Cost optimization in the cloud : An analysis on how to apply an optimization framework to the procurement of cloud contracts at Spotify
Abstract: In the modern era of IT, cloud computing is becoming the new standard. Companies have gone from owning their own data centers to procuring virtualized computational resources as a service. This technology opens up for elasticity and cost savings. Computational resources have gone from being a capital expenditure to an operational expenditure. Vendors, such as Google, Amazon, and Microsoft, offer these services globally with different provisioning alternatives. In this thesis, we focus on providing a cost optimization algorithm for Spotify on the Google Cloud Platform. To achieve this we construct an algorithm that breaks up the problem in four different parts. Firstly, we generate trajectories of monthly active users. Secondly, we split these trajectories up in regions and redistribute monthly active users to better describe the actual Google Cloud Platform footprint. Thirdly we calculate usage per monthly active users quotas from a representative week of usage and use these to translate the redistributed monthly active users trajectories to usage. Lastly, we apply an optimization algorithm to these trajectories and obtain an objective value. These results are then evaluated using statistical methods to determine the reliability. The final model solves the problem to optimality and provides statistically reliable results. As a consequence, we can give recommendations to Spotify on how to minimize their cloud cost, while considering the uncertainty in demand.
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