Agglomerative Hierarchical Clustering (AHC) is a general type of Hierarchical Clustering (HC) that forms clusters from the “bottom-up.” This paper focuses on the development of AHC methods based on ensemble-based approaches. Accordingly, we develop an AHC framework based on clusters clustering along with an innovative similarity criterion that performs clustering through ensemble approaches. The proposed algorithm consists of three main steps. In the first step, a group of single AHC methods are combined to detect relationships between samples and as well as the formation of initial clusters. The similarity of the samples is calculated using an innovative similarity criterion based on the clusters created. In the second step, all the initial clusters created by different methods are re-clustered to form hyper-clusters. After clusters clustering, each sample is assigned to a hyper-cluster with maximum similarity to create the final clusters in the third step. The comprehensive experimental study has been performed to evaluate the performance of the proposed algorithm based on several benchmark datasets from the UCI machine learning repository. The results clearly show that the proposed ensemble AHC-based framework performs better than the state-of-the-art methods.

Resource management plays a key role in the cloud-computing environment in which applications face with dynamically changing workloads. However, such dynamic and unpredictable workloads can lead to performance degradation of applications, especially when demands for resources are increased. To meet Quality of Service (QoS) requirements based on Service Level Agreements (SLA), resource management strategies must be taken into account. The question addressed in this research includes how to reduce the number of SLA violations based on the optimization of resources allocated to users applying an autonomous control cycle and a fuzzy knowledge management system. In this paper, an adaptive and fuzzy resource management framework (AFRM) is proposed in which the last resource values of each virtual machine are gathered through the environment sensors and are sent to a fuzzy controller. Then, AFRM analyzes the received information to make decision on how to reallocate the resources in each iteration of a self-adaptive control cycle. All the membership functions and rules are dynamically updated based on workload changes to satisfy QoS requirements. Two sets of experiments were conducted on the storage resource to examine AFRM in comparison to rule-based and static-fuzzy approaches in terms of RAE, utility, number of SLA violations, and cost applying HIGH, MEDIUM, MEDIUM-HIGH, and LOW workloads. The results reveal that AFRM outweighs the rule-based and static-fuzzy approaches from several aspects.