Joint computation and communication resource allocation in mobile-edge cloud computing networks

Xiaopeng Lin, Heli Zhang, Hong Ji, Victor C. M. Leung
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引用次数: 17

Abstract

Mobile devices are in general resource-constrained, having limited computation resource and battery life. It is a significant challenge to resolve the contradiction between resource-hungry applications and resource-constrained mobile devices. As a promising approach to address such a challenge, computation offloading enables mobile users to offload computation to nearby resource-rich cloud infrastructure to reduce energy consumption and improve performance. In this paper, we formulate the computation offloading decision making problem as a MEC server selection problem, considering how to find one proper MEC server for one user that carrying computation job. We propose a joint computation and communication resource allocation model. In this model, we consider both transmitting energy consumption and communication delay as communication cost in a multi-user and multi-MEC server scenario. We use two algorithms to solve the proposed model. One is Hungarian algorithm based MEC server selection algorithm, but it ignores the fairness of the system. Another is Fairness based MEC server selection algorithm, which incorporate the fairness of the system. Finally, we show the numerical simulation result.
移动边缘云计算网络中的联合计算与通信资源分配
移动设备通常资源受限,计算资源和电池寿命有限。解决资源饥渴型应用和资源受限型移动设备之间的矛盾是一个重大挑战。作为解决这一挑战的一种很有前途的方法,计算卸载使移动用户能够将计算卸载到附近资源丰富的云基础设施,以降低能耗并提高性能。本文将计算卸载决策问题表述为一个MEC服务器选择问题,考虑如何为一个承载计算任务的用户找到一个合适的MEC服务器。提出了一种联合计算和通信资源分配模型。在该模型中,我们考虑了多用户和多mec服务器场景下的传输能耗和通信延迟作为通信成本。我们使用两种算法来求解所提出的模型。一种是基于匈牙利算法的MEC服务器选择算法,但忽略了系统的公平性。另一种是基于公平性的MEC服务器选择算法,它结合了系统的公平性。最后给出了数值模拟结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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