With the burst of data volume and application complexity, applications running in cloud data centers are scheduled with two categories: data-intensive batch jobs that strive for fast completions, and customer-facing online services that pursue low response latencies. In this dissertation, we aim to separately identify the key factors when scheduling each of the two workloads, and optimize their performances with tailored scheduling designs. For data-parallel batch jobs, the communication is often the bottleneck, in which a collection of concurrent flows, termed coflow, transfer intermediate data between computation stages (e.g., shuffle phase in a MapReduce job). Scheduling coflows in a shared cluster is hard, where efficiency–minimized average coflow completion times (CCTs) and fairness–predictable networking performance are conflicting with each other. In this regard, we make the following contributions. First, we present Utopia, a coflow scheduling mechanism that minimizes the average CCT while ensuring predictable performance with isolation guarantees. Utopia achieves the best of both worlds by preferentially scheduling coflows in ascending order of their CCTs under fair-sharing alternatives, and providing provable network isolations in the long run. Second, for non-clairvoyant coflow scheduling where the coflow size is unavailable in advance (e.g., multi-stage applications with pipelines), we present non-clairvoyant DRF (NC-DRF), the other scheduling policy that provides predictable coflow completions. NC-DRF enforces fair-sharing scheduling based on the amount of flows a coflow has on each link, and outperforms alternatives by being aware of the coflow-level communication patterns. Trace-driven simulations and EC2 deployments have empirically confirmed that both Utopia and NC-DRF outperform existing alternatives and achieves long-term isolation guarantee. Online cloud services, on the other hand are deployed as long-running applications (LRAs) in containers, where the container placement is of paramount ...