Capacity Management in Hyper-Scale Datacenters using Predictive Modelling

Abstract: Big data applications have become increasingly popular with the emerge of cloud computing and the explosion of artificial intelligence. Hence, the increasing adoption of data-hungry machines and services is driving the need for more power to keep the datacenters of the world running. It has become crucial for large IT companies such as Google, Facebook, Amazon etc. to monitor the energy efficiency of their datacenters’ facilities and take actions on optimization of these heavy consumers of electricity. This master thesis work proposes several predictive models to forecast PUE (Power Usage Effectiveness), regarded as the industry-de-facto metric for measuring datacenter’s IT power efficiency. This approach is a novel capacity management technique to predict and monitor the environment in order to prevent future disastrous events, which are strictly unacceptable in datacenter’s business.