RECENT ADVANCES IN CLOUD COMPUTING : A TECHNICAL REVIEW
DOI: https://doi.org/10.65725/JCISE/2/2/001
JOURNAL OF COMPUTATIONAL INTELLIGENCE SCIENCE AND ENGINEERING (JCISE) 
ISSN: 3107-8168
Volume 2 Issue 2, Apr-Jun 2026

Abstract: Recent advancements in cloud computing have led to the development of various techniques for task scheduling and resource allocation, addressing challenges like complex workflow scheduling, quality of service (QoS), and energy consumption. Nonetheless, the effectiveness of these techniques can vary based on the application and cloud environment, requiring careful consideration. This paper provides a thorough technical review on the most recent developments in cloud computing, focusing on optimization algorithms, machine learning, and deep reinforcement learning techniques. Numerous techniques have been created to address task scheduling and resource allocation challenges, and a great number of excellent research articles have been published that thoroughly address the scheduling problem. These techniques, despite their shown efficacy, were created with specific goals and may have scalability considerations. This paper presents a brief survey on techniques based on optimization algorithms, machine learning, and deep reinforcement learning methods. Comparing these algorithms, the Deep Reinforcement Learning-based scheduling technique outperforms other methods, having better performance in terms of efficiency and adaptability.

Authors: Banupriya K

Keywords: Cloud, Scheduling, DRL, Qos, Resource allocation.