##common.pageHeaderLogo.altText##

Vol. 5 No. 12 (2018)

AI-Driven Decision-Making for Ensuring Data Reliability in Distributed Cloud Systems

International Journal of Emerging Trends in Science and Technology, Vol. 5 No. 12 (2018), 1 December 2018 , Page 6774-6794
https://doi.org/10.18535/ijetst/v5i12.01

Abstract

As distributed cloud systems continue to evolve and become integral to modern computing, ensuring data reliability remains a significant challenge. Distributed environments, while offering flexibility and scalability, face issues such as data inconsistency, network failures, and system downtime. Traditional methods of addressing these concerns, such as manual interventions or simple fault tolerance mechanisms, often fall short in maintaining consistent data reliability. This research explores the role of artificial intelligence (AI) in enhancing decision-making processes to ensure data reliability within distributed cloud systems. The study highlights how AI-driven models, particularly machine learning and predictive analytic, can pro-actively identify potential failures, optimize data storage, and implement self-healing strategies. By integrating AI into the decision-making framework of cloud systems, this paper demonstrates improved reliability, fault tolerance, and performance in real-time scenarios. Through a detailed analysis of an AI-powered framework, this research showcases the potential for AI to transform distributed cloud management, enabling adaptive and automated solutions for maintaining data consistency and reliability. The findings indicate that AI-driven approaches offer significant advantages over traditional methods, offering scalability, precision, and efficiency in ensuring the integrity of data across distributed cloud environments. Finally, the study concludes with recommendations for future research, including the integration of emerging AI technologies for even greater improvements in system reliability.


 

How to Cite

Pentyala, D. K. (2018). AI-Driven Decision-Making for Ensuring Data Reliability in Distributed Cloud Systems. International Journal of Emerging Trends in Science and Technology, 5(12), 6774–6794. https://doi.org/10.18535/ijetst/v5i12.01

