).

  20. Wang, H., Cai, L., Hao, X., Ren, J., & Ma, Y. (2023). ETS-TEE: An energy-efficient task scheduling strategy in a mobile trusted computing environment. Journal of Tsinghua Science and Technology, 28(1), 105–116.

    Article  Google Scholar 

  21. Kai, K., Cong, W., & Tao, L. (2016). Fog computing for vehicular ad-hoc networks: Paradigms, scenarios, and issues. The Journal of China Universities of Posts and Telecommunications, 23(2), 56–96.

    Article  Google Scholar 

  22. Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018). Mobile edge computing: A survey. Journal of IEEE Internet of Things, 5(1), 450–465.

    Article  Google Scholar 

  23. Jararweh, Y., Doulat, A., AlQudah, O., Ahmed, E., Al-Ayyoub, M., & Benkhelifa, E. (2015). The future of mobile cloud computing: Integrating cloudlets and mobile edge computing. In 23rd international conference on telecommunications (ICT) (pp. 1–5).

  24. Kitanov, S., Monteiro, E., & Janevski, T. (2016). 5g and the fog 2014: Survey of related technologies and research directions. In 18th Mediterranean electrotechnical conference (MELECON) (pp. 1–6).

  25. Beck, M. T., Werner, M., Feld, S., & Schimper, S. (2014). Mobile edge computing: A taxonomy. In Proceedings of the sixth international conference on advances in future internet, Citeseer.

  26. Yi, S., Li, C., & Li, Q. (2015). A survey of fog computing: Concepts, applications and issues. In Proceedings of the workshop on mobile big data, ser. Mobidata ’15 (pp. 37–42). ACM. [Online]. https://doi.org/10.1145/2757384.2757397

  27. Jararweh, Y., Doulat, A., Darabseh, A., Alsmirat, M., Al-Ayyoub, M., & Benkhelifa, E. (2016). Sdmec: Software defined system for mobile edge computing. In IEEE International Conference on Cloud Engineering Workshop (IC2EW) (pp. 88–93).

  28. Roman, R., Lopez, J., & Mambo, M. (2016). Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, [Online]. http://www.sciencedirect.com/science/article/pii/S0167739X16305635

  29. Ahmed, A., & Ahmed, E. (2016). A survey on mobile edge computing. In 10th international conference on intelligent systems and control (ISCO) (pp. 1–8).

  30. Borgia, E., Bruno, R., Conti, M., Mascitti, D., & Passarella, A. (2016). Mobile edge clouds for information-centric IoT services. In IEEE symposium on computers and communication (ISCC) (pp. 422–428).

  31. Marotta, M. A., Faganello, L. R., Schimuneck, M. A. K., Granville, L. Z., Rochol, J., & Both, C. B. (2015). Managing mobile cloud computing considering objective and subjective perspectives. Journal of Computer Networks., 93(3), 531–542.

    Article  Google Scholar 

  32. Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013). A survey of mobile cloud computing: Architecture, applications, and approaches. Journal of Wireless Communications and Mobile Computing, 13(18), 1587–1611.

    Article  Google Scholar 

  33. Hu, Y. C., Patel, M., Sabella, D., Sprecher, N., & Young, V. (2015). Mobile edge computing a key technology towards 5G. ETSI White Paper, 11(1), 1–16.

    Google Scholar 

  34. Asrani, P. (2013). Mobile cloud computing. International Journal of Engineering and Advanced Technology (IJEAT)., 2(4), 606–609.

    Google Scholar 

  35. Patel, M., Naughton, B., Chan, C., Sprecher, N., Abeta, S., & Neal, A. (2014). Mobile-edge computing introductory technical white paper. White Paper, Mobile-edge computing (MEC) industry initiative.

  36. Li, G., & Xu, Y. (2019). Energy consumption averaging and minimization for the software defined wireless sensor networks with edge computing. Journal of IEEE Access, 7(1), 173086–173097.

    Article  Google Scholar 

  37. Chen, Y., Zhao, F., Lu, Y., & Chen, X. (2023). Dynamic task offloading for mobile edge computing with hybrid energy supply. Journal of Tsinghua Science and Technology, 28(3), 421–432.

    Article  Google Scholar 

  38. Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the Internet of Things. In Proceeding 1st Ed. MCC workshop mobile cloud computing (pp. 13–16).

