  Warehouse-scale cloud datacenters co-locate workloads with different and
often complementary characteristics for improved resource utilization. To
better understand the challenges in managing such intricate, heterogeneous
workloads while providing quality-assured resource orchestration and user
experience, we analyze Alibaba's co-located workload trace, the first publicly
available dataset with precise information about the category of each job. Two
types of workload---long-running, user-facing, containerized production jobs,
and transient, highly dynamic, non-containerized, and non-production batch
jobs---are running on a shared cluster of 1313 machines. Our multifaceted
analysis reveals insights that we believe are useful for system designers and
IT practitioners working on cluster management systems.
