Dynamic Underload Host Detection for Performance Enhancement in Cloud Environment

Cloud computing provides on-demand availability of computing resources, data storage and computing power. Cloud service providers often have functions distributed over multiple locations, each of which is a data center.Cloud computing relies on sharing of resources to achieve coherence and typically uses a pay-as-you-go model. However, cloud computing faces some significant challenges in efficiently managing resources, optimizing performance, and reducing energy consumption. Among the mentioned challenges ensuring optimal energy consumption is the key concern. Further, improvisation in energy efficiency helps minimize carbon emissions and also enhances overall performance. One of the primary reason of energy misuse in computation is host underload i.e., the host is not operating on its optimum capacity. The challenge of host underload detection, can be efficiently managed with the help of linear regression method. Our proposed approach aims to simultaneously reduce consumption of energy, minimize virtual machine (VM) migration and uphold SLA (Service Level Agreement) compliance. Any reduction in the number of VM migrations, results in better resource utilization and also mitigates the impact on performance caused by frequent migrations. This approach seeks to strike a balance among energy efficiency and meeting SLA, without compromising quality of service provided.