HRMF-DRP: A next-generation solution for overcoming provisioning challenges in cloud environments

• Published in: Journal of network and computer applications Vol. 231; p. 103982
• Main Authors: D, Devi, S, Godfrey Winster
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2024
• Subjects: Cloud computing environment; Dynamic resource provisioning; Gaussian mixture model; Genetic algorithm; K-means clustering; Long-short term memory; Task scheduling and real-world workload traces; Genetic algorithm; Cloud computing environment; Dynamic resource provisioning; Long-short term memory; Task scheduling and real-world workload traces; Gaussian mixture model; K-means clustering
• ISSN: 1084-8045
• DOI: 10.1016/j.jnca.2024.103982

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. •Introduces HRMF-DRP for advanced cloud computing, address provisioning challenges.•Applies DBSCAN for task clustering and LSTM for workload prediction.•Utilizes SAGA for self-adaptive genetic algorithm-based task mapping.•Achieves superior outcomes in task completion time, workload balance and cost.