Characterizing Co-located Datacenter Workloads: An Alibaba Case Study
Published in
Asia Pacific Workshop on Systems(2018)External Links:
TL
TL;DR
Alibaba's co-located workload trace is analyzed, the first publicly available dataset with precise information about the category of each job, and reveals insights that are useful for system designers and IT practitioners working on cluster management systems.
Abstract
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.
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