Cloud data center workloads have time-dependencies and hence they are non-i.i.d (independent and identically distributed). In this paper, we propose a new model-based method for creating synthetic workload traces for cloud data enters that have similar time characteristics and cumulative distributions to those of the actual traces. We evaluate our method using the actual resource request traces of Azure collected in 2019 and the well-known Google cloud trace. Our method enables generating synthetic traces that can be used for a more realistic evaluation of cloud data centers.