Hybrid Deep Q-Network-Based Multilevel Access Controlled Task Scheduling and Load Balancing in Cloud Environment

A Hybrid Deep Reinforcement Learning Framework for Efficient Cloud Resource Scheduling

A hybrid Deep Reinforcement Learning (DRL) framework that combines Deep Q-Network, Proximal Policy Optimization and Advantage Actor-Critic to enable adaptive resource scheduling in cloud environments is proposed.

Deep reinforcement learning for cost-efficient resource management in hybrid cloud environments

Sensitivity and ablation studies confirm stable learning and controllable latency-cost trade-offs, demonstrating that lightweight RL can effectively deliver cost-efficient, adaptive autoscaling in hybrid cloud environments.

Task scheduling and load balancing in cloud, fog, and edge computing systems: a survey

Active-active technology for business applications on a power grid dispatch cloud platform with one cloud and multiple chips

Cloud platforms still suffer from problems such as insufficient resource utilization, low data convergence efficiency, and excessively long fault recovery links in areas like cross-chip adaptation, multi-primary/backup consistency guarantees, and fault self-healing. To address these issues, this paper proposes a multi-primary/backup technology system for power grid dispatching cloud platforms that integrates intelligent sensing and adaptive control. Capability tag modeling and unified resource abstraction are used to achieve unified cross-architecture access and containerized adaptive deployment for CPU/GPU/domestic chips. A hierarchical data consistency control and multi-primary conflict resolution mechanism is constructed by combining primary key routing and version vector constraints to improve the controllability of multi-primary/backup data synchronization. Experimental results show that, compared with comparative schemes, the proposed method increases the average CPU utilization to 68.3%, reduces the load variance to 0.027, increases the request success rate to 99.41%, and reduces the average response latency to 28.3 milliseconds. The results verify the comprehensive advantages of the proposed method in heterogeneous resource utilization, multi-activity consistency guarantees, and high-reliability self-healing, providing a feasible technical path for the construction of high-availability multi-activity power grid dispatching cloud platforms.

A Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing in Cloud Systems

Experiments show that the proposed Hybrid Framework for Joint Optimization of Resource Allocation and Load Balancing that spans two layers in heterogeneous cloud computing systems obtains 25-30% energy savings compared with ordinary methods, significantly reduces p95 latency and also achieves a relatively better Quality Of Service.

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

Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addressed through load balancing and task scheduling techniques. Efficient scheduling plays a vital role in maximizing resource utilization, minimizing response time, and maintaining acceptable Quality of Service (QoS), particularly under dynamic and large-scale workloads. Despite the progress achieved by traditional heuristics such as Min-Min and metaheuristic approaches like the Improved Sparrow Search Algorithm (ISSA), challenges related to scalability, adaptability, and computational overhead remain. Metaheuristic-based approaches often involve iterative optimization processes that may limit their efficiency in real-time scheduling scenarios. In this paper, we propose a lightweight Stochastic Predictive Energy-Aware Scheduling (SPES) algorithm that integrates predictive execution estimation, multi-resource awareness, and stochastic decision-making. Unlike deterministic scheduling strategies, SPES employs a Top K candidate selection mechanism combined with probabilistic weighting and epsilon-greedy exploration to enhance adaptability and avoid suboptimal resource allocation. The proposed method considers CPU, memory, and I/O demands to achieve balanced utilization across heterogeneous hosts while implicitly addressing energy efficiency through utilization-based modeling. The proposed algorithm is implemented and evaluated using the CloudSim 5.0 simulation framework under heterogeneous multi-region cloud environments with varying workload sizes. Experimental results demonstrate that SPES consistently outperforms ISSA and achieves makespan reductions of up to 23.8% while improving scalability, resource utilization, and scheduling efficiency under dynamic cloud workloads. These results indicate that SPES provides an effective lightweight scheduling solution for large-scale and energy-aware cloud computing environments and supports green computing objectives through improved resource efficiency.