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Artificial Intelligence in the 21st Century: Advances, Challenges, and Opportunities of Deep and Reinforcement Learning in Cloud Computing

 

 

Mrs.R.Surya Prabha, Mca,M.Phil.,(ph.D)

Department Of Computer Science

Sri Krishana Arts and science college

Coimbatore, India

suryaprabhar@skasc.ac.in

 

 

Varshini V K

Department of computer science

Sri Krishana arts and science college

Coimbatore,India

varshinivishvanathan@gmail.com

 

 

ABSTRACT:

Artificial Intelligence (AI) has swiftly emerged as a groundbreaking innovation, reshaping industries and redefining the way humans interact with technology in the 21st century,with deep learning and reinforcement learning (RL) serving as two of its most influential paradigms. These methods are not only advancing sectors such as autonomous systems, language analytics and healthcare systems, but are also reshaping the foundations of cloud computing. In large-scale cloud environments, where efficiency, scalability, and sustainability are critical, deep learning and reinforcement learning offer powerful alternatives to traditional optimization techniques that often fail to adapt in real time to dynamic workloads and fluctuating resource demands.

Deep reinforcement learning, in particular, demonstrates unique advantages by enabling systems to learn optimal strategies for task offloading, federated resource scheduling, load balancing, and intelligent path planning without relying on static heuristics. This adaptability positions RL as a key enabler of intelligent, self-optimizing cloud infrastructures. Recent surveys and research contributions highlight a wide range of successful applications—from energy-efficient data center management to autonomous workload distribution—while simultaneously identifying limitations such as instability during training, high computational costs, and the lack of transparency in decision-making processes.

These unresolved challenges create opportunities for innovation. Emerging directions include the integration of fusion-based AI models that integrate, the strengths of instruction-based, exploratory, and goal-oriented learning,as well as the development of transparent AI (XAI) models to ensure understandability, accountability,  trust in automated decision-making. By synthesizing current advances and open research gaps, this paper provides  broad .This paper offers an in-depth study of how deep learning impacts modern AI. and reinforcement learning , revolutionizing cloud computing. Furthermore, it outlines future pathways for building sustainable, intelligent, and resilient cloud ecosystems capable of meeting the growing computational and environmental demands of the digital era.

Keywords: Artificial Intelligence, Cloud Computing, Deep Reinforcement Learning, Resource Allocation, Energy Efficiency

 

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