Cloud data centers try to achieve two primary but competing goals for latency-sensitive services: meeting latency servicelevel objective (SLO) and reducing energy utilization. In this paper, we perform a joint optimization of both goals, such that given a tail latency constraint and request rate, the number of active servers and their corresponding core frequencies which cause the least energy overhead can be derived. Both the server sleep mode and setup delay are taken into account. Based on the assumption that requests arrive following a Poisson process, we provide a numerical method to compute the optimal number of active servers and a closed-form expression for their corresponding core frequency. We propose how the optimization technique can be used for real, bursty, non-Poisson data center traffic.