基于梯度下降纳米甲虫优化的集装箱节能迁移新方法

IF 0.6 Q3 MATHEMATICS
Rukmini Satyanarayan
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引用次数: 0

摘要

由于容器的可伸缩性、可移植性和可靠部署,特别是在微服务和智能车辆中,云服务越来越多地通过容器获得。由于工作负载和云资源的多样性,云容器的调度器组件在优化能源效率和最小化成本方面发挥着至关重要的作用。对云服务日益增长的需求在能源消耗方面提出了挑战。通过利用实时迁移技术,可以优化服务器中的能耗。本研究旨在提出一种混合模型,该模型使用梯度下降Namib甲虫优化(GNBO)算法促进容器从一台服务器迁移到另一台服务器,从而降低云服务器的能耗。这项工作是通过使用物理机(PM)、虚拟机(VM)和容器进行云模拟来完成的。任务以轮询方式分配给虚拟机。采用Actor-Critic Neural Network (ACNN)对pmms的载荷进行预测,并根据预测结果确定过载和欠载情况。提出的GNBO混合优化算法考虑了预测负载、迁移成本、资源利用率、能耗和网络带宽等因素,计算出最优解。该方法的负载为0.177 MIPS,迁移成本为10.146 J,能耗优化为0.068 W。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Approach for Energy-Efficient Container Migration by Using Gradient Descent Namib Beetle Optimization
Cloud services are increasingly available through containers due to their scalability, portability, and reliable deployment, particularly in microservices and smart vehicles. The scheduler component of cloud containers plays a crucial role in optimizing energy efficiency and minimizing costs due to the diversity of workloads and cloud resources. The growing demand for cloud services poses a challenge in terms of energy consumption. Optimizing energy consumption in servers is possible by utilizing live migration technology. This study aims to propose a hybrid model that facilitates the migration of containers from one server to another using Gradient Descent Namib Beetle Optimization (GNBO) algorithms, thereby reducing the energy consumption of cloud servers. The work is carried out through cloud simulation using Physical Machines (PM), Virtual Machines (VM), and Containers. Tasks are allocated to VMs in a round-robin manner. The Actor-Critic Neural Network (ACNN) is employed to predict the load of PMs, and overloading and underloading conditions are determined based on the load. The proposed GNBO hybrid optimization calculates the optimal solution considering predicted load, migration costs, resource utilization, energy consumption, and network bandwidth. This approach achieves a load of 0.177 MIPS, migration costs of 10.146 J, and optimizes energy consumption to 0.068 W.
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来源期刊
CiteScore
0.60
自引率
33.30%
发文量
0
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