Power optimization for data center networks (DCNs) has recently received increasing research attention, since a DCN can account for up to 20% of the total power consumption of a data center. An effective power-saving approach for DCNs is traffic consolidation, which consolidates traffic flows onto a small set of links and switches such that unused network devices can be shut down for power savings. While this approach has shown great promise, existing solutions are mostly centralized and do not scale well for large-scale DCNs. In this paper, we propose DISCO, a DIStributed traffic flow COnsolidation framework, with correlation analysis and delay constraints, for large-scale power efficient data center network. DISCO features two distributed traffic consolidation algorithms that provide different trade-offs (as desired by different DCN architectures) between scalability, power savings, and network performance. First, a flow-based algorithm is proposed to conduct consolidation for each flow individually, with greatly improved scalability. Second, an even more scalable switch-based algorithm is proposed to consolidate flows on each individual switch in a distributed fashion. We evaluate the DISCO algorithms both on a hardware testbed and in large-scale simulations with real DCN traces. The results show that, compared with state-of-the-art centralized solutions, DISCO can achieve nearly the same power savings with more than three orders of magnitude smaller problem size for individual optimizers (10⁴ to 10⁶ times faster for a DCN at the scale of 10K servers). The convergence of DISCO is also proved theoretically and evaluated experimentally.