Artificial intelligence-based smart cloud computing schema model

• Autorzy: Kanthavel Radhakrishnan , Dhaya Ramakrishnan , Khalaf Osamah Ibrahim , Hamad Abdulsattar Abdullah

Identyfikatory

DOI

10.2478/candc-2024-0025

• Języki publikacji: EN

Abstrakty

EN

In the contemporary digital era, cloud computing offers an ideal platform for artificial intelligence (AI) applications by providing the necessary computational power, memory, and scalability to handle the massive volumes of data required by intelligent algorithms. AI systems enable computing devices to make expert-level decisions by effectively leveraging information. However, challenges, related to adaptability, efficiency, privacy preservation, and the latent requirement for minimal user intervention remain critical. Notably, error detection efficiency can be improved by distributing data across multiple cloud storage services, akin to spreading data across physical disk drives. Nevertheless, continuously optimizing the performance and cost-efficiency of multiple cloud providers remains a complex task, due to varying pricing models and service quality levels. This paper aims to clarify how rule enforcement for distributed systems can be improved through the use of diverse cloud hosting services guided by authorization patterns. We propose an Effective AI Architecture for File Distribution Enhancement (EAIFDE), which aims to minimize costs and waiting times across various cloud platforms. The proposed architecture is validated using a cloud storage system simulator to evaluate the operational complexity and performance differences among multiple providers.

Słowa kluczowe

EN

artificial intelligence; cloud storage; predicted cost; authorization; efficacy; waiting time; efficiency analytics; predictive analytics

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2024
• Tom: Vol. 53, No. 4
• Strony: 639--672
• Opis fizyczny: Bibliogr. 47 poz., rys., tab.

Twórcy

autor

Kanthavel Radhakrishnan

• School of Electrical and Communications Engineering, PNG University of Technology, Papua New Guinea

autor

Dhaya Ramakrishnan

• School of Electrical and Communications Engineering, PNG University of Technology, Papua New Guinea

autor

Khalaf Osamah Ibrahim

• Department of Solar, Al-Nahrain Research Center for Renewable Energy, Al-Nahrain University, Jadriya, Baghdad, Iraq

