To improve system resource utilization, consolidating multi-tenants’ workloads on the common computing infrastructure is a popular way for the cloud data center. The typical deployment of the modern cloud data center is co-locating online services and offline analytics applications. However, the co-locating deployment inevitably brings workloads’ competitions for system resources, such as the CPU and the memory resources. These competitions result in that the user experience (the request latency) of the online services cannot be guaranteed. More and more efforts try to assure the latency requirements of services as well as the system resource efficiency. Mixing the cloud workloads and quantifying resource competition is one of the prerequisites for solving the problem. We proposed a benchmark suite—DCMIX as the cloud mixed workloads, which covered multiple application fields and different latency requirements. Furthermore the mixture of workloads can be generated by specifying mixed execution sequence in the DCMIX. We also proposed the system entropy metric, which originated from some basic system level performance monitor metrics as the quantitative metric for the disturbance caused by system resource competition. Finally, compared with the Service-Standalone mode (only executing the online service workload), we found that percentile latency of the service workload under the Mixed mode (workloads mix execution) increased 3.5 times, and the node resource utilization under that mode increased 10 times. This implied that mixed workloads can reflect the mixed deployment scene of cloud data center. Furthermore, the system entropy of mixed deployment mode was 4 times larger than that of the Service-Standalone mode, which implied that the system entropy can reflect the disturbance of the system resource competition. We also found that the isolation mechanism has some efforts for mixed workloads, especially the CPU-affinity mechanism.