Download Citation

References

  • Gonzalez, N. M., Carvalho, T. C. M. D. B., & Miers, C. C. (2017). Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures. Journal of Cloud Computing, 6, 1-20.
  • Zheng, Z., Zhu, J., & Lyu, M. R. (2013, June). Service-generated big data and big data-as-a-service: an overview. In 2013 IEEE international congress on Big Data (pp. 403-410). IEEE.
  • Chen, X., Lu, C. D., & Pattabiraman, K. (2014, November). Failure prediction of jobs in compute clouds: A google cluster case study. In 2014 IEEE International Symposium on Software Reliability Engineering Workshops (pp. 341-346). IEEE.
  • Malhotra, I., Gopinath, S., Janga, K. C., Greenberg, S., Sharma, S. K., & Tarkovsky, R.
  • (2014). Unpredictable nature of tolvaptan in treatment of hypervolemic hyponatremia:
  • case review on role of vaptans. Case reports in endocrinology, 2014(1), 807054.
  • Karakolias, S., Kastanioti, C., Theodorou, M., & Polyzos, N. (2017). Primary care
  • doctors’ assessment of and preferences on their remuneration: Evidence from Greek
  • public sector. INQUIRY: The Journal of Health Care Organization, Provision, and
  • Financing, 54, 0046958017692274.
  • Singh, V. K., Mishra, A., Gupta, K. K., Misra, R., & Patel, M. L. (2015). Reduction of
  • microalbuminuria in type-2 diabetes mellitus with angiotensin-converting enzyme
  • inhibitor alone and with cilnidipine. Indian Journal of Nephrology, 25(6), 334-339.
  • Karakolias, S. E., & Polyzos, N. M. (2014). The newly established unified healthcare
  • fund (EOPYY): current situation and proposed structural changes, towards an upgraded
  • model of primary health care, in Greece. Health, 2014.
  • Shilpa, Lalitha, Prakash, A., & Rao, S. (2009). BFHI in a tertiary care hospital: Does
  • being Baby friendly affect lactation success?. The Indian Journal of Pediatrics, 76, 655-
  • 657.
  • Polyzos, N. (2015). Current and future insight into human resources for health in Greece.
  • Open Journal of Social Sciences, 3(05), 5.
  • Gopinath, S., Janga, K. C., Greenberg, S., & Sharma, S. K. (2013). Tolvaptan in the
  • treatment of acute hyponatremia associated with acute kidney injury. Case reports in
  • nephrology, 2013(1), 801575.
  • Gopinath, S., Giambarberi, L., Patil, S., & Chamberlain, R. S. (2016). Characteristics and
  • survival of patients with eccrine carcinoma: a cohort study. Journal of the American
  • Academy of Dermatology, 75(1), 215-217.
  • Shakibaie-M, B. (2013). Comparison of the effectiveness of two different bone substitute
  • materials for socket preservation after tooth extraction: a controlled clinical study.
  • International Journal of Periodontics & Restorative Dentistry, 33(2).
  • Swarnagowri, B. N., & Gopinath, S. (2013). Ambiguity in diagnosing
  • esthesioneuroblastoma--a case report. Journal of Evolution of Medical and Dental
  • Sciences, 2(43), 8251-8255.
  • Gopinath, S., Janga, K. C., Greenberg, S., & Sharma, S. K. (2013). Tolvaptan in the
  • treatment of acute hyponatremia associated with acute kidney injury. Case reports in
  • nephrology, 2013(1), 801575.
  • Swarnagowri, B. N., & Gopinath, S. (2013). Pelvic Actinomycosis Mimicking
  • Malignancy: A Case Report. tuberculosis, 14, 15.
  • Gopinath, S., Giambarberi, L., Patil, S., & Chamberlain, R. S. (2016). Characteristics and
  • survival of patients with eccrine carcinoma: a cohort study. Journal of the American
  • Academy of Dermatology, 75(1), 215-217.
  • Swarnagowri, B. N., & Gopinath, S. (2013). Ambiguity in diagnosing
  • esthesioneuroblastoma--a case report. Journal of Evolution of Medical and Dental
  • Sciences, 2(43), 8251-8255.
  • Malhotra, I., Gopinath, S., Janga, K. C., Greenberg, S., Sharma, S. K., & Tarkovsky, R.
  • (2014). Unpredictable nature of tolvaptan in treatment of hypervolemic hyponatremia:
  • case review on role of vaptans. Case reports in endocrinology, 2014(1), 807054.
  • Swarnagowri, B. N., & Gopinath, S. (2013). Pelvic Actinomycosis Mimicking
  • Malignancy: A Case Report. tuberculosis, 14, 15.
  • Papakonstantinidis, S., Poulis, A., & Theodoridis, P. (2016). RU# SoLoMo ready?:
  • Consumers and brands in the digital era. Business Expert Press.
  • Poulis, A., Panigyrakis, G., & Panos Panopoulos, A. (2013). Antecedents and
  • consequents of brand managers’ role. Marketing Intelligence & Planning, 31(6), 654-673.
  • Poulis, A., & Wisker, Z. (2016). Modeling employee-based brand equity (EBBE) and
  • perceived environmental uncertainty (PEU) on a firm’s performance. Journal of Product
  • & Brand Management, 25(5), 490-503.
  • Mulakhudair, A. R., Hanotu, J., & Zimmerman, W. (2017). Exploiting ozonolysis-
  • microbe synergy for biomass processing: Application in lignocellulosic biomass
  • pretreatment. Biomass and bioenergy, 105, 147-154.
  • Abbas, Z., & Hussain, N. (2017). Enterprise Integration in Modern Cloud Ecosystems: Patterns, Strategies, and Tools.
  • Kommera, A. R. (2015). Future of enterprise integrations and iPaaS (Integration Platform as a Service) adoption. Neuroquantology, 13(1), 176-186.
  • Gudimetla, S. R. (2015). Beyond the barrier: Advanced strategies for firewall implementation and management. NeuroQuantology, 13(4), 558-565..
  • Sparks, E. R., Talwalkar, A., Haas, D., Franklin, M. J., Jordan, M. I., & Kraska, T. (2015, August). Automating model search for large scale machine learning. In Proceedings of the Sixth ACM Symposium on Cloud Computing (pp. 368-380).
  • Shah, V., & Shukla, S. (2017). Data distribution into distributed systems, integration, and advancing machine learning. Revista Espanola de Documentacion Cientifica, 11(1), 83-99.
  • Diamantoulakis, P. D., Kapinas, V. M., & Karagiannidis, G. K. (2015). Big data analytics for dynamic energy management in smart grids. Big Data Research, 2(3), 94-101.
