• JOSEPH R

The rapid evolution of cloud computing and the advent of quantum cloud environments have introduced new challenges and opportunities for task scheduling and resource management. This paper presents a comprehensive study of innovative approaches to scheduling and optimization in these dynamic environments. We address five key areas: (1) task scheduling in cloud computing using a novel Q-learning-based framework, (2) application of Deep Reinforcement Learning (DRL) for cloud resource scheduling, (3) online scheduling of deferrable jobs to minimize delays, (4) task placement in quantum cloud computing using DRL techniques, and (5) cost optimization for workflow scheduling with spot and on-demand instances. Each section includes a detailed mathematical formulation that supports the proposed models and methods, highlighting their efficiency and effectiveness in addressing real-world problems. Each exploring advanced methodologies for task scheduling, resource management, and optimization in cloud computing environments. The research spans various innovative applications of reinforcement learning (RL) and deep reinforcement learning (DRL) to tackle challenges posed by dynamic workloads, resource constraints, and emerging technologies like quantum computing. Each paper introduces unique frameworks, algorithms, and experimental insights, contributing to the evolving landscape of cloud computing.

Reinforcement Learning (RL) ,Deep Reinforcement Learning (DRL) ,Task Scheduling ,Resource Management

"Advancements in Reinforcement Learning for Task and Resource Scheduling in Cloud Computing: A Comprehensive Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.h1-h10, December-2024, Available :http://www.jetir.org/papers/JETIR2412701.pdf

"Advancements in Reinforcement Learning for Task and Resource Scheduling in Cloud Computing: A Comprehensive Survey", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. pph1-h10, December-2024, Available at : http://www.jetir.org/papers/JETIR2412701.pdf