autor

Hamad Abdulsattar Abdullah

• University of Samarra, College of Education, Department of Physics, Iraq

Bibliografia

• ALGARNI, A. and KUDENKO, D. (2017) Distribution of Data Across Multiple Cloud Storage using Reinforcement Learning Method. In: Proceedings of the 9th International Conference on Agents and Artificial Intelligence, ICAART 2017, 431–438.
• ARMBRUST,M., FOX, A., GRIFFITH, R., JOSEPH, A. D., KATZ, R., KONWINSKI, A., LEE, G., PATTERSON, D., RABKIN, A., STOICA, I. and ZAHARIA, M. (2010) A view of cloud computing. Commun. ACM, 53, 4, 50–58. https:// doi.org /10.1145/1721654.1721672.
• BALA, A., RASHID, R. Z. J. A., ISMAIL, I. et al. (2024) Artificial intelligence and edge computing for machine maintenance-review. Artif. Intell. Rev. 57(1), 119. https://doi.org/10.1007/s10462-024-10748-9
• BELGAUM, M. R., MUSA, S., ALAM, M. and MAZLIHAM, M. S. (2019) Integration Challenges of Artificial Intelligence in Cloud Computing, Internet of Things, and Software-Defined Networking. In: Proceedings of the 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 1-5. https://doi.org/10.1109/MACS48846. 2019.9024828
• BESSANI, A., CORREIA, M., QUARESMA, B., ANDR, F. and SOUSA, P. (2011) Depsky: Dependable and secure storage in a cloud-of-clouds. In: Proceedings of the Sixth Conference on Computer Systems, EuroSys ’11, New York, NY, USA. ACM, 31–46.
• BHARDWAJ, H., TOMAR, P., SAKALLE, A. and BHARDWAJ, A. (2021) Classification of extraversion and introversion personality trait using electroencephalogram signals. In: International Conference on Artificial Intelligence and Sustainable Computing. Springer, Cham, 31-39.
• BOWERS, K. D., JUELS, A. and OPREA, A. (2009) Hail: A high-availability and integrity layer for cloud storage. In: Proceedings of the 16th ACM Conference on Computer and Communications Security. New York, NY, USA. ACM, 187-198.
• BUYYA, R., YEO, C. S., VENUGOPAL, S., BROBERG, J. and BRANDIC, I. (2009) Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25, 6, 599–616 . doi: 10.1016/j.future.2008.12.001.
• CAGLAR, O., TASKIN, F., BAGLUM, C., ASIK, S. and YAYAN, U. (2023) Development of Cloud and Artificial Intelligence based Software Testing Platform (ChArIoT). In: Proceedings of the 2023 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 1-6. https://doi.org /10.1109/ASYU58738.2023.10296551
• DHAYA, R. and KANTHAVEL, R. (2022a) Applying Deep Convolution Neural Network (DCNN) Algorithm in the Cloud Autonomous Vehicles Traffic Model. The International Arab Journal of Information Technology, 19, 2, 186-194.
• DHAYA, R. and KANTHAVEL, R. (2022b) Dynamic automated infrastructure for efficient cloud data centre. Computers Materials & Continua 71(1), 1625-1639. https://doi.org/10.32604/cmc.2022.022213
• DHAYA, R., DEVI, M., KANTHAVEL, R., ALGARNI, F. and DIXIKHA, P. (2019) Exploration of Maximizing the Significance of Big Data in Cloud Computing. In: International Conference on Emerging Current Trends in Computing and Expert Technology. Springer, Cham, 695-702.
• DHAYA, R., KANTHAVEL, R. and MAHALAKSHMI, M. (2022) Enriched recognition and monitoring algorithm for private cloud data centre. Soft Computing, 26. 12871–12881 https://doi.org/ 10.1007/ s00500-021-05967-z
• DHIMAN, G. and ALGHAMDI, N. S. (2024) SMoSE: Artificial Intelligence-Based Smart City Framework Using Multi-Objective and IoT Approach for Consumer Electronics Application. IEEE Transactions on Consumer Electronics, 70, 1, 3848-3855. doi: 10.1109/TCE.2024.3363720
• GHAVAMIPOOR, H., MOUSAVI, S. A. K., FARAGARDI, H. R. and RASOULI, N. (2020) A Reliability Aware Algorithm for Workflow Scheduling on Cloud Spot Instances Using Artificial Neural Network. In: Proceedings of the 2020 10th International Symposium on Telecommunications (IST). IEEE, 67-71. https:// doi.org/ 10.1109/IST50524.2020.9345896
• GILL, S. S., XU, M., OTTAVIANI, C., PATROS, P., BAHSOON, R., SHAGHAGHI, A., GOLEC, M., STANKOVSKI, V., WU, H., ABRAHAM, A., SINGH, M., MEHTA, H., GHOSH, S. K., BAKER, T., PARLIKAD, A. K., LUTFIYYA, H., KANHERE, S. S., SAKELLARIOU, R., DUSTDAR, S., RANA, O., BRANDIC, I. and UHLIG, S. (2022) AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, 100514. https://doi.org/10.1016/j.iot.2022.100514
• GRIESCH, L., RITTELMEYER, J. and SANDKUHL, K. (2024) Towards AI as a Service for Small and Medium-Sized Enterprises (SME). In: J. P. A. Almeida, M. Kaczmarek-Heß, A. Koschmider and H. A. Proper, eds., The Practice of Enterprise Modeling. PoEM 2023. Lecture Notes in Business Information Processing 497. Springer, Cham. https://doi.org/10.1007/978-3-031-48583-1 3
• HAGEMANN, T. and KATSAROU, K. (2020) A Systematic Review on Anomaly Detection for Cloud Computing Environments. In: 2020 3rd Artificial Intelligence and Cloud Computing Conference (AICCC 2020). Kyoto. Japan. ACM, New York, NY, USA, 14, 18–20.
• KANUNGO, S. (2024) AI-driven resource management strategies for cloud computing systems, services, and applications. World Journal of Advanced Engineering Technology and Sciences, 11 (02), 559–566.
• KAPOOR, S. and PANDA, S. N. (2019) Energy-Aware Parallel Computing for Cloud Architecture using ANN. In: 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 1-4.
• KAUR, M. and DILBAG, S. (2021) Multi objective evolutionary optimization techniques based hyperchaotic map and their applications in image encryption. Multidimensional Systems and Signal Processing, 32, 1, 281–301.
