Guaranteed performance for data-parallel applications is important for both service providers and cloud data centers that host such services. A job of data-parallel applications involves communication among multiple machines to transmit intermediate results. Such communication comprises a collection of parallel flows, which is abstracted as a coflow in recent proposals. In this paper, we study the problem of meeting deadlines for coflows in data center networks. Existing flow-level scheduling schemes are insufficient to guarantee the coflow-level performance, since a coflow can meet its deadline only when all its constituent flows finish on time. Due to the scarce bandwidth on the network bottleneck, it is vital to coordinate concurrent coflows to meet as many deadlines as possible. We present Chronos, a scheduling framework that captures the correlation of flows belonging to a coflow, and handles the resource allocation among multiple concurrent coflows. Chronos is work-conserving and starvation-free without integrating complicated admission control mechanisms. We show via extensive simulations on ns3 that Chronos can make 1.6× more coflows meet their deadlines compared to flow-level schemes.