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In warehouse-scale cloud datacenters, co-locating online services and offlinebatch jobs is an efficient approach to improving datacenter utilization. Tobetter facilitate the understanding of interactions among the co-locatedworkloads and their real-world operational demands, Alibaba recently released acluster usage and co-located workload dataset, which is the first publiclydataset with precise information about the category of each job. In this paper,we perform a deep analysis on the released Alibaba workload dataset, from theperspective of anomaly analysis and diagnosis. Through data preprocessing, nodesimilarity analysis based on Dynamic Time Warping (DTW), co-located workloadscharacteristics analysis and anomaly analysis based on iForest, we revealsseveral insights including: (1) The performance discrepancy of machines inAlibaba's production cluster is relatively large, for the distribution andresource utilization of co-located workloads is not balanced. For instance, theresource utilization (especially memory utilization) of batch jobs isfluctuating and not as stable as that of online containers, and the reason isthat online containers are long-running jobs with more memory-demanding andmost batch jobs are short jobs, (2) Based on the distribution of co-locatedworkload instance numbers, the machines can be classified into 8 workloaddistribution categories1. And most patterns of machine resource utilizationcurves are similar in the same workload distribution category. (3) In additionto the system failures, unreasonable scheduling and workload imbalance are themain causes of anomalies in Alibaba's cluster.

Anomaly Analysis for Co-located Datacenter Workloads in the Alibaba Cluster