An Adaptive Control Method for Resource Provisioning with Resource Utilization Constraints in Cloud Computing

Abstract

Cloud computing enables users to purchase virtual resources on demand; therefore, the service requests change over time. Dynamic resource provisioning for cloud computing has become a key challenge. To reduce the associated costs, resource utilization must be improved, and ensure the Quality of Service (QoS) to meet the service-level agreements (SLAs). However, it is difficult to improve the resource utilization level and maintain a high QoS with fluctuating workloads in shared cloud computing systems. In this paper, we propose an adaptive control method for resource provisioning in cloud computing systems to simultaneously improve the resource utilization, achieve a satisfactory QoS, and react to the dynamic workload. We proposed an approach integrating adaptive multi-input and multi-output (MIMO) control and radial basis function (RBF) neural network to react to the highly dynamic workloads, and precisely control the resource allocation to improve resource utilization based on the maximum allowable QoS requirements. Experiments based on real-world workloads show that the proposed approach jointly improves resource utilization, maintains a satisfactory QoS, and handles workload fluctuations in a coordinated manner.

Keywords

• Adaptive control
• Neural network
• Dynamic resource provisioning
• Cloud computing
• QoS
• Resource efficiency