Regression-Based Dynamic Provisioning and Monitoring for Responsive Resources in Cloud Infrastructure Networks

Cloud computing model is the most complex computing model that requires implementing effective techniques to manage infrastructure resources of datacenters. Unproductive tasks scheduling can lead to an increase in the operational cost of cloud provider side, which in turn increases the cloud services cost at cloud consumer side. One of the effective techniques to address these issues in cloud datacenters is the elasticity by allowing dynamic resource provisioning based on the current demand and varying workload running upon virtual machines (VMs) over time. This leads to an increase in the resource utilization, and reduced power consumption by turning off the idle physical machines. However, the dynamic resource provisioning due to the growing service demand and higher quality of service requirements of the users can cause a violation of service level agreement. In this paper, we propose a model based on linear regression to manage and reformulate cloud users requests and dynamically generating rules based on historical data of their requests in order to update association functions to address and adapt the changes of different types of workloads running on the cloud provider datacenter. The experiments and simulation results based on dynamic workloads show the proposed algorithm significantly increases the resource utilization on cloud datacenter.