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.