International Journal of Reciprocal Symmetry and Theoretical Physics

Peer Reviewed Article

Vol. 7 (2020)

How Distributed Cloud Computing Works: An Overview

Published
21-04-2020

Abstract

The cloud computing model is generalized by distributed computing, which allows data and applications to be positioned, processed, and served from geographically dispersed locations to meet performance, redundancy, and compliance requirements. The classic cloud computing model provides users who wish to refrain from constructing, purchasing, or managing their information technology infrastructure with on-demand, metered access to computing resources such as storage, servers, databases, and applications. Public cloud service providers maintain and operate large server farms whose resources are shared amongst users. Users benefit from the isolation and security of their data thanks to virtualization techniques implemented in these server farms. Site redundancy that spans regions allows for recovery during outages or natural catastrophes. In addition, cloud users are not required to be involved in the monitoring or management processes necessary to keep the cloud operational. This study investigates whether distributed cloud service providers guarantee end-to-end management for the best data placement, computing processes, and network interconnections following the above specifications. And when seen from the perspective of someone who uses the cloud, the study offers a unified answer.

References

  1. Amin, R. (2019). Innovations in Information Systems for Business Functionality and Operations Management. ABC Research Alert, 7(3), 148–158. https://doi.org/10.18034/abcra.v7i3.546
  2. Achar, S. (2015). Requirement of Cloud Analytics and Distributed Cloud Computing: An Initial Overview. International Journal of Reciprocal Symmetry and Physical Sciences, 2, 12–18. https://upright.pub/index.php/ijrsps/article/view/70
  3. Achar, S. (2016). Software as a Service (SaaS) as Cloud Computing: Security and Risk vs. Technological Complexity. Engineering International, 4(2), 79-88. https://doi.org/10.18034/ei.v4i2.633
  4. Achar, S. (2018a). Data Privacy-Preservation: A Method of Machine Learning. ABC Journal of Advanced Research, 7(2), 123-129. https://doi.org/10.18034/abcjar.v7i2.654
  5. Achar, S. (2018b). Security of Accounting Data in Cloud Computing: A Conceptual Review. Asian Accounting and Auditing Advancement, 9(1), 60–72. https://4ajournal.com/article/view/70
  6. Bynagari, N. B. (2015). Machine Learning and Artificial Intelligence in Online Fake Transaction Alerting. Engineering International, 3(2), 115-126. https://doi.org/10.18034/ei.v3i2.566
  7. Chimakurthi, V. N. S. S. (2017). Cloud Security - A Semantic Approach in End to End Security Compliance. Engineering International, 5(2), 97-106. https://doi.org/10.18034/ei.v5i2.586
  8. Chimakurthi, V. N. S. S. (2019). Application Portfolio Profiling and Appraisal as Part of Enterprise Adoption of Cloud Computing. Global Disclosure of Economics and Business, 8(2), 129-142. https://doi.org/10.18034/gdeb.v8i2.610
  9. Colchester, K., Hagras, H., Alghazzawi, D. & Aldabbagh, G. (2016). A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms. Journal of Artificial Intelligence and Soft Computing Research, 7(1) 47-64. https://doi.org/10.1515/jaiscr-2017-0004
  10. Costa, M., Oliveira, D., Pinto, S. & Tavares, A. (2019). Detecting Driver’s Fatigue, Distraction and Activity Using a Non-Intrusive Ai-Based Monitoring System. Journal of Artificial Intelligence and Soft Computing Research, 9(4) 247-266. https://doi.org/10.2478/jaiscr-2019-0007
  11. Dipa, D. D., Abedin, K., Khan, M. M., & Hasan, M. M. (2015). Impacts of Energy Subsidy in Bangladesh: An Analysis. ABC Journal of Advanced Research, 4(1), 39-56. https://doi.org/10.18034/abcjar.v4i1.44
  12. Donepudi, P. K. (2017). Machine Learning and Artificial Intelligence in Banking. Engineering International, 5(2), 83-86. https://doi.org/10.18034/ei.v5i2.490
  13. Graziatti, L. V. (2017). The Treaty of Rome EEC and EURATOM 1957. ABC Research Alert, 5(3), Peru. https://doi.org/10.18034/abcra.v5i3.316
