  Big data processing applications are becoming more and more complex. They are
no more monolithic in nature but instead they are composed of decoupled
analytical processes in the form of a workflow. One type of such workflow
applications is stream workflow application, which integrates multiple
streaming big data applications to support decision making. Each analytical
component of these applications runs continuously and processes data streams
whose velocity will depend on several factors such as network bandwidth and
processing rate of parent analytical component. As a consequence, the execution
of these applications on cloud environments requires advanced scheduling
techniques that adhere to end user's requirements in terms of data processing
and deadline for decision making. In this paper, we propose two Multicloud
scheduling and resource allocation techniques for efficient execution of stream
workflow applications on Multicloud environments while adhering to workflow
application and user performance requirements and reducing execution cost.
Results showed that the proposed genetic algorithm is an adequate and effective
for all experiments.