  • Patel, A., Taghavi, M., Bakhtiyari, K., & Júnior, J. C. (2013). An intrusion detection and prevention system in cloud computing: A systematic review. Journal of network and computer applications, 36(1), 25-41.
  • Klein, D. J., McKown, M. W., & Tershy, B. R. (2015, May). Deep learning for large scale biodiversity monitoring. In Bloomberg Data for Good Exchange Conference (Vol. 10).
  • ur Rehman, M. H., Chang, V., Batool, A., & Wah, T. Y. (2016). Big data reduction framework for value creation in sustainable enterprises. International journal of information management, 36(6), 917-928.
  • Gheisari, M., Wang, G., & Bhuiyan, M. Z. A. (2017, July). A survey on deep learning in big data. In 2017 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC) (Vol. 2, pp. 173-180). IEEE.
  • Wen, Z., Yang, R., Garraghan, P., Lin, T., Xu, J., & Rovatsos, M. (2017). Fog orchestration for IoT services: issues, challenges and directions. IEEE Internet Computing, 21(2), 16-24.
  • McKee, D. W., Clement, S. J., Almutairi, J., & Xu, J. (2017, March). Massive-scale automation in cyber-physical systems: Vision & challenges. In 2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS) (pp. 5-11). IEEE.
  • Garraghan, P., Ouyang, X., Yang, R., McKee, D., & Xu, J. (2016). Straggler root-cause and impact analysis for massive-scale virtualized cloud datacenters. IEEE Transactions on Services Computing, 12(1), 91-104.
  • Elsebakhi, E., Lee, F., Schendel, E., Haque, A., Kathireason, N., Pathare, T., ... & Al-Ali, R. (2015). Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms. Journal of Computational Science, 11, 69-81.
  • Mohammed, I. A. (2012). An Exploratory Study on IoT-based Smart Technologies and Ongoing Research Trends. International Journal of Current Science (IJCSPUB) www. ijcspub. org, ISSN, 2250-1770.
  • Dold, J., & Groopman, J. (2017). The future of geospatial intelligence. Geo-spatial information science, 20(2), 151-162.
  • Su, C. H., Tzeng, G. H., & Hu, S. K. (2016). Cloud e-learning service strategies for improving e-learning innovation performance in a fuzzy environment by using a new hybrid fuzzy multiple attribute decision-making model. Interactive Learning Environments, 24(8), 1812-1835.
  • Yang, C., & Raskin, R. (2009). Introduction to distributed geographic information processing research. International Journal of Geographical Information Science, 23(5), 553-560.
  • Azhar, I. (2015). The interaction between artificial intelligence and identity & access management: An empirical study. Ishaq Azhar Mohammed," THE INTERACTION BETWEEN ARTIFICIAL INTELLIGENCE AND IDENTITY & ACCESS MANAGEMENT: AN EMPIRICAL STUDY", International Journal of Creative Research Thoughts (IJCRT), ISSN, 2320-2882.
  • Ali, F., & Hussain, N. (2017). Collaborative Commons in Tech: Enhancing Cross-Functional Development with Cloud Solutions.
  • Elsner, J., Sivicki, T., Meisen, P., Meisen, T., & Jeschke, S. (2016). Implementing a Volunteer Notification System Into a Scalable, Analytical Realtime Data Processing Environment. Automation, Communication and Cybernetics in Science and Engineering 2015/2016, 841-853.
  • Lee, C. K. H. (2017). A GA-based optimisation model for big data analytics supporting anticipatory shipping in Retail 4.0. International Journal of Production Research, 55(2), 593-605.
  • Shah, H. (2017). Deep Learning in Cloud Environments: Innovations in AI and Cybersecurity Challenges. Revista Espanola de Documentacion Cientifica, 11(1), 146-160.
  • Höchtl, J., Parycek, P., & Schöllhammer, R. (2016). Big data in the policy cycle: Policy decision making in the digital era. Journal of Organizational Computing and Electronic Commerce, 26(1-2), 147-169.
  • Guo, Z. X., Wong, W. K., & Guo, C. (2014). A cloud-based intelligent decision-making system for order tracking and allocation in apparel manufacturing. International journal of production research, 52(4), 1100-1115.
  • Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of emerging technologies in accounting, 13(2), 1-20.
  • Tang, W., Feng, W., Jia, M., Shi, J., Zuo, H., Stringer, C. E., & Trettin, C. C. (2017). A cyber-enabled spatial decision support system to inventory Mangroves in Mozambique: coupling scientific workflows and cloud computing. International Journal of Geographical Information Science, 31(5), 907-938.
  • Mishra, N., Singh, A., Kumari, S., Govindan, K., & Ali, S. I. (2016). Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing. International Journal of Production Research, 54(23), 7115-7128.
  • Evans, K. J., Terhorst, A., & Kang, B. H. (2017). From data to decisions: helping crop producers build their actionable knowledge. Critical reviews in plant sciences, 36(2), 71-88.
  • Gui, Z., Yang, C., Xia, J., Liu, K., Xu, C., Li, J., & Lostritto, P. (2013). A performance, semantic and service quality-enhanced distributed search engine for improving geospatial resource discovery. International Journal of Geographical Information Science, 27(6), 1109-1132.
  • Gruen, A. (2013). SMART Cities: The need for spatial intelligence. Geo-Spatial Information Science, 16(1), 3-6.
  • Efe, A. Evolution of Management Information Systems by Super Artificial Intelligence Revolutions. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 8(2), 127-142.
  • Vermesan, O., & Bacquet, J. (Eds.). (2017). Cognitive Hyperconnected Digital Transformation: Internet of Things Intelligence Evolution. River Publishers.
  • Ghobaei-Arani, M., Shamsi, M., & Rahmanian, A. A. (2017). An efficient approach for improving virtual machine placement in cloud computing environment. Journal of Experimental & Theoretical Artificial Intelligence, 29(6), 1149-1171.
    • Article Viewed: 3 Total Download

    Downloads

    Download data is not yet available.

    ##plugins.themes.ojsPlusA.frontend.article.downloadstatastics##

    Make a Submission

    Make a Submission

    Current Issue

    Information

    Keywords