  In warehouse-scale cloud datacenters, co-locating online services and offline
batch jobs is an efficient approach to improving datacenter utilization. To
better facilitate the understanding of interactions among the co-located
workloads and their real-world operational demands, Alibaba recently released a
cluster usage and co-located workload dataset, which is the first publicly
dataset with precise information about the category of each job. In this paper,
we perform a deep analysis on the released Alibaba workload dataset, from the
perspective of anomaly analysis and diagnosis. Through data preprocessing, node
similarity analysis based on Dynamic Time Warping (DTW), co-located workloads
characteristics analysis and anomaly analysis based on iForest, we reveals
several insights including: (1) The performance discrepancy of machines in
Alibaba's production cluster is relatively large, for the distribution and
resource utilization of co-located workloads is not balanced. For instance, the
resource utilization (especially memory utilization) of batch jobs is
fluctuating and not as stable as that of online containers, and the reason is
that online containers are long-running jobs with more memory-demanding and
most batch jobs are short jobs, (2) Based on the distribution of co-located
workload instance numbers, the machines can be classified into 8 workload
distribution categories1. And most patterns of machine resource utilization
curves are similar in the same workload distribution category. (3) In addition
to the system failures, unreasonable scheduling and workload imbalance are the
main causes of anomalies in Alibaba's cluster.