  39. Xie, X., Zeng, H. J., & Ma, W. Y. (2002). Enabling personalization services on the edge. In Proceedings of 10th ACM international conference on multimedia (pp. 263–266).

  40. Gelsinger, P. P. (2001). Microprocessors for the new millennium: Challenges, opportunities, and new frontiers. In Proceedings IEEE international solid-state circuits conference (pp. 22–25).

  41. Ibrahim, S., Jin, H., Cheng, B., Cao, H., Wu, S., & Qi, L. (2009). CLOUDLET: Towards MapReduce implementation on virtual machines. In Proceedings of 18th ACM international symposium on high perform. Distributed computing (pp. 65–66).

  42. Gonzalez, N. M. (2016). Fog computing: Data analytics and cloud distributed processing on the network edges. In Proceedings of 35th international conference on Chilean computer science society (SCCC) (pp. 1–9).

  43. Dastjerdi, A. V., Gupta, H., Calheiros, R. N., Ghosh, S. K., & Buyya, R. (2016). Fog computing: Principles, architectures, and applications. In Proceedings of Internet of Things: Principle & paradigms. San Mateo, CA, USA.

  44. Li, J., Zhang, T., Jin, J., Yang, Y., Yuan, D., & Gao, L. (2017). Latency estimation for fog-based Internet of Things. In Proceedings of 27th international telecommunication network application conference (ITNAC) (pp. 1–6).

  45. Hu, P., Dhelim, S., Ning, H., & Qiu, T. (2017). Survey on fog computing: Architecture, key technologies, applications, and open issues. Journal of Networks and Computer Application, 98(1), 27–42.

    Article  Google Scholar 

  46. Yi, S., Li, C., & Li, Q. (2015). A survey of fog computing: Concepts, applications, and issues. In Proceedings of workshop mobile big data (pp. 37–42).

  47. Perera, C., Qin, Y., Estrella, J. C., Reiff-Marganiec, S., & Vasilakos, A. V. (2017). Fog computing for sustainable smart cities: A survey. Journal of ACM Computing Surveys, 50(3), 1–43.

    Google Scholar 

  48. Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2018). A comprehensive survey on fog computing: State-of-the art and research challenges. Journal of IEEE Communication Surveys and Tutorials, 20(1), 416–464.

    Article  Google Scholar 

  49. Huang, C., Lu, R., & Choo, K. K. R. (2017). Vehicular fog computing: Architecture, use case, and security and forensic challenges. Journal of IEEE Communication Magazine, 55(11), 105–111.

    Article  Google Scholar 

  50. Fog computing and the Internet of Things: Extend the cloud to where the things are. Cisco, San Jose, CA, USA, White Paper (2015). [Online]. https://www.cisco.com/c/dam/en_us/solutions/trends/iot/../computing-overview.pdf

  51. Vaquero, L. M., & Rodero-Merino, L. (2014). Finding your way in the fog: Towards a comprehensive definition of fog computing. ACMSIGCOMM Computer Communication Review., 44(5), 27–32.

    Article  Google Scholar 

  52. Garfinkel, S. (1999). Architects of the information society: 35 years of the Laboratory for Computer Science at MIT. MIT Press.

    Book  Google Scholar 

  53. Mobile edge computing: Introductory technical white paper (2014). ETSI. https://portal.etsi.org/Portals/0/TBpages/MEC/Docs/Mobile-edge%20Computing-IntroductoryTechnicalWhitePaper-V1%2018-09-14.pdf

  54. Bonomi, F. (2011). Connected vehicles, the internet of things, and fog computing. In Proceeding of 8th ACM international workshop on vehicular inter-networking, Las Vegas, NV, USA.

  55. Wang, Y. W. (2015). Cloud dew architecture. International Journal of Cloud Computing, 4(3), 199–210.

    Article  Google Scholar 

  56. Zhang, Y. (2004). Transparence computing: Concept, architecture, and example. Journal of Acta Electronica Sinica, 32(12), 169–173.

