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Adopting Artificial Intelligence and Deep Learning Techniques in Cloud Computing for Operational Efficiency

Authors: Sandesh Achar

Abstract:

Artificial intelligence (AI) is being increasingly incorporated into many applications across various sectors such as health, education, security, and agriculture. Recently, there has been rapid development in cloud computing technology, resulting in AI’s implementation into cloud computing to enhance and optimize the technology service rendered. The deployment of AI in cloud-based applications has brought about autonomous computing, whereby systems achieve stated results without human intervention. Despite the amount of research into autonomous computing, work incorporating AI/ML into cloud computing to enhance its performance and resource allocation remains a fundamental challenge. This paper highlights different manifestations, roles, trends, and challenges related to AI-based cloud computing models. This work reviews and highlights investigations and progress in the domain. Future directions are suggested for leveraging AI/ML in next-generation computing for emerging computing paradigms such as cloud environments. Adopting AI-based algorithms and techniques to increase operational efficiency, cost savings, automation, reducing energy consumption and solving complex cloud computing issues are the major findings outlined in this paper.

Keywords: Artificial intelligence, AI, cloud computing, deep learning, machine learning, ML, internet of things, IoT.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.7439357

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[1] Belgaum, M. R., Alansari, Z., Musa, S., Mansoor Alam, M., & Mazliham, M. S. (2021). Role of artificial intelligence in cloud computing, IoT and SDN: Reliability and scalability issues. International Journal of Electrical and Computer Engineering (IJECE), 11(5), 4458. https://doi.org/10.11591/ijece.v11i5.pp4458-4470
[2] Tredinnick, L. (2017). Artificial intelligence and professional roles. Business Information Review, 34(1), 37–41. https://doi.org/10.1177/0266382117692621
[3] Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems, 49, 194-214. https://doi.org/10.1016/j.jmsy.2018.10.005
[4] Gill, S. S., Tuli, S., Xu, M., Singh, I., Singh, K. V., Lindsay, D., Tuli, S., Smirnova, D., Singh, M., Jain, U., Pervaiz, H., Sehgal, B., Kaila, S. S., Misra, S., Aslanpour, M. S., Mehta, H., Stankovski, V., & Garraghan, P. (2019). Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges. Internet of Things, 8, 100118. https://doi.org/10.1016/j.iot.2019.100118
[5] Rimal, B. P., Choi, E., & Lumb, I. (2009). A Taxonomy and Survey of Cloud Computing Systems. In 2009 Fifth International Joint Conference on INC, IMS and IDC. IEEE. https://doi.org/10.1109/ncm.2009.218
[6] Casavant, T. L., & Kuhl, J. G. (1988). A taxonomy of scheduling in general-purpose distributed computing systems. IEEE Transactions on Software Engineering, 14(2), 141–154. https://doi.org/10.1109/32.4634
[7] Yu, J., & Buyya, R. (2005). A Taxonomy of Workflow Management Systems for Grid Computing. Journal of Grid Computing, 3(3-4), 171–200. https://doi.org/10.1007/s10723-005-9010-8
[8] Qayyum, A., Ijaz, A., Usama, M., Iqbal, W., Qadir, J., Elkhatib, Y., & Al-Fuqaha, A. (2020). Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security. Frontiers in Big Data, 3. https://doi.org/10.3389/fdata.2020.587139
[9] Petrović, A., & Žižović, M. (2019). Integration of Artificial Intelligence with Cloud Services. In Sinteza 2019. Singidunum University. https://doi.org/10.15308/sinteza-2019-381-387
[10] Pusztai, T., Morichetta, A., Pujol, V. C., Dustdar, S., Nastic, S., Ding, X., Vij, D., & Xiong, Y. (2021). A Novel Middleware for Efficiently Implementing Complex Cloud-Native SLOs. In 2021 IEEE 14th International Conference on Cloud Computing (CLOUD). IEEE. https://doi.org/10.1109/cloud53861.2021.00055
[11] Tuli, S., Gill, S. S., Xu, M., Garraghan, P., Bahsoon, R., Dustdar, S., Sakellariou, R., Rana, O., Buyya, R., Casale, G., & Jennings, N. R. (2022). HUNTER: AI based holistic resource management for sustainable cloud computing. Journal of Systems and Software, 184, 111124. https://doi.org/10.1016/j.jss.2021.111124
[12] Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services on cloud computing environment. Measurement, 119,117-128. https://doi.org/10.1016/j.measurement.2018.01.022
[13] Ulrich, L. (2020). Top 10 tech cars: The scramble for electric dominance has begun. IEEE Spectrum, 57(4), 30–39. https://doi.org/10.1109/mspec.2020.9055970
[14] Masood, A., & Hashmi, A. (2019). AIOps: Predictive Analytics & Machine Learning in Operations. In Cognitive Computing Recipes (pp. 359–382). Apress. https://doi.org/10.1007/978-1-4842-4106-6_7
