  With the increasing popularity of cloud computing, datacenters are becoming
more important than ever before. A typical datacenter typically consists of a
large number of homogeneous or heterogeneous servers connected by networks.
Unfortunately, these servers and network equipment are often under-utilized and
power hungry. To improve the utilization of hardware resources and make them
power efficiency in datacenters, workload characterization and analysis is at
the foundation. In this paper, we characterize and analyze the job arriving
rate, arriving time, job length, power consumption, and temperature dissipation
in a real world datacenter by using statistical methods. From the
characterization, we find unique features in the workload can be used to
optimize the resource utilization and power consumption of datacenters
