Genetic algorithms for energy efficient virtualized data centers

Avast majority of servers in classical data centers of all scales are underutilized for a significant amount of time. These servers operate at a very low rate of efficiency and consume huge amounts of energy. In this work, we investigate how dynamic consolidation and workload forecasting can be exploited to increase the energy efficiency of a virtualized, heterogeneous server infrastructure. We base our evaluation on real utilization traces of a production system operated by the University of Vienna's central IT department. The traces contain the CPU utilization of more than 30 VMs over a period of four weeks. These VMs offer all kinds of services to students, staff and other visitors. We use these traces to investigate a business infrastructure scenario, where energy costs are just one of several parts of operational costs. We present a novel cost model using configurable penalties for the most important operational cost categories. We compare the total costs of a bin-packing related heuristic and a new genetic VM mapping algorithm used for dynamic consolidation (GA) and load balancing (LB). We tradeoff forecasting against resource reserves in combination with shorter measurement intervals. We demonstrate the flexibility of a genetic algorithm. The GA and LB approaches are directly influenced by penalizing cost parameters. Our cost model allows easy adaption by infrastructure operators to implement custom priorities and optimization goals.