The cloud computing infrastructure is a distributed environment and the existing research works have some provisioning problems such as suboptimal resource utilization and high execution time. The Heterogeneity Resource Management Framework for Dynamic Resource Provisioning (HRMF-DRP) is proposed for focusing on task scheduling and workload management. This framework incorporates advanced algorithms for dataset preprocessing, task clustering, workload prediction, and dynamic resource provisioning. For data preprocessing, the real-world workload traces were captured from the Planet Lab dataset that are taken as input for the preprocessing stage. The data preprocessing is responsible for ensuring data quality and reliability by using different models like missing data handling, outlier detection and removal as well as standardization and normalization. In this paper, the tasks are grouped into clusters by utilizing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) model and this model categorizes the data points into border points, core points and noise points based on their density. The temporal dependencies are captured for the workload prediction by using Long Short-Term Memory (LSTM) neural network model. A Gaussian Mixture Model (GMM) model is responsible for estimating the number of Virtual machines (VMs) present in the workload prediction process. The Self-Adaptive Genetic Algorithm (SAGA) is implemented for task mapping that adjusts the parameters to change workload patterns for contributing adaptability and robustness. The different experimental evaluations are conducted based on the task completion time, workload balance index, resource utilization efficiency and workload prediction accuracy. The proposed model achieved the workload prediction accuracy of 98.5%, cost of $89.6, execution time of 125ms, Task Completion Time (TCT) of 40ms, Workload Balance Index (WBI) of 0.96 and Resource Utilization Efficiency (RUE) of 0.93. The quantitative results collectively position HRMF-DRP as a practical and efficient solution, promising advancements in dynamic resource provisioning for cloud computing, particularly within the Infrastructure as a Service (IaaS) cloud model.

中文翻译：

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HRMF-DRP：克服云环境中配置挑战的下一代解决方案


云计算基础设施是一个分布式环境，现有的研究工作存在一些配置问题，例如资源利用率不高、执行时间较长等。动态资源配置的异构资源管理框架（HRMF-DRP）是为了关注任务调度和工作负载管理而提出的。该框架结合了用于数据集预处理、任务集群、工作负载预测和动态资源配置的高级算法。对于数据预处理，从 Planet Lab 数据集中捕获实际工作负载跟踪，并将其作为预处理阶段的输入。数据预处理负责通过使用不同的模型（例如缺失数据处理、异常值检测和删除以及标准化和归一化）来确保数据质量和可靠性。在本文中，利用基于密度的噪声应用空间聚类（DBSCAN）模型将任务分组为簇，该模型根据数据点的密度将数据点分为边界点、核心点和噪声点。使用长短期记忆 (LSTM) 神经网络模型捕获时间依赖性以进行工作负载预测。高斯混合模型 (GMM) 模型负责估计​​工作负载预测过程中存在的虚拟机 (VM) 数量。自适应遗传算法（SAGA）用于任务映射，调整参数以改变工作负载模式，从而提高适应性和鲁棒性。根据任务完成时间、工作负载平衡指标、资源利用效率和工作负载预测准确性进行不同的实验评估。 所提出的模型实现了 98.5% 的工作负载预测精度、89.6 美元的成本、125 毫秒的执行时间、40 毫秒的任务完成时间（TCT）、0.96 的工作负载平衡指数（WBI）和 0.93 的资源利用效率（RUE）。定量结果总体上将 HRMF-DRP 定位为实用且高效的解决方案，有望在云计算动态资源配置方面取得进步，特别是在基础设施即服务 (IaaS) 云模型中。