  The Cloud infrastructure offers to end users a broad set of heterogenous
computational resources using the pay-as-you-go model. These virtualized
resources can be provisioned using different pricing models like the unreliable
model where resources are provided at a fraction of the cost but with no
guarantee for an uninterrupted processing. However, the enormous gamut of
opportunities comes with a great caveat as resource management and scheduling
decisions are increasingly complicated. Moreover, the presented uncertainty in
optimally selecting resources has also a negatively impact on the quality of
solutions delivered by scheduling algorithms. In this paper, we present a
dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS
algorithm) for the management of scientific workflows in Cloud. Our model
minimizes both the makespan and the monetary cost by dynamically selecting
reliable or unreliable virtualized resources. For covering the uncertainty in
decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing
model of the resources that will host every task. We evaluate the performance
of the proposed algorithm using real workflow applications being tested under
the assumption of different probabilities regarding the revocation of
unreliable resources. Numerical results depict the performance of the proposed
approach and a comparative assessment reveals the position of the paper in the
relevant literature.
