Adaptive Execution of Scientific Workflow Applications on Clouds - Cloud Computing with e-Science Applications - page 86

The Cloudbus Workflow Engine incorporates a market-oriented utility
computing model that supports grids, desktops, and clouds. It supports the
concept of InterCloud for allocation and management of resources for execu-
tion of workflow applications [1].
Kim et al. [14] proposed a WfMS able to deploy workflows in hybrid infra-
structures composed of TeraGrid nodes and Amazon EC2 resources. Our
proposed system, on the other hand, can also leverage resources from private
and public cloud providers.
Gogouvitis et al. [15] proposed a WfMS for deploying workflow applica-
tions on virtualized environments that is able to utilize resources from
public clouds. However, it has no dynamic provisioning capabilities to speed
application execution and to meet real-time application performance require-
ments as does our approach.
Fernandez et al. [16] proposed a cloud WfMS that applies a concept called
chemical programming for the application scheduling. The system, however,
does not offer dynamic resource provisioning capabilities and autonomic
self-healing features.
CometCloud [17] is a more recent tool that implements an infrastructure
for autonomic management of workflow applications on clouds.
4.8 Conclusions and Future Work
Clouds became a powerful platform for e-research as they enable scientists to
have access to elastic, cost-effective, and virtually infinite computing power.
Because clouds provide their users the view of infinite computing capac-
ity, the real limitations on the scalability of the applications lie in the avail-
able budget for cloud usage and limitations in the applications themselves.
Therefore, it is important that scientific application developers enable their
applications to get the most from the cloud.
In this chapter, we discussed recent trends for execution of workflows in clouds.
The architecture we presented is composed of a platform layer and an applica-
tion layer. The platform layer enables operations such as dynamic resource pro-
visioning, autonomic scheduling of applications, fault tolerance, security, and
privacy in data access. The features enabled by this layer can be explored by
virtually any application that can be described as scientific workflow.
In the application layer, we discussed a data analytics application enabling
simulation of the public transport system of Singapore and the effect of
abnormal events in the transport network. The application consists of an
agent-based simulation of the public transport system of Singapore, and it
allows evaluation of effects of incidents (such as train delays) in the flow of
passengers in the country.