An Ensemble Clustering Approach for Modeling Hidden Categorization Perspectives for Cloud Workloads

Abstract Effectively managing cloud resources is a complex task due to the interdependencies of various cloud-hosted services and applications. This task is integral to workload categorization, which groups similar cloud workloads to inform workload scheduling and resource management procedures. Although traditional clustering algorithms can categorize workloads into single data grouping patterns, the multifaceted nature of cloud workloads may conceal multiple valid categorization perspectives, indicating a need for a more adaptable clustering approach. This paper presents a novel ensemble clustering approach to enhance the scheduling workload categorization process in cloud computing. Our approach combines various normalization and transformation techniques, including principal component analysis, to form multiple data preprocessing pipelines. The data derived from these pipelines then serve as input for multiple base clustering learners. A novel combined score based on the Silhouette score, Calinski-Harabasz index, and Davies-Bouldin index is then employed to select the optimal models and preprocessing setups. The clustering outcomes of these models are encoded and inputted into a meta-clustering algorithm, effectively capturing complex categorization perspectives. Evaluation of this approach using real-world workload trace data from Microsoft Azure significantly enhances workload segmentation efficacy, thereby improving resource management and quality of service in cloud data centers. This method offers a promising pathway toward improved workload clustering in cloud computing, demonstrating the practical utility of advanced ensemble clustering techniques.