  14. Hasan, M. A. ul. (2013). Goodness of Cloud Computing: does Bangladesh ready for it. Global Disclosure of Economics and Business, 2(2), 159-167. https://doi.org/10.18034/gdeb.v2i2.184
  15. Hossain, S., Mrida, M. I. A., Das, P. C., Sayed, A., Rahman, N., & Rashid, M. M. (2015). Mimo Channel and Performance Analysis using OFDM System for Reduced Bit Error Rate. ABC Research Alert, 3(3), Bangladesh. https://doi.org/10.18034/abcra.v3i3.299
  16. Hossain, S., Rahman, M. S., & Yeasmin, S. (2019). Environmental Radioactivity Monitoring and Assessment of Excess Lifetime Cancer Risk to People in Demra Thana, Dhaka, Bangladesh. ABC Research Alert, 7(3), Bangladesh. https://doi.org/10.18034/abcra.v7i3.269
  17. Hughes, R. T. (1996). An evaluation of machine learning techniques for software effort estimation. University of Brighton.
  18. Jordanov, I., Petrov, N. & Petrozziello, A. (2018). Classifiers Accuracy Improvement Based on Missing Data Imputation. Journal of Artificial Intelligence and Soft Computing Research, 8(1) 31-48. https://doi.org/10.1515/jaiscr-2018-0002
  19. Koren, O., Hallin, C., Perel, N. & Bendet, D. (2019). Decision-Making Enhancement in a Big Data Environment: Application of the K-Means Algorithm to Mixed Data. Journal of Artificial Intelligence and Soft Computing Research, 9(4) 293-302. https://doi.org/10.2478/jaiscr-2019-0010
  20. Nasreen, H., & Hassan, M. (2019). Nitazoxanide – A New Option in Biliary Ascariasis. ABC Research Alert, 7(1), Bangladesh. https://doi.org/10.18034/abcra.v7i1.236
  21. Noshi C, Noynaert S. Jerome Schubert. Data Mining Approaches for Casing Failure Prediction and Prevention. presented at the International Petroleum Technology Conference. 2019; IPTC-19311-MS. Available: https://doi.org/10.2523/IPTC-19311-MS
  22. Noshi CI, Noynaert SF, Schubert JJ. Failure Predictive Analytics Using Data Mining: How to Predict Unforeseen Casing Failures? Presented at the Abu Dhabi International Petroleum Exhibition & Conference. 2018;SPE-193194-MS. Available: https://doi.org/10.2118/193194-MS
  23. Ojah M, Emumena E, Collins O, Oduh P, Steve A. Determination of Optimal Well Location in Bounded Reservoirs Using the Dimensionless Pressure Derivative. SPE-203627-MS. presented at the SPE Nigeria Annual International Conference and Exhibition; 2020. Available: https://doi.org/10.2118/203627-MS
  24. Ojha, A., & Bairagi, S. (2013). Corporate Restructuring. ABC Research Alert, 1(1), India. https://doi.org/10.18034/abcra.v1i1.243
  25. Reddy C.S.; Raju KVSN, (2009). An Improved Fuzzy Approach for COCOMO’s Effort Estimation using Gaussian Membership Function. Journal of software, 4(5), 452-459.
  26. Achar, S. (2019a). Behavioral and Perceptual Models for Secure Data Analysis and Management. Global Disclosure of Economics and Business, 8(2), 143-152. https://doi.org/10.18034/gdeb.v8i2.653
  27. Achar, S. (2019b). Early Consequences Regarding the Impact of Artificial Intelligence on International Trade. American Journal of Trade and Policy, 6(3), 119-126. https://doi.org/10.18034/ajtp.v6i3.634
  28. Shewalkar, A., Nyavanandi, D. & Ludwig, S. (2019). Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU. Journal of Artificial Intelligence and Soft Computing Research, 9(4) 235-245. https://doi.org/10.2478/jaiscr-2019-0006
  29. Taher-Uz-Zaman, M., Ahmed, M. S., Hossain, S., Hossain, S., & Jamal, G. R. A. (2014). Multipurpose Tactical Robot. Engineering International, 2(1), 21-27. https://doi.org/10.18034/ei.v2i1.204
  30. Vadlamudi, S. (2015). Enabling Trustworthiness in Artificial Intelligence - A Detailed Discussion. Engineering International, 3(2), 105-114. https://doi.org/10.18034/ei.v3i2.519
  31. Wittig, G., Finnie, G. (1997). Estimating software development effort withconnectionist models. Information and Software Technology 39 (7), 469–476.
  32. Yahaya, I., & Mato, U. (2017). Workforce Diversity and Organizational Effectiveness in 21st Century Business Arena. Asian Accounting and Auditing Advancement, 8(1), 24–29. https://doi.org/10.18034/4ajournal.v8i1.46
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