Data center resource management with temporal dynamic workload

The proliferation of Internet services drives the data center expansion in both size and the number. More importantly, the energy consumption (as part of the total cost of ownership (TCO)) has become a social concern. When the workload demand is given, the data center operators desire minimizing their TCO. On the other hand, when the workload demand is unknown while the requirements on quality of experience (QoE) of the Internet services are given, the data center operators need to determine the appropriate amount of resources and design redirection strategies in presence of multiple data centers to guarantee the QoE. For the first problem, we present formulations to minimize server energy consumption and server cost with dynamic temporal demand and propose novel aggregation methods to reduce computational complexity. The Dynamic Voltage/Frequency Scaling (DVFS) capacity is further considered in our model. Our numerical results show that adopting DVFS results in a significant reduction of energy consumption. For the second problem, the data center provides resources via the cloud computing model. We propose a hierarchical modeling approach that can easily combine all components in the data center provisioning environment. The numeric results show that our model serves as a very useful analytical tool for data center operators to provide appropriate resources as well as design redirection strategies.