![](bg6.png)
data that are out there, or she requests data services
to deploy the data in the cloud, then these data may
be included in the sought part of the service, since
their deployment (i.e. storage, caching, built of data
structures on top of it) in the cloud is flexible.
The overall framework can be implemented on
service brokers of the open market. The framework
instances can allow communication of the brokers
by creating an overlay network (e.g. with centralized
or Peer-to-Peer coordination), which can propagate
local and received remote advertisements of
services. We envision an open market that could
take a further step down the road of searching and
matching services, by enabling groups of users with
semantically related needs for services to advertise
their request or availability as a team and match
services in a systematic manner.
While we expect search of services to be
performed from top to bottom, meaning search
performed based on the overall summary of complex
hierarchically structured services, matching services
in the framework should be enabled by algorithms
that aims to match the services from bottom to top,
meaning starting from the matching of atoms and
moving to complex services built on top of them.
The goal is to enable dynamic and near-real-time
search and match of services. Therefore, it is
necessary to include methods that take as input
search requests and service advertisements and
schedule their matching in time frames in which the
sought or advertised services are valid. Moreover,
since the matched services may not have the same
durability in time, the methods need to adapt the
scheduling time frame in order to achieve optimal
consumption of services through more accurate
search and match. Such methods enable the
maintenance of multiple dynamic service queues, the
number and size of which depends on the variation
of the durability of the incoming services.
7 CONCLUSIONS
The provisioning of data services is a new paradigm
in data management that represents a very attractive
solution for the management of business and large-
scale data due to its low cost and high performance
capabilities. The industry of cloud computing tries to
catch up with fulfilling these data management
needs but lacks the appropriate technology for the
realization of cloud-hosted database systems, which
is a critical component in the software stack of many
cloud applications. The proposed framework aims to
fill the gap between providers and consumers of data
services that exists in today’s cloud business, by not
only solving the above two issues, but also by
offering an all-inclusive solution for the offer of
efficient and appropriate data services. Such a
solution will enable the successful search and match
of data services via the advertisement of service
need and availability.
REFERENCES
Moore, R., Lopes, J., 1999. Paper templates. In
TEMPLATE’06, 1st International Conference on
Template Production. SCITEPRESS.
Smith, J., 1998. The book, The publishing company.
London, 2
nd
edition.
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz,
R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A.,
Stoica, I., Zaharia, M., 2010. A view of cloud
computing. In CACM, vol. 53, pp. 50--58.
DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G.,
Lakshman, A., Pilchin, A., Sivasubramanian, S.,
Vosshall, P., Vogels, W., 2007. Dynamo: Amazon’s
Highly Available Key-Value Store. In Proc. of ACM
SIGOPS, vol. 41, pp. 205--220.
Chang, F., Dean, J., Sanjay, G., Hsieh, W. C., Wallach, D.
A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.
E., 2006. Bigtable: A Distributed Storage System for
Structured Data. In Proc. of USENIX OSDI, pp. 5--15.
Bernstein, P. A., Cseri, I., Dani, N., Ellis, N., Kalhan, A.,
Kakivaya, G., Lomet, D. B., Manne, R., Novik, L.,
Talius, T., 2011. Adapting Microsoft SQL Server for
Cloud Computing. In Proc. of ICDE, pp. 1255—1263.
Kraska, T., Hentschel, M., Alonso, G., Kossmann, D.
2009. Consistency Rationing in the Cloud: Pay only
when it matters. In Proc. of VLDB, pp. 253--264.
Abadi, D. J., 2009. Data management in the cloud:
Limitations and opportunities. In IEEE Bulletin on
Data Eng., vol. 32, pp. 3—12.
Aboulnaga, A., Salem, K., Soror, A. A., Minhas, U. F.,
2009. Deploying database appliance in the cloud. In
IEEE Bulletin on Data Eng., vol. 32, pp. 13--20.
Paton, N. W., Arago, M. A. T. de, Lee, K.,. Fernandes, A.
A, Sakellariou, R., 2009. Optimizing Utility in Cloud
Computing through Autonomic Workload Execution.
In IEEE Bulletin on Data Eng., vol. 32, pp. 51--58.
Soror, A., Minhas, U. F., Aboulnaga, A., Salem, K.,
Kokosielis, P., Kamath, S., 2008. Automatic virtual
machine configuration for database workloads. In
Proc. of ACM SIGMOD, pp. 953—966.
Shivam, P., Demberel, A., Gunda, P., Irwin, D., Grit, L.,
Yumerefendi, A., Babu, S., Chase, J., 2007.
Automated and On-Demand Provisioning of Virtual
Machines for Database Applications. In Proc. of ACM
SIGMOD, pp.1079--1081.
Weikum, G., Moenkeberg, A., Hasse, C., Zabback, P.,
2002. Self-tuning Database Technology and
Information Services: from Wishful Thinking to
Viable Engineering. In Proc. of VLDB, pp. 20—31.
CLOSER2014-4thInternationalConferenceonCloudComputingandServicesScience
444