Cost-Effective Cloud Resource Provisioning Using Linear Regression

In the era of cloud computing, accessing virtual computing resources has become increasingly convenient for users to meet their demands. Cloud providers offer two primary payment plans for virtual resource provisioning: reservation and on-demand. The reservation plan requires users to reserve resources and pay upfront, making it more cost-effective for long-term requirements despite demand uncertainty. Conversely, the on-demand plan charges based on actual resource usage, making it more expensive but suitable for short-term needs. Efficient resource provisioning is crucial to balance user demands and costs, as inefficient provisioning can lead to high costs. A key challenge is determining the optimal number of resources to reserve to accommodate uncertain demands while minimizing costs. This paper addresses the resource reservation problem in cloud environments by focusing on the optimal reservation of virtual machines (VMs). We propose a linear regression approach that fits a linear function to features such as past demands and previously reserved instances that are still available to determine the quantity of VMs to reserve. Our model assigns specific weights to these features to predict required reserved instances, minimizing the overall cost, including both the expense of reserving resources and renting additional on-demand resources as needed. Our evaluation, based on real standard workload traces, demonstrates the effectiveness of our approach in achieving cost-efficient resource provisioning, reducing the total cost by efficiently balancing reserved and on-demand resources.