Performance and Energy Consumption Aspects in Migrating Big Data Systems to the Cloud

Massive data processing areas (big data) have taken advantage on the availability of cloud computing. Applications processed in virtualized environments have become usual. Since datacenters are responsible for demanding around of 2% of the global energy, understand the impact of virtualization on big data applications is necessary, as well its energy consumption. In this work, we evaluated two resources scheduling policies to process big data in three different sizes of virtual machines. Our experiments showed that execution time (performance) and consumption of a big data tasks can result in relevant discrepancies even with the same resources budget.