Taming Energy Costs of Large Enterprise Systems Through Adaptive Provisioning

One of the most pressing concerns in modern datacenter management is the rising cost of operation. Therefore, reducing variable expense, such as energy cost, has become a number one priority. However, reducing energy cost in large distributed enterprise system is an open research topic. These systems are commonly subjected to highly volatile workload processes and characterized by complex performance dependencies. This paper explicitly addresses this challenge and presents a novel approach to Taming Energy Costs of Larger Enterprise Systems (Tecless). Our adaptive provisioning methodology combines a low-level technical perspective on distributed systems with a high-level treatment of workload processes. More concretely, Tecless fuses an empirical bottleneck detection model with a statistical workload prediction model. Our methodology forecasts the system load online, which enables on-demand infrastructure adaption while continuously guaranteeing quality of service. In our analysis we show that the prediction of future workload allows adaptive provisioning with a power saving potential of up 25 percent of the total energy cost.