2018/08/08 by Yue Cheng, Cheng, Yue, Zheng Chai +3 · 2 citations
Computer Science · #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Parallel #Software System Performance and Reliability #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1808.02919
openalex publication_date 2018/08/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.