Linear and Logistic Regression Based Monitoring for Resource Management in Cloud Networks

Abstract

Understanding and implementing the effective techniques to manage the infrastructure resources of cloud datacenter has recently become important. The energy consumption and ineffective resource utilization 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 server consolidation by allowing multiple virtual machines (VMs) include varying workload to host in a single physical machine. This leads to an increase in the resource utilization, and reduced power consumption by turning off the idle physical machines. However, consolidating the virtual machines due to varying workload in cloud applications can cause a violation of service level agreement. In this paper, we propose a model based on linear and logistic regression to detect overloaded hosts by dynamically generating rules based on historical data of hosts and datacenter 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 outperforms the other competitive host detection algorithms.