    Google Scholar 

  57. Hurwitz, J. S., Bloor, R., Kaufman, M., & Halper, F. (2009). Cloud computing for dummies. Wiley.

    Google Scholar 

  58. Mobile edge computing (2017). ETSI. https://portal.etsi.org/tb.aspx?tbid=826&SubTB=826,835

  59. Open Fog Consortium (2015). Open Fog. http://www.openfogconsortium.org

  60. Yang, M., Ma, H., Wei, S., Zeng, Y., Chen, Y., & Hu, Y. (2020). A multi-objective task scheduling method for fog computing in cyber-physical-social services. Journal of IEEE Access, 8(1), 65085–65095.

    Article  Google Scholar 

  61. Branch, R. (2014). Cloud computing and big data: A review of current service models and hardware perspectives. Journal of Software Engineering and Applications, 7(1), 686–693.

    Article  Google Scholar 

  62. Zhou, Y., Zhang, Y., Xie, Y., Zhang, H., Yang, L. T., & Min, G. (2014). TransCom: A virtual disk-based cloud computing platform for heterogeneous services. Journal of IEEE Transactions on Network & Service Management, 11(1), 46–59.

    Article  Google Scholar 

  63. Zhang, Y. (2004). Transparence computing: Concept, architecture and example. Journal of Acta Electronica Sinica, 32(12A), 169–173.

    Google Scholar 

  64. Zhang, Y. (2008). The challenges and opportunities in transparent computing. In Proceedings of IEEE/IFIP EUC, Shanghai, China.

  65. Kuang, W. (2009). NSAP: A network storage access protocol for transparent computing. Journal of Tsinghua University Science and Technology, 49(1), 106–109.

    Google Scholar 

  66. Zhang Y., & Zhou, Y. (2006). Transparent computing: A new paradigm for pervasive computing. In Proceedings of international conference on ubiquitous intelligence and computing (pp. 1–11). Springer.

  67. Zhou, Y., Zhang, Y., Liu, H., Xiong, N., & Vasilakos, A. V. (2014). A bare-metal and asymmetric partitioning approach to client virtualization. Journal of IEEE Transaction on Services Computing, 7(1), 40–53.

    Article  Google Scholar 

  68. Wu, M. (2012). Analysis and a case study of transparent computing implementation with UEFI. International Journal of Cloud Computing, 1(4), 312–328.

    Article  Google Scholar 

  69. Wang, Y. W. (2015). The relationships among cloud computing, fog computing, and dew computing. http://www.dewcomputing.org/index.php/2015/11/12/therelationships-among-cloud-computingfogcomputingand-dew-computing

  70. Wang, Y. W. (2015). The initial definition of dew computing. http://www.dewcomputing.org/index.php/2015/11/10/theinitial-definition-of-dew-computing

  71. Skala, K., Davidovic, D., Afgan, E., Sovic, I., & Sojat, Z. (2015). Scalable distributed computing hierarchy: Cloud, fog and dew computing. Open Journal of Cloud Computing, 2(1), 16–24.

    Google Scholar 

  72. Wang, Q., Guo, S., Liu, J., Pan, C., & Yang, L. (2019). Profit maximization incentive mechanism for resource providers in mobile edge computing. In Proceedings of IEEE transactions on services computing (pp. 1–12).

  73. Kumar, R., & Yadav, S. K. (2017). Scalable key parameter yields of resources model for performance enhancement in mobile cloud computing. Springer Journal of Wireless Personal Communications, 95(4), 3969–4000.

    Article  Google Scholar 

  74. Qi, Q., & Tao, F. (2019). A smart manufacturing service system based on edge computing, fog computing, and cloud computing. Journal of IEEE Access, 7(1), 86769–86777.

    Article  Google Scholar 

  75. Yadav, S. K., & Kumar, R. (2021). A mobile cloud computing framework for execution of data as a service using cloudlet. Kuwait Journal of Science, 48(3), 1–12.

    Article  MathSciNet  Google Scholar 

  76. Yadav, S. K., & Kumar, R. (2021). A scalable and utility driven profit maximized auction of resources model for cloudlet based mobile edge computing. Springer Journal of Wireless Personal Communications, 119(1), 527–565.

    Article  Google Scholar 

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Yadav, S.K., Kumar, R. EVACON-Rainsnow Computing: An Amalgamation of Cloud and Its Inherited Computing to Encourage End User for Both Localized and Globalized Remote Computing. Wireless Pers Commun 132, 2737–2792 (2023). https://doi.org/10.1007/s11277-023-10741-5

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