Joint optimization of the cost of computation and virtual machine image storage in cloud infrastructure

In the field of cost optimization in cloud computing infrastructure, different strategies are used to calculate Virtual Machine (VM) allocations to support workloads while minimizing costs. Usually, VM allocations strategies are focused on determining the number and types of VMs required to support workloads at every moment, but generally they lack procedures to account for the storage costs of VMs. A significant part of these costs is generated by the storage of the Virtual Machine Images (VMIs) required to deploy VMs. In this paper, we present an improvement to a state-of-the-art VM allocation strategy, by integrating VMI costs. To achieve this, the allocation model of the strategy is extended. Then a wide set of experiments is carried out to study the improvements in the cost optimization. Several factors are analyzed and the most significant ones identified. The experimentation shows savings up to 20%, but are very dependent on the VMI sizes, the characteristics of the workload and the cloud infrastructure.