  With the growing amount of data, data processing workloads and the management
of their resource usage becomes increasingly important. Since managing a
dedicated infrastructure is in many situations infeasible or uneconomical,
users progressively execute their respective workloads in the cloud. As the
configuration of workloads and resources is often challenging, various methods
have been proposed that either quickly profile towards a good configuration or
determine one based on data from previous runs. Still, performance data to
train such methods is often lacking and must be costly collected.
  In this paper, we propose a collaborative approach for sharing anonymized
workload execution traces among users, mining them for general patterns, and
exploiting clusters of historical workloads for future optimizations. We
evaluate our prototype implementation for mining workload execution graphs on a
publicly available trace dataset and demonstrate the predictive value of
workload clusters determined through traces only.
