In Figure 7, a management plan called shutdown-
ClusterMember is triggered by the policy, following
the event-condition-action (ECA) rule used in policy
systems. Figure 8 illustrates the plan: First the plan
has to check whether the cluster manager is available
- obviously, this is a safety measure. If successful, the
plan issues a request against the cluster member to
shut down. This step might require more detail, e.g.,
if it is a database we might want to stop the datasource
first, to avoid data inconsistencies, but conceptually
this is the step that needs to be taken. Once stopped,
the cluster member can be removed from the cluster
manager and thus will no longer consume energy.
Figure 8: The shutdownClusterMember Plan.
A policy-based mechanism to shut down a clus-
ter member to save energy is just one example how
policies can be used to realize energy-efficient cloud
services. Other use cases can be imaged: If clouds
would have an energy certification, i.e. the data center
is operated on renewable energy, a cloud service could
declare a policy requiring such a certification, and if
not fulfilled the service model is not allowed to be in-
stantiated. Furthermore, policy-based scheduling or
workload planning or other energy-saving, cloud ser-
vice specific measures can be imagined.
6 CONCLUSION
In this paper, we have shown how to design, develop
and run standardized, energy-efficient cloud services.
We extended a TOSCA cloud management platform
with policy handling parts. We showed how to com-
bine TOSCA models with policies (in the policy lan-
guage Ponder2). Thereby we realized description and
execution of portable green cloud services. Ponder2
has been chosen as our policy language because of its
dynamicity and versatility. We believe this work to be
the foundation to combine two of the most important
future markets of the IT industry. To our knowledge
there is no work conducted to combine cloud services
with Green IT aspects so far, so this work to com-
bine cloud services with ecological aspects promises
extraordinary potential for cloud service providers.
With cloud computing in general becoming more and
more a commodity, the offering of energy efficient
services will become a competitive advantage as well
as it will serve the environment.
This work was partially funded by the BMWi
project Migrate! (01ME11055).
REFERENCES
Beloglazov, A., Abawajy, J., and Buyya, R. (2012). Energy-
aware resource allocation heuristics for efficient man-
agement of data centers for cloud computing.
Berl, A., Gelenbe, E., Girolamo, M. D., Giuliani, G., Meer,
H. D., Dang, M. Q., and Pentikousis, K. (2010).
Energy-efficient cloud computing.
Buyya, R., Beloglazov, A., and Abawajy, J. (2010). Energy-
efficient management of data center resources for
cloud computing: A vision, architectural elements,
and open challenges. In Int. Conf. on Parallel and
Distributed Processing Techniques and Applications.
Dargie, W. (2012). Analysis of the power consumption of
a multimedia server under different dvfs policies. In
CLOUD. IEEE.
Duy, T. V. T., Sato, Y., and Inoguchi, Y. (2010). Perfor-
mance evaluation of a green scheduling algorithm for
energy savings in cloud computing. In Parallel & Dis-
tributed Processing, Workshops and Phd Forum.
Hwang, C.-H. and Wu, A. (2000). A predictive system shut-
down method for energy saving of event-driven com-
putation.
IETF (2001). Ietf policy model. http://tools.
ietf.org/html/rfc3060.
Keoh, S. L., Twidle, K., Pryce, N., Schaeffer-Filho, A. E.,
Lupu, E., Dulay, N., Sloman, M., Heeps, S., Strowes,
S., Sventek, J., and Katsiri, E. (2007). Policy-based
management for body-sensor networks. In 4th Int.
Workshop on Wearable and Implantable Body Sensor
Networks. Springer Berlin Heidelberg.
Liu, Z., Lin, M., Wierman, A., Low, S., and Andrew, L.
(2011). Geographical load balancing with renewables.
OASIS (2005). Xacml policy model extensible access con-
trol markup language (xacml) version 2.0, pp. 16-18.
OASIS (2013). Tosca - topology and orchestration specifi-
cation for cloud application.
PONDER (2013). http://www.ponder2.net.
Sueur, E. L. and Heiser, G. (2010). Dynamic voltage and
frequency scaling: The laws of diminishing returns. In
Proc. of the 2010 int. conf. on Power aware computing
and systems. USENIX Association.
Tonti, G., Bradshaw, J. M., Jeffers, R., Montanari, R., Suri,
N., and Uszok, A. (2003). Semantic web languages
for policy representation and reasoning: A compari-
son of kaos, rei, and ponder. In The Semantic Web-
ISWC 2003. Springer Berlin Heidelberg.
Wang, L., von Laszewski, G., Dayal, J., and Wang, F.
(2010). Towards energy aware scheduling for prece-
dence constrained parallel tasks in a cluster with dvfs.
In CCGrid. IEEE/ACM.
CLOSER2014-4thInternationalConferenceonCloudComputingandServicesScience
58