Significance of Artificial Intelligence and Machine Learning Techniques in Smart Cloud Computing: A Review
V. Radhamani1, G. Dalin2
1V. Radhamani*, Assistant Professor, Department of Computing at Coimbatore Institute of Technology, Coimbatore.
2G. Dalin, Associate Professor, PG & Research Department of Computer Science, Hindustan College of Arts and Science, Coimbatore.a.
Manuscript received on September 02, 2019. | Revised Manuscript received on September 05, 2019. | Manuscript published on September 30, 2019. | PP: 1-7 | Volume-9 Issue-3, September 2019. | Retrieval Number: C3265099319/19©BEIESP | DOI: 10.35940/ijsce.C3265.099319
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Realization of the tremendous features and facilities provided by Cloud Computing by the geniuses in the world of digital marketing increases its demand. As customer satisfaction is the manifest of this ever shining field, balancing its load becomes a major issue. Various heuristic and meta-heuristic algorithms were applied to get optimum solutions. The current era is much attracted with the provisioning of self-manageable, self-learnable, self-healable, and self-configurable smart systems. To get self-manageable Smart Cloud, various Artificial Intelligence and Machine Learning (AI-ML) techniques and algorithms are revived. In this review, recent trend in the utilization of AI-ML techniques, their applied areas, purpose, their merits and demerits are highlighted. These techniques are further categorized as instance-based machine learning algorithms and reinforcement learning techniques based on their ability of learning. Reinforcement learning is preferred when there is no training data set. It leads the system to learn by its own experience itself even in dynamic environment.
Keywords: Cloud Computing, Load Balancing, Optimal Solution, Artificial Intelligence and Machine Learning Techniques, Instance-based Learning, Reinforcement Learning.