• KAUR, M., SINGH, D., KUMAR, V., GUPTA, B. B. and ABD EL-LATIF, A. A. (2021) Secure and Energy Efficient-Based E-Health Care Framework for Green Internet of Things. IEEE Transactions on Green Communications and Networking, 5, 3, 1223–1231.
• KLIMT, B. and YANG, Y. (2004) The Enron Corpus: A New Dataset for Email Classification Research. Carnegie Mellon University https://www.cs.cmu.edu/_enron/
• KUSHWAHA, S. S., JOSHI, S., SINGH, D., KAUR, M. and LEE, H.-N. (2022) Systematic Review of Security Vulnerabilities in Ethereum Blockchain Smart Contract. IEEE Access, 10, 6605-6621.
• LIN, C., SUN, H., HWANG, J., VUKOVIC, M. and ROFRANO, J. (2019) Cloud Readiness Planning Tool (CRPT): An AI-Based Framework to Automate Migration Planning. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), 58–62.
• MOHAMED, N., SRIDHARA RAO, L., SHARMA, M., SURESH BABU, R., ALFURHOOD, B. S. and SHUKLA, S. K. (2023) In-depth Review of Integration of AI in Cloud Computing. In: Proceedings of the 2023 3rd International Conference on Advanced Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 1431–1434. https://doi.org/10.1109/ICACITE57410. 2023.10182738
• PAPAIOANNOU, T., BONVIN, N. and ABERER, K. (2012) Scalia: An adaptive scheme for efficient multi-cloud storage. In: High Performance Computing. Networking, Storage and Analysis (SC). 2012 International Conference. ACM, 1-10.
• PARASHAR, V., KASHYAP, R., RIZWAN, A., KARRAS, D. A., ALTAMIRANO, G. C., DIXIT, E. and AHMADI, F. (2022) Aggregation-Based Dynamic Channel Bonding to Maximize the Performance of Wireless Local Area Networks (WLAN). Wireless Communications and Mobile Computing, 2022, Article ID 4464447, 1-11. https://doi.org/10.1155/2022/4464447
• PRASAD, N., PANDIAN, P. K. G., NUGURI, S., SAOJI, R. and DEVAGUPTAPU, B. (2024) Integration of Cloud Computing, Artificial Intelligence, and Machine Learning for Enhanced Data Analytics. International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 11–20.
• RAO, R. and RAO, S. (2012) Application of Artificial Neural Networks in Capacity Planning of Cloud Based IT Infrastructure. In: IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 1-4.
• ROBERTSON, J., FOSSACECA, J. M. and BENNETT, K. W. (2021) A Cloud-Based Computing Framework for Artificial Intelligence Innovation in Support of Multidomain Operations. IEEE Transactions on Engineering Management, 69(6), 3913–3922. https://doi.org/10.1109/TEM.20213088382
• SATHYARAJ, R., KANTHAVEL, R., CAVALIERE, L. P. L., VYAS, S. and MAHESWARI, S. (2022) Analysis on Prediction of COVID-19 with Machine Learning Algorithms. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 30 (Supplement 1), 67–82.
• SINGH, P. and SHARMA, A. (2019) Heuristic Approaches for Efficient Cloud Resource Management and Load Balancing. Future Generation Computer Systems, 92, 66-80.
• SUTTON, R. S. and BARTO, A. G. (2018) Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
• TANIMOTO, S., SAKURADA, Y., SEKI, Y., IWASHITA, M., MATSUI, S., SATO, H. and KANAI, A. (2013) A study of data management in hybrid cloud configuration. In: Proceedings of the 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel / Distributed Computing. IEEE, 381-386.
• VAN HASSELT, H. and WIERING, M. A. (2007) Reinforcement learning in continuous action spaces. In: 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, IEEE, 272–279.
• VAN HASSELT, H., GUEZ, A. and SILVER, D. (2016) Deep reinforcement learning with double Q- Learning. AAAI, 2094–2100.
• VENGEROV, D. (2008) A reinforcement learning framework for online data migration in hierarchical storage systems. The Journal of Supercomputing, 43, 1, 1-19.
• VOAS, J. and ZHANG, J. (2009) Cloud computing: New wine or just a new bottle? IT Professional, 11, 15–17.
• WAN, J., YANG, J., WANG, Z. and HUA, Q. (2018) Artificial Intelligence for Cloud-Assisted Smart Factory. IEEE Access, 6, 55419 - 55430. doi: 10.1109/ACCESS.2018.2871724.
• WHITESON, S. (2012) Evolutionary Computation for Reinforcement Learning. Springer, Heidelberg-New York-Dordrecht-London, 325–355.
• XU, C.-Z., RAO, J. and BU, X. (2012) A unified reinforcement learning approach for autonomic cloud management. Journal of Parallel and Distributed Computing, 72, 2, 95–105.
• ZHANG, Y., WANG, Y. and LIU, J. (2021) AI-Driven Resource Management for Cloud-Based Data Services. IEEE Transactions on Cloud Computing, 9(4), 1230–1242. https://doi.org/10.1109/TCC.2020.2994822
• ZHENG, Y. and WEN, X. (2021) The Application of Artificial Intelligence Technology in Cloud Computing Environment Resources. Journal of Web Engineering, 20(6), 1853–1866. https://doi.org/10.13052/jwe1540-9589.2067.
• ZHOU, A. C., HE, B., CHENG, X. and LAU, C. T. (2015) A declarative optimization engine for resource provisioning of scientific workflows in iaas clouds. In: Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing, HPDC ’15. New York, NY, USA. ACM, 223–234.
• ZHOU, Z. and LIU, X. (2018) GPU-Accelerated Security Algorithms for Cloud-based Authentication Systems. Journal of Cloud Computing: Advances, Systems, and Applications, 7(1), 25-42.
• ZULUAGA, M., KRAUSE, A. and PUSCHEL, M. (2016) E-pal: An active learning approach to the multi-objective optimization problem. Journal of Machine Learning Research, 17, 1–32.