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Kênh 555win: · 2025-08-20 14:00:51

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Energy consumption in data center is currently the main focus of many large-scale enterprises and cloud service providers. Dynamic virtual machine (VM) consolidation technologies are …

In the simulation model of complex production processes, virtual machine resource modelling is a core component for achieving efficient task scheduling and resource allocation.

To address these limitations, this study proposes an intelligent resource allocation framework that leverages machine learning to enhance cloud efficiency. By integrating deep learning (LSTM) …

Placement strategies are found by using a deep reinforcement learning framework to select the best placement heuristic for each virtual machine composing the workload. Results indicate …

May 26, 2025 · lem remains understudied. We first show that, unlike most Keywords: Cloud Computing, Virtual Machine Reschedul- combinatorial optimization tasks, the inference time of …

They adopt resource virtualization to maximize hardware utilization, allocating Virtual Machines (VMs) [14, 60] with the requested resources to end-users. An industry-scale data center is …

Optimizing cloud resource allocation using advanced AI techniques: A comparative study of reinforcement learning and genetic algorithms in multi-cloud environments

To address this, we propose a reinforcement learning management policy, which is able to run decentral-ized, and achieve fast convergence towards efficient resource allocation, resulting in …

The suggested approach promotes the use of cutting-edge architecture to combine virtual machines, and, therefore, strike a balance between performance and energy requirements. …

Dynamic Resource Allocation (DRA) is used in cloud computing environments. It is considered as an important optimization technique to achieve maximum resource efficiency and scalability, as …

This paper delves into the algorithmic development for scheduling VMs across N physical machines (PMs), each configured with double Non-Uniform Memory Access (NUMA) nodes, a …

Abstract Heuristic approaches require fixed knowl-edge of how resource allocation should be carried out, and this can be limiting when managing variable cloud workloads. Solutions based …

May 23, 2025 · Modern industry-scale data centers need to manage a large number of virtual machines (VMs). Due to the continual creation and release of VMs, many small resource …

Deep Reinforcement Learning (DRL) offers a solution but often lacks alignment with lean principles and is hard to interpret, limiting effective human-machine collaboration. This study …

Jul 26, 2025 · This research suggests an actor-critic approach for cloud computing resource allocation that is based on deep reinforcement learning. The suggested approach generates …

Jan 17, 2024 · Thus, Machine learning (ML) can be used to schedule resources efficiently or optimal request allocation. Deep reinforcement learning (DRL), a combination of deep learning …

The proposed Reinforcement Learning Strategies for Dynamic Resources model is designed to optimize resource allocation, management, and utilization in a dynamic and evolving …

Jun 1, 2022 · This paper presents a new resource allocation model based on an intelligent multi-agent system and reinforcement learning method (IMARM). It combines the multi-agent …

Consolidating VMs has been highlighted as a promising method for improving resource efficiency and saving energy in data centers. This research provides deep learning augmented …

Jul 1, 2022 · In this paper we explore the application of Reinforcement Learning (RL) algorithms for the VM consolidation problem demonstrating their capacity to optimize the distribution of …

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