<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Mobile edge computing (MEC) is one of the key technologies to achieve high bandwidth, low latency and reliable service in fifth generation (5G) networks. In order to better evaluate the performance of the probabilistic offloading strategy in a MEC system, we give a modeling method to capture the stochastic behavior of tasks based on a multi-source fluid queue. Considering multiple mobile devices (MDs) in a MEC system, we build a multi-source fluid queue to model the tasks offloaded to the MEC server. We give an approach to analyze the fluid queue driven by multiple independent heterogeneous finite-state birth-and-death processes (BDPs) and present the cumulative distribution function (CDF) of the edge buffer content. Then, we evaluate the performance measures in terms of the utilization of the MEC server, the expected edge buffer content and the average response time of a task. Finally, we provide numerical results with some analysis to illustrate the feasibility of the stochastic model built in this paper.</p>
</abstract>

Mobile edge computing (MEC) is one of the key technologies to achieve high bandwidth, low latency and reliable service in fifth generation (5G) networks. In order to better evaluate the performance of the probabilistic offloading strategy in a MEC system, we give a modeling method to capture the stochastic behavior of tasks based on a multi-source fluid queue. Considering multiple mobile devices (MDs) in a MEC system, we build a multi-source fluid queue to model the tasks offloaded to the MEC server. We give an approach to analyze the fluid queue driven by multiple independent heterogeneous finite-state birth-and-death processes (BDPs) and present the cumulative distribution function (CDF) of the edge buffer content. Then, we evaluate the performance measures in terms of the utilization of the MEC server, the expected edge buffer content and the average response time of a task. Finally, we provide numerical results with some analysis to illustrate the feasibility of the stochastic model built in this paper.

A Multi–Source Fluid Queue Based Stochastic Model of the Probabilistic Offloading Strategy in a MEC System With Multiple Mobile Devices and a Single MEC Server

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mobile edge computing (MEC) is one of the key technologies to achieve high bandwidth, low latency and reliable service in fifth generation (5G) networks. In...

A Multi–Source Fluid Queue Based Stochastic Model of the Probabilistic Offloading Strategy in a MEC System With Multiple Mobile Devices and a Single MEC Server

Published Online: Mar 31, 2022

Page range: 125 - 138

Received: Aug 02, 2021

Accepted: Dec 28, 2021

DOI: https://doi.org/10.34768/amcs-2022-0010

Keywords
mobile edge computing, probabilistic offloading strategy, multi-source fluid queue, birth-and-death process, cumulative distribution function

© 2022 Huan Zheng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Multi–Source Fluid Queue Based Stochastic Model of the Probabilistic Offloading Strategy in a MEC System With Multiple Mobile Devices and a Single MEC Server

Published Online: Mar 31, 2022

Page range: 125 - 138

Received: Aug 02, 2021

Accepted: Dec 28, 2021

DOI: https://doi.org/10.34768/amcs-2022-0010

Keywordsmobile edge computing, probabilistic offloading strategy, multi-source fluid queue, birth-and-death process, cumulative distribution function

© 2022 Huan Zheng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
mobile edge computing, probabilistic offloading strategy, multi-source fluid queue, birth-and-death process, cumulative distribution function