Power optimization has become a key challenge in the design of today's data centers. Many recent studies have shown that there are typically three major power consumers in a data center: servers, cooling systems, and the data center network (DCN). While the power efficiency of data center cooling has been significantly improved in the recent years, it is foreseeable that servers and DCN are becoming the two most significant power consumers in the future. Unfortunately, while existing research efforts focus mainly on computer servers to lower their power consumption, only few studies have tried to address the power consumption of DCN, which can account for about 20% of the total power consumption of a data center. This project aims to design a correlation-aware power optimization framework that jointly minimizes the total power consumption of the DCN and servers in a data center. The success of this timely project can greatly impact the data center design by significantly reducing DCN power consumption.
The main technical approach of the power optimization framework is correlation-aware server and traffic consolidations. Similar to servers, a DCN is also often underutilized. As a result, traffic flows can be consolidated onto a small set of links and switches, such that unused network devices can be shut down for power savings. Server and traffic consolidations should be conducted jointly because server consolidation without considering the DCN may cause traffic congestion and thus degraded network performance. On the other hand, server consolidation may change the DCN topology, allowing new opportunities for power savings. This framework is designed based on a key observation that the utilizations of different servers or the bandwidth demands of different flows usually do not peak at exactly the same time. Therefore, if the correlations among servers and traffic flows are considered, more power savings can be achieved during server and traffic consolidations. The power optimization framework also includes multi-dimensional DCN power optimization with flow completion time guarantees and highly scalable optimization algorithms for large-scale data centers.
