Abstract
Multi-access edge computing (MEC) has been proposed as an approach capable of addressing latency and bandwidth issues in application computation offloading to extend the capabilities beyond the computational and storage limitations of mobile devices. However, there is a critical challenge in MEC to maintain the service continuity between the offloaded user application that is running on the MEC host and the mobile device when a user is moving from radio node to radio node. Furthermore, energy consumption of application computation offloading is an important consideration for MEC service providers in terms of operational costs. Therefore, we formulate the MEC host selection and user application migration problem as a shortest path problem of network energy minimization. We simulate the problem in a hierarchical MEC network deployment environment. We also propose the metric, computational intensity (CI), that can be used by MEC service providers to address the MEC host selection problem. Our results show that with the increment of CI, the selection of MEC hosts tends to move toward level 3 (central deployment) due to energy efficiency and then return to the deployment at level 1 (radio node level) due to latency constraint of the user application. We show that with high accuracy in predicting the user mobility and the available resources in the MEC network, latency- and mobility-aware MEC host selection and user application migration can be pre-calculated to improve response time and energy efficiency.
Highlights
E NHANCED mobile broadband, ultra-reliable and low latency communications (URLLC) and massive machine type communications are expected to be the dominant mobile applications in future, creating a diversity of requirements and challenges for the mobile networks [1]
We model the energy efficiency in Multi-access edge computing (MEC) hosts selection and user application migration problem considering the user mobility and latency requirements, which is currently lacking in literature, as a shortest path problem
computational intensity (CI) is varied to analyze the dynamics of energy efficiency in MEC hosts selection and user application migration problem
Summary
E NHANCED mobile broadband (eMMB), ultra-reliable and low latency communications (URLLC) and massive machine type communications (mMTC) are expected to be the dominant mobile applications in future, creating a diversity of requirements and challenges for the mobile networks [1]. Mobile devices have limited computational capabilities and battery power due to size constraint. These limitations of mobile devices introduce challenges in running emerging mobile applications in an energy-efficient manner. Mobile cloud computing (MCC) has the potential to address the aforementioned challenges by offloading computation-intensive tasks from mobile devices to the cloud servers. MCC can improve the performance of mobile applications and reduce the energy consumption of mobile devices [3]. Long network transmission from the mobile devices to the cloud servers may cause
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