[15] Dang, Y., Lin, Q., & Huang, P. (2019). AIOps: Real-World Challenges and Research Innovations. In 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). IEEE. https://doi.org/10.1109/icse .companion.2019.00023
[16] Nastic, S., Morichetta, A., Pusztai, T., Dustdar, S., Ding, X., Vij, D., Xiong, Y., & Dustdar, S. (2020). SLOC: Service Level Objectives for Next Generation Cloud Computing. IEEE Internet Computing, 24(3), 39–50. https://doi.org/10.1109/mic.2020.2987739
[17] Robertson, J., Fossaceca, J., & Bennett, K. (2021). A Cloud-Based Computing Framework for Artificial Intelligence Innovation in Support of Multidomain Operations. IEEE Transactions on Engineering Management, 1– 10. https://doi.org/10.1109/tem.2021.3088382
[18] Horn, G., Skrzypek, P., Materka, K., & Przeździȩk, T. (2019). Cost Benefits of Multi-cloud Deployment of Dynamic Computational Intelligence Applications. In Advances in Intelligent Systems and Computing (pp. 1041–1054). Springer International Publishing. https://doi.org/10.1007/978-3-030-15035-8_102
[19] Lee, J. (2020). Introduction: The Development and Application of AI Technology. In Industrial AI (pp. 1–4). Springer Singapore. https://doi.org/10.1007/978-981-15-2144-7_1
[20] Marshall, T. E., & Lambert, S. L. (2018). Cloud-Based Intelligent Accounting Applications: Accounting Task Automation Using IBM Watson Cognitive Computing. Journal of Emerging Technologies in Accounting, 15(1), 199–215. https://doi.org/10.2308/jeta-52095
[21] Jha, N., Prashar, D., & Nagpal, A. (2021). Combining Artificial Intelligence with Robotic Process Automation—An Intelligent Automation Approach. In Studies in Computational Intelligence (pp. 245–264). Springer International Publishing. https://doi.org/10.1007/978-3-030-65661-4_12
[22] Chaudhary, R., Aujla, G. S., Kumar, N., & Rodrigues, J. J. P. C. (2018). Optimized Big Data Management across Multi-Cloud Data Centers: Software-Defined-Network-Based Analysis. IEEE Communications Magazine, 56(2), 118–126. https://doi.org/10.1109/mcom.2018.1700211
[23] Rajeswari, S. V. K. R., & Ponnusamy, V. (2022). AI-Based IoT Analytics on the Cloud for Diabetic Data Management System. In Integrating AI in IoT Analytics on the Cloud for Healthcare Applications (pp. 143–161). IGI Global. https://doi.org/10.4018/978-1-7998-9132-1.ch009
[24] 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., . . . 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
[25] Tao, F., Cheng, Y., Zhang, L., & Nee, A. Y. C. (2015). Advanced manufacturing systems: socialization characteristics and trends. Journal of Intelligent Manufacturing, 28(5), 1079–1094. https://doi.org/10.1007/s10845-015-1042-8
[26] Wan, J., Yi, M., Li, D., Zhang, C., Wang, S., & Zhou, K. (2016). Mobile Services for Customization Manufacturing Systems: An Example of Industry 4.0. IEEE Access, 4, 8977–8986. https://doi.org/10.1109/access.2016.2631152
[27] Wan, J., Zhang, D., Sun, Y., Lin, K., Zou, C., & Cai, H. (2014). VCMIA: A Novel Architecture for Integrating Vehicular Cyber-Physical Systems and Mobile Cloud Computing. Mobile Networks and Applications, 19(2), 153–160. https://doi.org/10.1007/s11036-014-0499-6
[28] He, X., Wang, K., Huang, H., & Liu, B. (2018). QoE-Driven Big Data Architecture for Smart City. IEEE Communications Magazine, 56(2), 88– 93. https://doi.org/10.1109/mcom.2018.1700231
[29] Wan, J., Tang, S., Li, D., Imran, M., Zhang, C., Liu, C., & Pang, Z. (2019). Reconfigurable Smart Factory for Drug Packing in Healthcare Industry 4.0. IEEE Transactions on Industrial Informatics, 15(1), 507–516. https://doi.org/10.1109/tii.2018.2843811
[30] Chen, M., Zhou, P., & Fortino, G. (2017). Emotion Communication System. IEEE Access, 5, 326–337. https://doi.org/10.1109/access.2016.2641480
[31] Wan, J., Yang, J., Wang, Z., & Hua, Q. (2018). Artificial Intelligence for Cloud-Assisted Smart Factory. IEEE Access, 6, 55419 55430. https://doi.org/10.1109/access.2018.2871724
[32] Shit, R. C., Sharma, S., Puthal, D., & Zomaya, A. Y. (2018). Location of Things (LoT): A Review and Taxonomy of Sensors Localization in IoT Infrastructure. IEEE Communications Surveys & Tutorials, 20(3), 2028-2061. https://doi.org/10.1109/comst.2018.2798591
[33] Ning, Z., Dong, P., Wang, X., Hu, X., Guo, L., Hu, B., Guo, Y., Qiu, T., & Kwok, R. Y. K. (2020). Mobile Edge Computing Enabled 5G Health Monitoring for Internet of Medical Things: A Decentralized Game Theoretic Approach. IEEE Journal on Selected Areas in Communications, 1. https://doi.org/10.1109/jsac.2020.3020645
[34] Sun, L., Jiang, X., Ren, H., & Guo, Y. (2020). Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application. IEEE Access, 8, 101079–101092. https://doi.org/10.1109/access.2020.2997831
[35] Hummer, W., Muthusamy, V., Rausch, T., Dube, P., El Maghraoui, K., Murthi, A., & Oum, P. (2019). ModelOps: Cloud-Based Lifecycle Management for Reliable and Trusted AI. In 2019 IEEE International Conference on Cloud Engineering (IC2E). IEEE. https://doi.org/10.1109/ic2e.2019.00025