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In general, the deployment configuration is selected manually based on expert knowledge.
Compared with traditional wireless networks, they are more efficient in terms of deployment, configuration, and maintenance.
A deployment configuration may be executed a number of times.
It can be represented through different views: structural, behavioral, deployment, configuration, etc.
We assume an arbitrary deployment configuration in which an SN can monitor multiple target points.
It supports deployment, configuration and monitoring of content and cloud resources using Web-based widgets.
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The approach is based on model-driven principles and uses both UML and Bayesian Networks to capture, analyse and optimise cloud deployment configurations.
Probability and optimization techniques are used to quantify and evaluate the risk and cost-effectiveness of various device deployment configurations, which are captured by using a system life-cycle cost model.
An enhanced understanding of the pathophysiologic role of ESS in restenosis and thrombosis might dictate hemodynamically favorable stent designs and deployment configurations to reduce the potential for late lumen loss and thrombotic obstruction.
Probability and optimization techniques are used to quantify and evaluate the cost- and risk-effectiveness of various deployment configurations, which is captured by using a system life-cycle cost model that incorporates the deployment cost, operating cost, and costs associated with system decisions.
Next, Section 4.5 discusses the different deployment configurations in which we have executed these benchmarks.
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