Dynamic Resource Provisioning in Cloud Environments Using Predictive Analytics
Cloud computing services have quickly proven to be one of the primary technologies that can meet the dynamic needs of an organization, regarding IT resources distribution. But the problem of efficient resource provisioning is still considered quite pressing, whenever traditional approaches are applied, it results in resources over-provisioning or under-provisioning and, thus, is followed by increased costs, poor performance, and inefficient use of energy. In order to overcome these challenges there has been proposed dynamic resource provisioning concept based on the use of predictive analytics. This approach makes use of machine learning and data science aspects for continuously predicting the resource demand in order that cloud environments could accurately allocate the necessary resources in real time depending on workloads.
The usage of predictive analytics in context with dynamic resource provisioning for cloud computing is the focus of this paper. This part talks about the basics of cloud computing and resources, the importance of machine learning models in the context of demand forecasting, and a plethora of dynamic provisioning techniques consisting of elastic scaling, load forecasting and cost consideration provisioning, and many more. This paper also explores day-to-day examples of how predictive analytics has been deployed to streamline the feature-entailing provisioning operation in cloud-based applications, from e-business sites to green data centers.
In addition, the paper outlines the following imperatives: data quality, scalability of the reaches of the models in the paper, and latency issues that need to be resolved to facilitate the broader use of prediction analysis in the management of cloud resources. At last, it underscores the future scope, such as incorporating edge computing, using AI algorithms and more advanced machine learning algorithms which will pave the way to fortify the dynamics of resource provisioning. This study would therefore seek to provide further input into the onward evolution of more intelligent and effective as well as cheaper models of cloud computing.
This abstract aims to present the goals of the paper and main ideas considered in it, besides, it highlights the main context for a better understanding of the use of dynamic resource provisioning facilitated by the predictive analytics in cloud environments.
Vijayakumar, S., Zhu, Q., & Agrawal, G. (2010, November). Dynamic resource provisioning for data streaming applications in a cloud environment. In 2010 IEEE Second International Conference on Cloud Computing Technology and Science (pp. 441-448). IEEE.
Zhang, Q., Zhani, M. F., Zhang, S., Zhu, Q., Boutaba, R., & Hellerstein, J. L. (2012, September). Dynamic energy-aware capacity provisioning for cloud computing environments. In Proceedings of the 9th international conference on Autonomic computing (pp. 145-154).
Amiri, M., & Mohammad-Khanli, L. (2017). Survey on prediction models of applications for resources provisioning in cloud. Journal of Network and Computer Applications, 82, 93-113.
Al-Ayyoub, M., Jararweh, Y., Daraghmeh, M., & Althebyan, Q. (2015). Multi-agent based dynamic resource provisioning and monitoring for cloud computing systems infrastructure. Cluster Computing, 18, 919-932.
Islam, S., Keung, J., Lee, K., & Liu, A. (2012). Empirical prediction models for adaptive resource provisioning in the cloud. Future Generation Computer Systems, 28(1), 155-162.
Jiang, Y., Perng, C. S., Li, T., & Chang, R. N. (2013). Cloud analytics for capacity planning and instant VM provisioning. IEEE Transactions on Network and Service Management, 10(3), 312-325.
Zhang, Q., Zhani, M. F., Boutaba, R., & Hellerstein, J. L. (2014). Dynamic heterogeneity-aware resource provisioning in the cloud. IEEE transactions on cloud computing, 2(1), 14-28.
Kumbhare, A. G., Simmhan, Y., Frincu, M., & Prasanna, V. K. (2015). Reactive resource provisioning heuristics for dynamic dataflows on cloud infrastructure. IEEE Transactions on Cloud Computing, 3(2), 105-118.
Shyam, G. K., & Manvi, S. S. (2016). Virtual resource prediction in cloud environment: a Bayesian approach. Journal of Network and Computer Applications, 65, 144-154.
