Conference paper Open Access
Workflow scheduling is one of the key issues in
the management of workflow execution. Typically, a workflow
application can be modeled as a Directed-Acyclic Graph (DAG).
In this paper, we present GoDAG, an approach that can learn
to well schedule workflows on multi-resource clusters. GoDAG
directly learns the scheduling policy from experience through
deep reinforcement learning. In order to adapt deep reinforcement
learning methods, we propose a novel state representation,
a practical action space and a corresponding reward definition
for workflow scheduling problem. We implement a GoDAG
prototype and a simulator to simulate task running on multiresource
clusters. In the evaluation, we compare the GoDAG with
three state-of-the-art heuristics. The results show that GoDAG
outperforms the baseline heuristics, leading to less average
makespan to different workflow structures.