  Stream workflow application such as online anomaly detection or online
traffic monitoring, integrates multiple streaming big data applications into
data analysis pipeline. This application can be highly dynamic in nature, where
the data velocity may change at runtime and therefore the resources should be
managed overtime. To manage these changes, the orchestration of this
application requires a dynamic execution environment and dynamic scheduling
technique. For the former requirement, Multicloud environment is a visible
solution to cope with the dynamic aspects of this workflow application. While
for the latter requirement, dynamic scheduling technique not only need to
adhere to end user's requirements in terms of data processing and deadline for
decision making, and data stream sources location constraints, but also adjust
provisioning and scheduling plan at runtime to cope with dynamic variations of
stream data rates. Therefore, we propose a two-phase adaptive scheduling
technique to efficiently schedule dynamic workflow application in Multicloud
environment that can respond to changes in the velocity of data at runtime. The
experimental results showed that the proposed technique is close to the lower
bound and effective for different experiment scenarios.

Distributed, Parallel, and Cluster Computing