Advancements in Green Cloud Computing: A Survey of Resource Management and Carbon Emission Reduction

Abstract

: Green Cloud Computing has become a sustainable computing paradigm which seeks to minimize energy usage, carbon dioxide emissions and the cost of operating systems while keeping performance peak in Cloud Computing systems with a high level of services. Clustering of all cloud computing, big data analytics, Artificial Intelligence (AI) is putting enormous pressure on the world's economy and the environment to supply the energy needed for its escalating growth. Efficient use of the computer resources such as job scheduling, load balancing and fault tolerance have become essential to maximize cloud operations and ensure sustainable utilization of computer resources. Task scheduling allows to schedule tasks to the right resources to minimize execution time and energy, load balancing allows to distribute the tasks equally between servers to avoid hot spots and optimize the use of resources. To ensure service availability and system reliability, fault-tolerance mechanisms offer fault detection, prediction and recovery in the event of hardware, software and network failure. To solve these challenges, the Deep Learning (DL) techniques have been found to be effective solutions for the intelligent workload prediction, adaptive resource allocation, proactive fault detection and automatic decision making. Cloud systems can be designed to be environmentally friendly while providing good service quality and performance by integrating carbon emission factors with scheduling, load balancing and fault-tolerant decision making. Combining DL models with the job scheduling, load balancing and fault-tolerance features of the cloud computing systems, cloud service providers can optimize even further energy consumption, lower carbon footprint, enhance Quality of Service (QoS) and improve service dependability. Optimization algorithms, Reinforcement Learning (RL), model-based methods, Neural Networks and hybrid intelligent approaches have been the recent developments with great promise for developing sustainable and resilient cloud computing environments. This article discusses several cloud-based research efforts and offers a thorough overview and comparison of current strategies for task scheduling that is both energy efficient and aware of carbon emissions. By systematically reviewing existing resource management techniques, this study highlights critical gaps and opportunities for improvement. Researchers interested in innovative cloud computing and discovering ways to lower emissions of carbon in cloud settings will find the results useful.