straints. These policies generate Service Level Agree-
ments (SLAs) legally binding contracts that sets con-
straints on different QoS metrics.
An example of business process that illustrates
service selection via policy mechanism is shown in
Figure 1. Figure 1 demonstrates run-time system of
the oil reservoir automation control framework.
The oil reservoir automation system consists of a
number of allocated set of sensors embedded to each
oil well equipment that measure various parameters.
Each oil well has one intelligent controller for collec-
tion and transmission of data measured by sensors. In
case of loss of communications with the server, it is
necessary to continuously transfer data using an alter-
native method or, in other words, to guarantee certain
level of reliability.
We introduce following assumptions for the node
in the sensor network and its attributes:
• Each node has the same logic, transmission and
storage of data.
• Each node has the same technical specifications
including processor that can perform data (Out)
and low level computations and, consequently, re-
ceived data packed and queued while waiting their
processing.
• Each node receives data from several sensors and
from neighboring nodes.
• Each node knows how many neighbors (sources)
surrounded it and it is able to locally measure the
number of packets received from neighbors. Each
source has a fair bit of buffer space in a queue.
That is each source has own ”channel” not avail-
able to other sources. Therefore, this infrastruc-
ture is typical example of queuing network.
• The ratio Tp / Ts (passive / sleep timers) deter-
mines the cost of energy and the system response
to the dynamic changes.
Figure 1: Business process modeling of QoS-aware service
system for oil reservoir automation control.
It can be observed especially in the automation of
oil and gas industry while monitoring oil fields via
controlled sensors and maintaining of high quality of
services (QoS) leads to improvement of the oil pro-
duction rate. In other words, the stipulations of unde-
layed data collection and transmission pre-determine
a choice of the best solutions of optimal production
and forecast accuracy.
The control framework can be considered as one
that finds optimal set of ICs in order to provide mini-
mum cost and maximum performance for a desirable
QoS parameters. An adaptation of the system may be
performed through local interactions and, therefore,
the overhead is limited by interaction with neighbor-
ing nodes. This architecture can be scaled up by al-
lowing the deployment of multiple service instances
running on different servers that are devoted for each
intelligent controller.
The choice of appropriate services or the process
of service selection is defined by policies that show
QoS characteristics for the service such as response
time, reliability, availability, and throughput. It is
essential to design and implement admission control
mechanism that will be able to conduct optimal ser-
vice selection in order to introduce service composi-
tion framework supporting QoS. In other words, this
framework should represent QoS-aware management
and adaptation infrastructure that provides essential
service requirements.
Therefore, we suggest that the relationship be-
tween possible service composition and QoS con-
straints will be incorporated into the design of a QoS-
aware sensor network architecture: the additional
complexity providing significant benefit at runtime
through automated policy generation. Section 2 intro-
duce MDE approach for QoS-aware system architec-
ture. Section 3 describes automatic policy generation
for the proposed architecture. Section 4 demonstrates
Case study. Section 5 compares different techniques
that base on policy-aware service composition. Sec-
tion 6 summarizes contribution and results.
2 MODEL-DRIVEN QoS-AWARE
SYSTEM ARCHITECTURE
It is quite often when control of quality of service at-
tributes at run-time is ambitious as there are abundant
calculations needed to prepare data for transmission.
Moreover, the accuracy of data processing affects the
future loads of the distributed system. The main
goal of this paper is to apply Model-Driven QoS-
aware architecture embedded into the network of con-
trollers which is capable to provide minimum delays
while transmitting and processing data and meeting
desirable quality of service requirements. QoS-aware
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