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      <value>traffic management</value>
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      <language>eng</language>
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    <parentTitle language="eng">Proceedings of the 2021 17th International Conference on Network and Service Management (CNSM 2021)</parentTitle>
    <identifier type="issn">2165-963X</identifier>
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    <parentTitle language="eng">2021 IEEE Conference on Network Function Virtualization and Software Defined Networks : Proceedings</parentTitle>
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      <last_name>Hosn</last_name>
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    <parentTitle language="eng">2021 IEEE Conference on Network Function Virtualization and Software Defined Networks : Proceedings</parentTitle>
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      <value>Benchmarking Tool</value>
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      <value>SDN Controller</value>
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      <value>P4Runtime</value>
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    <title language="eng">Modeling and Performance Analysis of P4 Programmable Devices</title>
    <parentTitle language="eng">2021 IEEE Conference on Network Function Virtualization and Software Defined Networks : Proceedings</parentTitle>
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      <value>Programmable Data Plane</value>
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      <value>Performance Modeling</value>
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    <title language="eng">User-centric Markov reward model for state-dependent Erlang loss systems</title>
    <abstract language="eng">Markov reward models are commonly used in the analysis of systems by integrating a reward rate to each system state. Typically, rewards are defined based on system states and reflect the system’s perspective. From a user’s point of view, it is important to consider the changing system conditions and dynamics while the user consumes a service. The key contributions of this paper are proper definitions for (i) system-centric reward and (ii) user-centric reward of the Erlang loss model M/M/n-0 and M/M(x)/n with state-dependent service rates, as well as (iii) the analysis of the relationships between those metrics. Our key result allows a simple computation of the user-centric rewards. The differences between the system-centric and the user-centric rewards are demonstrated for a real-world cloud gaming use case. To the best of our knowledge, this is the first analysis showing the relationship between user-centric rewards and system-centric rewards. This work gives relevant and important insights in how to integrate the user’s perspective in the analysis of Markov reward models and is a blueprint for the analysis of other services beyond cloud gaming while also considering user engagement.</abstract>
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