Jul 2026· Journal of Ubiquitous Computing and Communication Technologies· 0 citations· 10 references
TL;DR
The analysis proves that the combination of carbon awareness and reinforcement learning helps to create an intelligent, adaptive, and ecologically sustainable system for managing cloud resources.
Abstract
Due to the rapid development of cloud computing services, today's data centers are consuming much more energy and emitting much higher levels of carbon dioxide into the atmosphere compared to earlier years, thus requiring the development of sustainable solutions for resource management in the cloud. In this paper, an approach called Carbon Aware Cloud Resource Scheduling based on the use of Reinforcement Learning is presented. The proposed approach includes workload demand, resource utilization, and carbon intensity values in its decision making process in order to allocate workloads efficiently and minimize environmental impact caused by cloud computing services. The reinforcement learning algorithm trains the RL agent to find the optimal strategies for workload scheduling that involve execution of workloads, postponing their execution, migrating them from one location to another and re-allocation of resources. The results from experimental evaluation indicate that the suggested framework successfully reduces the carbon emission level up to 30% and energy cost level to around 24%, respectively, in comparison with traditional scheduling methods, without lowering the SLA compliance rate down to 98.4% and workload starvation level up to 0.3%. The analysis proves that the combination of carbon awareness and reinforcement learning helps to create an intelligent, adaptive, and ecologically sustainable system for managing cloud resources.
The rapid expansion of cloud computing and large-scale data centers has significantly increased energy consumption and carbon emissions, creating critical sustainability concerns for modern computing infrastructures. This paper proposes the Adaptive Carbon-Aware Virtualized Energy-efficient Scheduling (ACAVES) framework to improve resource utilization and reduce environmental impact in cloud environments. The framework combines workload monitoring, task classification, virtual machine consolidation, carbon-aware scheduling, and energy optimization within an integrated architecture. An adaptive scheduling mechanism allocates workloads according to utilization patterns, energy requirements, and carbon emission estimates. Experimental evaluation was performed using heterogeneous workloads containing 10,000 tasks executed over 50 physical servers and 200 virtual machines. Results demonstrate that the proposed ACAVES framework reduced energy consumption from 520 kWh to 385 kWh and carbon emissions from 310 kgCO2 to 215 kgCO2. Additionally, server utilization improved from 68% to 87%, while average task completion time decreased from 820 ms to 670 ms, confirming the effectiveness and scalability of the proposed sustainable scheduling framework.
S. K, Kishore Bitra, Usha Desai· 2026 International Conferenc...· 0 citations
: 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.
M. Dhanalakshmi, S. Manoharan· International Journal of Sci...· 0 citations
Improvements in memetic-based hybrid scheduling underscore the potential of memetic-based hybrid scheduling to support environmentally sustainable and performance-efficient cloud infrastructures.
Chennoji Sandhya, Mandla Alphonsa, Vankudoth Biksham et al.· International Journal of Int...· 0 citations
The findings suggest that HORAM is far better at using resources; fewer tasks are completed, and the total power consumed is lower than with traditional scheduling algorithms, suggesting the suggested architecture is a viable solution to sustainable cloud infrastructure management.
S. Balakrishnan, K. Aravind, T. Veeramani et al.· SN Computer Science· 0 citations
Results show that the proposed framework for thermal-aware and carbon-efficient workload allocation could effectively select out the most suitable servers for workloads not only from viewpoint of carbon usage but also from the thermal perspectives, and at the same time, it also reduces the amount of cooling required and carbon emissions while keeps several performance metrics at comparable levels.
Chandan Hegde, Adarsh Bilimisi, Pruthvik J· International Journal of Lat...· 0 citations
This study presents an efficient algorithm for large-scale resource management in a multi-tenant cloud environment that integrates intelligent resource scheduling, workload balancing and adaptive virtual machine allocation to optimize resource utilization while satisfying multiple performance objectives.
Onwuegbuchulem Gift., B. O., M. D. et al.· International journal of re...· 0 citations
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