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Various quality of service parameters like connection establishment delay, priority parameter, transit delay, throughput, residual error ratio, etc., are critically studied for the network.
When control loops incorporate fieldbus systems, typical quality of service parameters like delay and jitter play an important role with respect to the performance of the control.
The administrator is usually concerned with a more objective perspective based on Quality of Service parameters, such as throughput, delay, and jitter.
Classifying the arriving traffic based on destination core node information and quality of service parameters alleviates the need of performing table look-ups on packet basis.
In this context, an efficient resource management of different networks (a good selection of weights for their quality of service parameters) constitutes an optimization problem, where several heuristic methods using simple rules try to find the best available network.
Rajagopalan et al. [9] take into account quality of service parameters such as residual bandwidth, number of subscribers, duration of calls, frequency of calls and their priority.
Similar(46)
Regrettably, the existing network infrastructures are unable to process requests involving time as a Quality of Service parameter.
The varying characteristics of mHealth applications (particularly different real-time monitoring solutions) in means of the required network resources and Quality of Services parameters invoke elaboration of effective flow-based mobility management algorithms.
Further, software tools for predicting the received signal quality [ 9– 15] very often focus on Quality of Services parameters and do not account for exposure values.
These parameters are input into the linear model, which maps them to the Quality-of-Service (QoS) parameters.
We propose a Markov model to analyze the quality-of-service (QoS) parameters in terms of energy consumption, delay, and throughput.
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