Mian, R., Martin, P., & Vazquez-Poletti, J. L. (2013). Provisioning data analytic workloads in a cloud. Future Generation Computer Systems, 29(6), 1452-1458.
Alam, K., Mostakim, M. A., & Khan, M. S. I. (2017). Design and Optimization of MicroSolar Grid for Off-Grid Rural Communities. Distributed Learning and Broad Applications in Scientific Research, 3.
Integrating solar cells into building materials (Building-Integrated Photovoltaics-BIPV) to turn buildings into self-sustaining energy sources. Journal of Artificial Intelligence Research and Applications, 2(2).
Agarwal, A. V., & Kumar, S. (2017, November). Unsupervised data responsive based monitoring of fields. In 2017 International Conference on Inventive Computing and Informatics (ICICI) (pp. 184-188). IEEE.
Agarwal, A. V., Verma, N., Saha, S., & Kumar, S. (2018). Dynamic Detection and Prevention of Denial of Service and Peer Attacks with IPAddress Processing. Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 1, 707, 139.
Mishra, M. (2017). Reliability-based Life Cycle Management of Corroding Pipelines via Optimization under Uncertainty (Doctoral dissertation).
Agarwal, A. V., & Kumar, S. (2017, October). Intelligent multi-level mechanism of secure data handling of vehicular information for post-accident protocols. In 2017 2nd International Conference on Communication and Electronics Systems (ICCES) (pp. 902-906). 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.
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).
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.
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.
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.
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.
Lin, L. I., & Hao, L. I. (2024). The efficacy of niraparib in pediatric recurrent PFA⁃ type ependymoma. Chinese Journal of Contemporary Neurology & Neurosurgery, 24(9), 739.
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.
Swarnagowri, B. N., & Gopinath, S. (2013). Pelvic Actinomycosis Mimicking Malignancy: A Case Report. tuberculosis, 14, 15.
Krishnan, S., Shah, K., Dhillon, G., & Presberg, K. (2016). 1995: FATAL PURPURA FULMINANS AND FULMINANT PSEUDOMONAL SEPSIS. Critical Care Medicine, 44(12), 574.
Krishnan, S. K., Khaira, H., & Ganipisetti, V. M. (2014, April). Cannabinoid hyperemesis syndrome-truly an oxymoron!. In JOURNAL OF GENERAL INTERNAL MEDICINE (Vol. 29, pp. S328-S328). 233 SPRING ST, NEW YORK, NY 10013 USA: SPRINGER.
Krishnan, S., & Selvarajan, D. (2014). D104 CASE REPORTS: INTERSTITIAL LUNG DISEASE AND PLEURAL DISEASE: Stones Everywhere!. American Journal of Respiratory and Critical Care Medicine, 189, 1.
Mahmud, U., Alam, K., Mostakim, M. A., & Khan, M. S. I. (2018). AI-driven micro solar power grid systems for remote communities: Enhancing renewable energy efficiency and reducing carbon emissions. Distributed Learning and Broad Applications in Scientific Research, 4.
Nagar, G. (2018). Leveraging Artificial Intelligence to Automate and Enhance Security Operations: Balancing Efficiency and Human Oversight. Valley International Journal Digital Library, 78-94.
Agarwal, A. V., Verma, N., Saha, S., & Kumar, S. (2018). Dynamic Detection and Prevention of Denial of Service and Peer Attacks with IPAddress Processing. Recent Findings in Intelligent Computing Techniques: Proceedings of the 5th ICACNI 2017, Volume 1, 707, 139.
Mishra, M. (2017). Reliability-based Life Cycle Management of Corroding Pipelines via Optimization under Uncertainty (Doctoral dissertation).
Agarwal, A. V., Verma, N., & Kumar, S. (2018). Intelligent Decision Making Real-Time Automated System for Toll Payments. In Proceedings of International Conference on Recent Advancement on Computer and Communication: ICRAC 2017 (pp. 223-232). Springer Singapore
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