Aug 2026· International Journal of Intelligent Systems and Applications· Vol 18, pp. 73-96· 0 citations· 38 references
TL;DR
Improvements in memetic-based hybrid scheduling underscore the potential of memetic-based hybrid scheduling to support environmentally sustainable and performance-efficient cloud infrastructures.
Abstract
Minimizing energy consumption and carbon emissions while maintaining system performance is a critical challenge in cloud task scheduling. This paper presents a multi-objective scheduling framework based on a Memetic Algorithm (MA) designed to optimize task-to-VM mapping with respect to energy efficiency, carbon footprint, and throughput. The algorithm employs a weighted fitness function that integrates actual and idle energy usage, simulated time-varying carbon intensity, and task throughput. To enhance solution quality, MA combines global evolutionary operations (selection, crossover, mutation) with local search heuristics that adaptively refine candidate solutions based on workload characteristics and green energy opportunities. The carbon emission model incorporates dynamic emission factors (γ) derived from location- and time-sensitive datasets, reflecting real-world variability in grid carbon intensity. The proposed method is evaluated using the NASA Ames iPSC/860 workload under both low and high resource utilization scenarios. Comparative results demonstrate that the proposed MA approach achieves reduces the carbon emission by 20.2%, minimizes energy consumption by 17.7%, and enhances throughput by 21.2% over conventional techniques such as HDDPGTS and RAPTS, while also ensuring competitive performance in terms of makespan and resource utilization. These improvements underscore the potential of memetic-based hybrid scheduling to support environmentally sustainable and performance-efficient cloud infrastructures. The findings highlight the importance of integrating eco-aware intelligence into task scheduling policies, particularly for mission-critical and energy-intensive cloud applications.
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
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.
Kirupavathy P., Hareeni C., Jayashri K. et al.· Journal of Ubiquitous Comput...· 0 citations
Efficient energy scheduling in heterogeneous computing environments is a critical challenge, as task allocation decisions directly affect both energy consumption and execution performance. This work presents an energy aware scheduling framework based on a discretized grasshopper optimization algorithm (GOA), designed to balance energy reduction with acceptable makespan. The model formulates scheduling as a constrained objectives optimization problem, incorporating energy use, makespan, heterogeneous resource capacities, workflow precedence, and non preemptive execution. A constant aware representation and repair based decoding strategy enable GOA to generate feasible task to resources assignments. Implemented in Python, the framework is evaluated against HEFT, Min and Random scheduling under varying workload. Results show that the schedules based on GOA achieves lower energy consumption and improved performance delay energy while maintaining competitive makespan, with performance gains becoming more pronounced as workload complexity increases. These findings demonstrate the scalability and effectiveness of discretised GOA as a metaheuristic solution for energy aware scheduling in heterogeneous systems.
Macauley Opuwari, C. Igiri, D. Ikeh· International Journal Of Eng...· 0 citations
Experimental results demonstrate that DL-EATS achieves the lowest energy consumption, shortest makespan, minimal SLA violation rate, and highest resource utilization, representing an 18.5% improvement in energy efficiency over the next best method and substantial gains across all performance metrics.
Abdulmumini Adamu, A. A. Abdulwasiu· Journal of Science Research...· 0 citations
Energy-aware task scheduling in heterogeneous cloud infrastructures remains challenging due to the combinatorial growth of task-to-resource assignments, resource heterogeneity, and the need to balance energy consumption with scheduling performance. This paper proposes an Adaptive Dominance-Guided Grey Wolf Optimizer (ADG-GWO) for non-preemptive task scheduling in heterogeneous cloud environments. ADG-GWO adapts Grey Wolf Optimization to discrete task-to-VM assignment by integrating dominance-guided genetic reproduction, Hamming-distance-based diversity regulation, and adaptive reproduction control. These mechanisms are designed to improve search stability, reduce premature convergence, and support effective exploration of high-dimensional assignment spaces without expanding the externally tuned hyperparameter space.The proposed method is evaluated through simulation under workload-scaling and capacity-scaling scenarios using heterogeneous cloud configurations. For evaluation, workload instances and heterogeneous VM configurations are derived from Google Cluster Trace 2019 to instantiate realistic task-to-VM scheduling scenarios. The results show that the proposed dominance-guided adaptive search improves energy-aware scheduling effectiveness while maintaining competitive scheduling efficiency in heterogeneous cloud environments.
Saleh Al Shamaa, Wei Shi, J. Corriveau· IEEE International Conferenc...· 0 citations
A hybrid nature-inspired algorithm called fruit fly optimization–ant colony optimization (FOA-ACO), which combines the exploitative ant colony optimization (ACO) and the exploratory fruit fly optimization algorithm (FOA) is suggested, which enhances overall cloud performance.
Narayana Rao Appini, K. Premnadh, Karnam Sreenu et al.· International Journal of Onl...· 0 citations