(An) optimal resource management for energy-efficient mobile augmented reality services in mobile edge computing

Abstract

In this dissertation, we propose an optimal resource management scheme for energy-efficient mobile augmented reality (MAR) services in mobile edge computing (MEC). Through computation offloading to the MEC, we consider an MAR device (MD) that detects an object to extract virtual information, and integrate the virtual information into a real-world environment. Here, the “object” means “physical object” to be detected, and the result of object detection (i.e., object detection information) can be stored in the mobile cache in this proposed work. We assume that the requests of object detection information stored in the mobile cache follow Zipf distribution. The MD can retrieve the previous object detection results if the object detection request is matched with the related result in the cache (i.e., cache hit). For this purpose, depending on whether the cache is hit or not, analytical models for 1) the energy consumption of an MD and 2) the service latency for the MAR are rigorously derived and investigated. Specifically, the MD’s energy consumption and latency come from looking up for the previous object detection result in the cache. Also, if a cache is missed, additional energy consumption and latency incur from transferring a captured image to MEC for obtaining the object detection result as well as processing latency at the MEC. Therefore, the cache size directly affects the energy consumption and latency. The larger cache size, the higher chance of the object detection result in the cache while including the greater lookup energy. Considering a tradeoff between energy and service latency in terms of the cache size in MAR with MEC, we design a theoretical framework for the mobile cache management of the MD to optimize the mobile cache size while guaranteeing the required service latency. From the numerical experiments, we evaluate the performance of the proposed scheme and demonstrate insights regarding the optimization of the performance of MEC-assisted MAR services with a mobile cache. Furthermore, when multiple mobile augmented reality devices utilize mobile edge computing to use mobile augmented reality applications, resources of the mobile edge computing server must be managed on the server side to ensure service quality for each user. Therefore, we propose a resource management scheme for mobile augmented reality applications using a mobile edge computing system on the server side. To this end, a resource optimization scheme is designed to operate on a mobile edge computing system. This will be helpful in servicing MAR applications through optimal resource management in a 5G environment.