SPES: A Stochastic Predictive Energy-Aware Scheduling Approach for Efficient Multi-Region Cloud Computing

GPS: A Lightweight Greedy-Predictive Scheduling Approach for Energy-Aware Multi-Region Cloud Computing

Efficient resource allocation and task scheduling remain fundamental challenges in cloud computing because of resource heterogeneity, dynamic workload characteristics, and the increasing demand for scalable, energy-efficient, and sustainable cloud infrastructures. Conventional scheduling approaches, including Min-Min and the Improved Sparrow Search Algorithm (ISSA), have improved resource utilization and load balancing. However, they still face limitations in scalability, execution efficiency, and adaptive scheduling under heterogeneous and dynamically changing cloud workloads. To overcome these limitations without introducing the computational overhead associated with iterative optimization techniques, this paper proposes a lightweight Greedy Predictive Scheduling (GPS) algorithm that combines predictive resource utilization estimation with greedy host selection to improve scheduling decisions across heterogeneous multi-region cloud environments. The proposed scheduler integrates predictive execution estimation, multi-resource awareness, adaptive greedy decision making, and utilization-aware energy consideration to improve scheduling decisions across heterogeneous multi-region cloud environments. The proposed approach is implemented and evaluated using the CloudSim Plus 5.0 simulation framework, and the experimental results demonstrate that GPS achieves better performance than ISSA across our experiments. GPS achieves a makespan reduction of up to 31.25% compared with ISSA while consistently improving execution efficiency, scalability, and balanced resource utilization across heterogeneous multi-region cloud environments, demonstrating that GPS provides an effective lightweight scheduling solution for large-scale energy-aware cloud computing.

Efficient Algorithm for Multi-Objective Dynamic Resource Allocation in Cloud Computing

Cloud computing environments depend heavily on efficient Dynamic Resource Allocation (DRA) mechanisms to ensure optimal utilization of computational resources while maintaining low operational cost, reduced energy consumption, and acceptable Quality of Service (QoS) under continuously fluctuating workloads. However, many existing resource allocation techniques in cloud systems are limited by poor adaptability, high computational overhead, inefficient virtual machine migration, and inability to simultaneously optimize multiple conflicting objectives such as throughput, Service Level Agreement (SLA) compliance, and power efficiency. These limitations create the need for a more intelligent, scalable and adaptive resource management framework capable of making real-time allocation decisions in heterogeneous cloud environments. This study therefore presents the design and development of DynamiCloud, a scalable and computationally efficient multi-objective dynamic resource allocation model for cloud computing. The research aimed at developing an efficient algorithm for multi-objective Dynamic Resource Allocation (DRA) in cloud computing. An object-oriented system design methodology was adopted in modeling the proposed framework; while a Deep Reinforcement Learning (DRL)-based optimization algorithm was implemented to enable the system learn optimal VM allocation and reallocation policies from environmental states, reward signals, and workload behavior patterns. The design was implemented using python. Comparing the results of our implementation with the existing tools shows that our objectives were met.

Masterpiece Optimization Algorithm-Based Priority-Aware Load Balancing Strategy for Cloud Data Centers

: Cloud Computing (CC) is one of the widely used technologies due to its advanced features such as pay-per-use, scalability, and flexibility. The primary objective of CC is to allow users to access and purchase cloud services that are on demand through internet-based applications. Efficient load-balancing in the cloud faces challenges of high-dimensional state spaces and scalability with increasing tasks. To solve this problem, the Masterpiece Optimization Algorithm (MOA) with a priority constraint is employed for load-balancing according to the tasks efficiently. The MOA is integrated with a priority-based cost function to enhance the task scheduling process by introducing a multi-dimensional approach for load balancing. The priority-based framework helps the scheduler to dynamically recalibrate workloads. The experimental results achieve a total energy consumption of 39.8 W and an average CPU resource utilization of 99.54%, which is better than the existing algorithms, such as the hybrid Particle Swarm Grey Wolf Optimization (PSGWO) algorithm.

Energy-Aware Task Scheduling using Grasshopper Optimisation Algorithm

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.

Comparative Performance and Computational Complexity Analysis of Hybrid WGO–DRL and Heuristic, Metaheuristic and Bio-Inspired Scheduling Algorithms in Cloud Computing

Cloud computing environments are becoming increasingly dynamic and heterogeneous, making efficient task scheduling a critical challenge for cloud service providers. Traditional heuristic scheduling methods such as Round Robin (RR), Min–Min, Max–Min, and First Come First Serve are commonly used because of their simplicity and low computational overhead. However, these approaches often struggle to adapt to fluctuating workloads and heterogeneous resource conditions, which may result in inefficient resource utilization, increased energy consumption, and longer execution times. To address these limitations, this study investigates a hybrid scheduling framework that integrates Hybrid Wild Goose Optimization (HWGO) with Deep Reinforcement Learning (DRL). In the proposed framework, the DRL component observes the current system state and generates adaptive scheduling decisions, while the HWGO algorithm refines these decisions through multi-objective optimization. The hybrid model aims to enhance scheduling efficiency by reducing makespan, minimizing energy consumption, balancing CPU load across virtual machines, and decreasing migration overhead. The performance of the HWGO–DRL framework is evaluated through a comparative analysis with conventional heuristic scheduling algorithms including(RR, FCFS, Min-Min, Max-Min), metaheuristic algorithms (GA, PSO, ACO), and bio-inspired algorithms (CSO, DFA, FDA, LOA) under identical cloud simulation conditions. Experimental results demonstrate that the hybrid approach achieves improved scheduling performance across multiple evaluation metrics. Notable improvements are observed in execution efficiency, workload distribution, and energy utilization. The proposed model achieves up to 30–40% reduction in makespan and 20–30% improvement in energy efficiency compared to baseline methods.. These findings indicate that intelligent hybrid optimization techniques can provide adaptive and efficient task scheduling solutions for modern cloud computing environments.

Energy-Efficient Hybrid Metaheuristic Resource Allocation Model for Dynamic Virtual Machine Scheduling in Cloud Computing Platforms