Power-aware Multi-DataCenter Management using Machine Learning

The cloud relies upon multi-datacenter (multi-DC) infrastructures distributed along the world, where people and enterprises pay for resources to offer their web-services to worldwide clients. Intelligent management is required to automate and manage these infrastructures, as the amount of resources and data to manage exceeds the capacities of human operators. Also, it must take into account the cost of running the resources (energy) and the quality of service towards web-services and clients. (De-)consolidation and priming proximity to clients become two main strategies to allocate resources and properly place these web-services in the multi-DC network. Here we present a mathematical model to describe the scheduling problem given web-services and hosts across a multi-DC system, enhancing the decision makers with models for the system behavior obtained using machine learning. After running the system on real DC infrastructures we see that the model drives web-services to the best locations given quality of service, energy consumption, and client proximity, also (de-)consolidating according to the resources required for each web-service given its load.