Abstract
Virtualized small cell networks (SCNs) integrated with mobile edge computing (MEC) is a promising paradigm to provide both wideband access and intensive computation economically for user equipments (UEs) in the scenario of multiple mobile virtual network operators and infrastructure providers. However, the model of offloading in this case is often of high complexity and lacks effective solution. In this paper, by jointly considering offloading, time slice and power allocation, we formulate the energy consumption reduction of the UEs in virtualized SCNs with MEC as a mixed integer nonlinear programming. Our aim is to minimize the total energy consumption of the UEs subject to minimum overall throughput of the network. To solve the problem efficiently, we convert it into a biconvex problem by adding auxiliary variable, which enables the derivation of an efficient iterative algorithm by two subproblems. Towards the first subproblem, we introduce local variables to handle the coupling constraint, and propose an alternating direction method of multipliers (ADMM)-based distributed algorithm, where the closed-form expressions of the optimal solutions in variables updating are derived. For the second subproblem, the closed-form expressions of the optimal solution is also derived. Finally, the effectiveness of the proposed algorithm is demonstrated by extensive simulations with different system configurations.
Highlights
W ITH the vigorous development of mobile Internet and Internet of Things [1], many innovative wireless data services are emerging, such as automatic driving, virtualManuscript received January 15, 2019; revised July 25, 2019; accepted October 17, 2019
The results are divided into three parts: (i) We compare the proposed algorithm with several benchmark algorithms in terms of energy consumption per bit, which is defined as the ratio of overall energy consumption of all user equipments (UEs) and data amount of all tasks
The impacts of the number of mobile edge computing (MEC) servers and UEs on the evaluated algorithms are studied and analyzed. (ii) The convergence of the proposed algorithm is studied, in which the impacts of penalty factor ρ are investigated. (iii) The complexity of the proposed algorithm is evaluated in term of running time on the simulation platform
Summary
W ITH the vigorous development of mobile Internet and Internet of Things [1], many innovative wireless data services are emerging, such as automatic driving, virtualManuscript received January 15, 2019; revised July 25, 2019; accepted October 17, 2019. Date of publication October 24, 2019; date of current version March 18, 2020. The mobile intelligent terminal gradually replaces the personal computer and becomes the main tool in everyday life. These trends pose great challenges to the conventional mobile cellular networks and cloud computing mode [3]. The cell partition and spectrum usage of conventional cellular cannot satisfy the access of large number of mobile intelligent terminals. Some services with high real-time requirements are very sensitive to latency, while the traditional cloud computing mode requires that the tasks be transferred to the cloud computing center. Since the center often locates in the core network, the returning of the computation results leads to large latency
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