Abstract
This paper investigates the gain of parallel processing, when mobile edge computing (MEC) is implemented in dense wireless networks. In this scenario users connect to several access points (APs), and utilize the computation capability of multiple servers at the same time. This allows a balanced load at the servers, with the eventual cost of decreased spectrum efficiency. The problem of sum transmission energy minimization under response time constraints is considered and proved to be non-convex. The complexity of optimizing a part of the system parameters is investigated, and based on these results an iterative resource allocation procedure is proposed that converges to a local optimum. The performance of the joint resource allocation is evaluated by comparing it to lower and upper bounds defined by less or more flexible multi-cell MEC architectures. The results show that the free selection of the AP is crucial for achieving decent system performance. The average level of parallel processing is in general low in dense systems, but it is an important option for the rare, highly unbalanced instances.
Highlights
T HE EVOLUTION of mobile information industry is driving the rapid development of various mobile applications in areas such as health care, industrial automation or retail [1]–[3]
In these dense wireless networks, users may reside in the coverage area of several wireless access points (APs), and could utilize the computing resources of more than one mobile edge computing (MEC) server for processing
The objective of the considered MEC system is to minimize the energy consumption for data transmission under the delay constraint, by jointly allocating the data to be sent to the APs, the wireless and the computing resources
Summary
T HE EVOLUTION of mobile information industry is driving the rapid development of various mobile applications in areas such as health care, industrial automation or retail [1]–[3]. In this paper we consider application scenarios where MEC services need to be provided over a limited geographic area, such as a factory floor, a hospital or elderly home or a warehouse, covered by a dense wireless network. In these dense wireless networks, users may reside in the coverage area of several wireless access points (APs), and could utilize the computing resources of more than one MEC server for processing. How to perform an optimized joint resource allocation to balance this tradeoff for dense wireless networks is a challenging and open problem.
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