IaaS reserved contract procurement optimisation with load prediction

• Introduction of an algorithm for automated IaaS contract procurement.
• Do workload prediction techniques prove valuable w.r.t. acquiring IaaS contracts?
• An extensive evaluation using a large set of real-world web traffic workloads.

The increased adoption of cloud computing, combined with the recent proliferation of pricing plans has increased the relevance of automating the complex and time consuming tasks of selecting, procuring and managing cloud resources. In this work, we present an approach to automate the procurement decision of reserved contracts in the context of Infrastructure-as-a-Service (IaaS) providers. Such reserved contracts offer the consumer a significant price reduction compared to pay-per-hour pricing models, in exchange for an upfront payment. We present an algorithm that uses load prediction to make cost-efficient purchasing decisions, and evaluate whether the use of automated time series forecasting proves useful in this context. The algorithm takes into account a wide range of contract types as well as the organisation’s current contract portfolio. We evaluate the effectiveness of different contract renewal policies and load predictors based on ARIMA, Holt–Winters and exponential smoothing techniques, and compare these with the performance of a simple predictor. We adopt a large set of 51 real-world web application load traces to evaluate the performance and scalability of our algorithm through simulation. Our results show that the algorithm is able to significantly reduce IaaS resource costs through automated reserved contract procurement, but that the use of advanced prediction techniques only proves beneficial in specific cases. The algorithms scalability is shown to be sufficient for its adoption in settings with a large number of contracts.