Streaming applications have become increasingly im- portant and widespread, and they will be running on soon- to-be-prevalent chip multiprocessors (CMPs). We address the problem of energy-aware scheduling of streaming ap- plications, which are represented by task graphs, on a CMP using on/off and dynamic voltage scaling (DVS) on a per-processor basis. The goal is to minimize the en- ergy consumption of streaming applicationswhile satisfying two typical quality-of-service (QoS) requirements, namely, throughput and response time. To the best of our knowl- edge, this paper is the first work to tackle this problem. We make a key observation: the trade-off between static power and dynamic power should play a critical role in both parallel processing and pipelining that are used to re- duce energy consumption in the scheduling process. Based on this observation, we propose two scheduling algorithms, Scheduling1D and Scheduling2D, for linear and general task graphs, respectively. The proposed algorithms exploit the difference between the two QoS requirements and per- form processor allocation, task mapping and task speed scheduling simultaneously. Experimental results show that the proposed algorithms can achieve significant energy sav- ings (e.g., 24% on average for 70nm technology) over the baseline that only considers the response time requirement.