  In this paper, we propose Peacock, a new distributed probe-based scheduler
which handles heterogeneous workloads in data analytics frameworks with low
latency. Peacock mitigates the \emph{Head-of-Line blocking} problem, i.e.,
shorter tasks are enqueued behind the longer tasks, better than the
state-of-the-art. To this end, we introduce a novel probe rotation technique.
Workers form a ring overlay network and rotate probes using elastic queues. It
is augmented by a novel probe reordering algorithm executed in workers. We
evaluate the performance of Peacock against two state-of-the-art probe-based
solutions through both trace-driven simulation and distributed experiment in
Spark under various loads and cluster sizes. Our large-scale performance
results indicate that Peacock outperforms the state-of-the-art in all cluster
sizes and loads. Our distributed experiments confirm our simulation results.
