基于超额订阅与服务定价利用的工业云资源管理利润最大化框架
An Oversubscription and Service Pricing Exploitation-Based Profit Maximization Framework for Industry Cloud Resource Management
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中文总结 AI 辅助
该工业云资源管理框架结合超额订阅与异构服务定价，采用自适应集成机器学习预测及模糊C均值聚类，经仿真验证可显著降低电费、功耗等，提升资源利用率与利润。
中文摘要 AI 辅助
本文提出了一种新颖的工业云资源管理框架，该框架利用资源超额订阅和异构服务定价模型，以最大化工业云提供商的利润和运营效率。该框架提出了一种由自适应集成机器学习驱动的预测模型，用于基于各用户虚拟机（VM）的历史资源利用率，主动估计虚拟机的资源利用率，以最小化因用户超额订阅导致的资源浪费。据此，使用模糊C均值聚类对具有相似预测资源使用情况的虚拟机进行分组，这有助于确定在执行用户请求前需部署的特定配置虚拟机的数量。同时，该框架包含两类不同的云服务定价模型，即延迟敏感模型和尽力而为模型。据此，通过选择最合适的虚拟机对用户请求进行分类和执行，目标是最大化收入并降低云数据中心（CDC）的电力成本。使用两个基准虚拟机轨迹进行的实验仿真及与最新方法的比较验证了所提框架的性能，其可显著降低55.56%的电费，最多降低60.7%的功耗和51%的活动服务器数量，同时最多提高60%的资源利用率和51.18%的利润。
英文摘要
This article proposed a novel industry cloud resource management framework that exploits resource oversubscription and heterogeneous service pricing models to maximize profitability and operational efficiency for industry cloud providers. The framework proposes an adaptive ensemble machine learning driven prediction model for proactive estimation of resource utilization of Virtual Machines (VM)s based on previous resource utilization of respective users' VMs to minimize resource wastage due to oversubscription by them. Accordingly, the VMs having similar predicted resource usage are grouped using Fuzzy C means clustering. This helps to determine the required number of VMs with specific configuration to be deployed before executing user requests. Concurrently, the framework incorporates two distinct categories of cloud service pricing models, namely the Delay Sensitive Model and the Best-Effort Model. Accordingly, the user requests are classified and executed by selecting the most suitable VMs, with the goal of maximizing revenue and reducing electricity costs in cloud data centers (CDCs). Experimental simulation and comparison against state-of-the-art methods, using two benchmark VM traces, validates the performance of proposed framework. It significantly reduces electricity bills by 55.56 percentage, power consumption and active servers by up to 60.7 percentage and 51 percentage, respectively, while improving resource utilization and profits by up to 60 percentage and 51.18 percentage, respectively
