  Scientific workflows have been predominantly used for complex and large scale
data analysis and scientific computation/automation and the need for robust
workflow scheduling techniques has grown considerably. But, most of the
existing workflow scheduling algorithms do not provide the required reliability
and robustness. In this paper, a new fault tolerant workflow scheduling
algorithm that learns replication heuristics in an unsupervised manner has been
proposed. Furthermore, the use of light weight synchronized checkpointing
enables efficient resubmission of failed tasks and ensures workflow completion
even in precarious environments. The proposed technique improves upon metrics
like Resource Wastage and Resource Usage in comparison to the Replicate-All
algorithm, while maintaining an acceptable increase in Makespan as compared to
the vanilla Heterogeneous Earliest Finish Time (HEFT).
