DyRAC: Cost-aware Resource Assignment and Provider Selection for Dynamic Cloud Workloads

A primary concern for cloud users is how to minimize the total cost of ownership of cloud services. This is not trivial to achieve due to workload dynamics. Users need to select the number, size, type of VMs, and the provider to host their services based on available offerings. To avoid the complexity of re-configuring a cloud service, related work commonly approaches cost minimization as a packing problem that minimizes the resources allocated to services. However, this approach does not consider two problem dimensions that can further reduce cost: (1) provider selection and (2) VM sizing. In this paper, we explore a more direct approach to cost minimization by adjusting the type, number, size of VM instances, and the provider of a cloud service (i.e. a service deployment) at runtime. Our goal is to identify the limits in service cost reduction by online re-deployment of cloud services. For this purpose, we design DyRAC, an adaptive resource assignment mechanism for cloud services that, given the resource demands of a cloud service, estimates the most cost-efficient deployment. Our evaluation implements four different resource assignment policies to provide insight into how our approach works, using VM configurations of actual offerings from main providers (AWS, GCP, Azure). Our experiments show that DyRAC reduces cost by up to 33% compared to typical strategies.