Towards a model for cloud computing cost estimation with reserved resources
Ilja Livenson, Georg Singer, Satish Narayana Srirama, Ulrich Norbisrath, Marlon Dumas · 2010
Cloud computing has been touted as a lower-cost alternative to in-house IT infrastructure recently. However, case studies and anecdotal evidence suggest that it is not always cheaper to use cloud computing resources in lieu of in-house infrastructure. Also, several factors influence the cost of sourcing computing resources from the cloud. For example, cloud computing providers offer virtual machine instances of different types. Each type of virtual machine strikes a different tradeoff between memory capacity, processing power and cost. Also, some providers offer discounts over the hourly price of renting a virtual machine instance if the instance is reserved in advance and an upfront reservation fee is paid. The choice of virtual machine types and the reservation schedule have a direct impact on the running costs. Therefore, IT decision-makers need tools that allow them to determine the potential cost of sourcing their computing resources from the cloud and the optimal sourcing strategy. This paper presents an initial model for estimating the optimal cost of replacing in-house servers with cloud computing resources. The model takes as input the load curve, RAM, storage and network usage observed in the in-house servers over a representative season. Based on this input, the model produces an estimate of the amount of virtual machine instances required across the planning time, in order to replace the in-house infrastructure. As an initial validation of the model, we have applied it to assess the cost of replacing an HPC cluster with virtual machine instances sourced from Amazon EC2.