Arseam

International Journal of Advances in Engineering & Scientific Research

CLOUD PERFORMANCE ENGINEERING AT SCALE: ADVANCED TECHNIQUES FOR MONITORING, DIAGNOSTICS, AND OPTIMIZATION

Hitesh Jodhavat

Hitesh Jodhavat Cloud and Software Performance Architect India.

DOI : https://doi.org/10.53882/IJAESR.2024.1101007 Page No : 93-105

Published Online : 2024-12-25

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The objective of this project is researching and developing innovative techniques for autonomous management of the cloud by using AI - self-optimization and self-healing. Autonomous cloud management aims at reducing human interaction, enhancing the efficiency of cloud services, and strengthening dependability. This research becomes important with the increasing intricacy of cloud infrastructures and the necessity for dynamic responses in real-time to create resilience and ensure optimum operation. It adopts a multi-faceted approach to achieve self-healing and self-optimization of cloud systems. For self-healing, we employ automated recovery procedures, predictive maintenance models, and AI-driven anomaly detection algorithms. These are designed to identify and correct errors without human intervention. We apply machine learning algorithms in self-optimization processes to analyze patterns in the workload, project the resources to be used and then to dynamically distribute those resources, all in efforts to maximize productivity and save cost. Different performance metrics, such as reaction time, throughput, and resource utilization, are used to test and validate the AI methods in the experimental setup simulating a cloud environment. Using AI-based self-healing solutions significantly reduced downtime and increased the dependability of the system. The anomaly detection algorithms were very effective in identifying possible problems and were followed by automatic recovery procedures that returned everything to normal very quickly. Predictive maintenance models effectively predicted probable problems, making preventive actions possible. Machine learning models achieved a balance between workload and resource allocation for self-optimization, which led to improvement in performance indicators. The AI-based strategies showed better cost-saving and resource-utilization efficiency than conventional techniques.

Keywords: Self-Managing Cloud, Artificial Intelligence, Self-Recovery, Self-Improvement, and Cloud Computing.

REFERENCES

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Ansari, S. A. et. al (2023). Multi video summarization using query based deep optimization algorithm. International Journal of Machine Learning and Cybernetics, 1-16. DOI: http://dx.doi.org/10.1007/s13042-023-01852-3
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Sujithra, M., et. al (2022). A Study On Psychological Stress Of Working Women In Educational Institution Using Machine Learning. In 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. Retrieved From: https://ieeexplore.ieee.org/document/9984460
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Tang et al. (2014): Loss of mTOR-dependent macroautophagy causes autistic-like synaptic pruning deficits. DOI: https://doi.org/10.1016/j.neuron.2014.07.040
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Jodhavat H. (2024), “Cloud Performance Engineering At Scale: Advanced Techniques For Monitoring, Diagnostics, And Optimization”, International Journal of Advances in Engineering & Scientific Research,Volume 11, Issue 2, 2024, pp 93-105. DOI : https://doi.org/10.53882/IJAESR.2024.1101007

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REFERENCES

Ansari, S. A et. al (2018). A Review on Multisource Data Analysis using soft computing Techniques. In 2018 4th International Conference on Computing Communication and Automation (ICCCA) (pp. 1-6). IEEE. DOI: http://dx.doi.org/10.1109/CCAA.2018.8777551
Ansari, S. A. et. al (2020). A review on video analytics its challenges and applications. Advances in Bioinformatics, Multimedia, and Electronics Circuits and Signals: Proceedings of GUCON 2019, 169-182. DOI: http://dx.doi.org/10.1007/978-981-15-0339-9_14
Ansari, S. A. et. al (2022). A fusion of dolphin swarm optimization and improved sine cosine algorithm for automatic detection and classification of objects from surveillance videos. Measurement, 192, 110921. DOI: https://doi.org/10.1016/j.measurement.2022.110921
Ansari, S. A. et. al (2023). Multi video summarization using query based deep optimization algorithm. International Journal of Machine Learning and Cybernetics, 1-16. DOI: http://dx.doi.org/10.1007/s13042-023-01852-3
Ansari, S. A. et. al (2023, March). A Comprehensive Study on Video Captioning Techniques, Benchmark Datasets and QoS Metrics. In 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 1598-1603). IEEE. Retrieved From: https://vufind-test.katalog.k.utb.cz/SummonRecord/FETCH-ieee_primary_101125243?sid=77480263
Ansari, S.A. et al. (2024). MetaV: A Pioneer in feature Augmented Meta-Learning Based Vision Transformer for Medical Image Classification. Interdiscip Sci Comput Life Sci (2024). DOI: https://doi.org/10.1007/s12539-024-00630-1
Armbrust, M., et. al (2010). A view of cloud computing. Communications of the ACM, 53(4), 50-58. DOI: https://doi.org/10.1145/1721654.1721672
Bharathy, S. S. et. al (2017). Hand Gesture Recognition for Physical Impairment Peoples. SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE), 6-10. Retrieved From: https://www.internationaljournalssrg.org/IJCSE/2017/Volume4-Issue10/IJCSE-V4I10P102.pdf
Chandola, V., et. al (2009). Anomaly detection: A survey. ACM Computing Surveys (CSUR), 41(3), 1- 58. DOI: https://doi.org/10.1145/1541880.1541882
Creswell, et. al (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications. Retrieved From: https://www.scirp.org/reference/ReferencesPapers?ReferenceID=1964849
Mao, H. et. al (2016). Solution-Processed Two-Dimensional MoS2 Nanosheets: Preparation, Hybridization, and Applications. DOI: https://doi.org/10.1002/anie.201509933
Mihailescu, M., et. al (2010). Dynamic resource pricing on federated clouds. In 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (pp. 513-517). IEEE. DOI: https://doi.org/10.1109/CCGRID.2010.123
Rahman, M. A. (2012). Influence of simple shear and void clustering on void coalescence. Retrieved From: https://unbscholar.lib.unb.ca/items/659cc6b8-bee6-4c20-a801-1d854e67ec48
Rao, J., et. al (2010). A Distributed Self-learning Approach for Elastic Provisioning of Virtualized Cloud Resources. Retrieved From: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=228fccbfca9b2b13debce05f5e8876781c29a9bd
Sujithra, M., et. al (2022). A Study On Psychological Stress Of Working Women In Educational Institution Using Machine Learning. In 2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. Retrieved From: https://ieeexplore.ieee.org/document/9984460
Suresh, K., et. al (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520. DOI: http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1515-1520
Tang et al. (2014): Loss of mTOR-dependent macroautophagy causes autistic-like synaptic pruning deficits. DOI: https://doi.org/10.1016/j.neuron.2014.07.040
Zhang, Q., et. al (2010). Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7-18. Retrieved From: https://jisajournal.springeropen.com/articles/10.1007/s13174-010-0007-6

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