5G networks are going to support a variety of vertical services, with a diverse set of key performance indicators (KPIs), by using enabling technologies such as software-defined networking and network function virtualization. It is the responsibility of the network operator to efficiently allocate the available resources to the service requests in such a way to honor KPI requirements, while accounting for the limited quantity of available resources and their cost. A critical challenge is that requests may be highly varying over time, requiring a solution that accounts for their dynamic generation and termination. With this motivation, we seek to make joint decisions for request admission, resource activation, VNF placement, resource allocation, and traffic routing. We do so by considering real-world aspects such as the setup times of virtual machines, with the goal of maximizing the mobile network operator profit. To this end, first, we formulate a one-shot optimization problem which can attain the optimum solution for small size problems given the complete knowledge of arrival and departure times of requests over the entire system lifespan. We then propose an efficient and practical heuristic solution that only requires this knowledge for the next time period and works for realistically-sized scenarios. Finally, we evaluate the performance of these solutions using real-world services and large-scale network topologies. {Results demonstrate that our heuristic solution performs better than a state-of-the-art online approach and close to the optimum.
翻译:5G网络将支持各种纵向服务,利用软件界定的网络和网络功能虚拟化等赋能技术,利用一套不同的关键业绩指标(KPIs)支持各种纵向服务。网络操作员有责任高效率地将现有资源分配给服务请求,以便满足KPI的要求,同时考虑可用资源的有限数量及其成本。一个严峻的挑战是,请求可能随时间而有很大差异,需要找到一个能说明其动态生成和终止的解决方案。我们利用这一动机,寻求对申请的接收、资源激活、VNF定位、资源分配和交通路线进行联合决定。我们这样做的办法是考虑虚拟机器的设置时间等现实世界方面,目标是最大限度地增加移动网络操作者的利润。为此目的,首先,我们提出一个一手优化问题,在完全了解整个系统寿命期间需求抵达和离开时间的情况下,可以找到解决小规模问题的最佳办法。我们然后提出一个高效和实用的超现实的、超现实的解决方案。我们这样做是为了考虑虚拟机器的设置时间,并努力为近似近似的情景。最后,我们用一个比实际规模的在线解决方案来评估我们最高级的解决方案的业绩。