Reconfiguration in Network Slicing—Optimizing the Profit and Performance
Reconfiguration in Network Slicing—Optimizing the Profit and Performance
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DOI：
10.1109/tnsm.2019.2899609
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发表时间：
2019-02
影响因子：
5.3
通讯作者：
Gang Wang;G. Feng;Tony Q. S. Quek;Shuang Qin;Ruihan Wen;W. Tan
中科院分区：
文献类型：
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作者：
Gang Wang;G. Feng;Tony Q. S. Quek;Shuang Qin;Ruihan Wen;W. Tan
网络切片使多样化的服务能够由网络功能虚拟化的软件定义网络中的隔离切片来容纳。为了在动态环境中保持令人满意的用户体验和服务提供商的高利润，可能需要根据变化的业务需求和资源可用性来重新配置切片。然而，频繁的重新配置会产生一定的成本，并可能导致服务中断。在本文中，我们提出了一个混合切片重新配置（HSR）框架，其中快速切片重新配置（FSR）计划重新配置流的到达/离开的时间尺度上的各个切片，而尺寸切片与重新配置（DSR）计划偶尔进行调整分配的资源，根据随时间变化的流量需求。为了优化切片的利润，即，总效用减去资源消耗和重构成本，我们制定了FSR和DSR的问题，这是很难解决的，由于不连续和非凸的重构成本函数。因此，我们近似的重构成本函数与${L} _{1}$范数，这保持了稀疏的解决方案，从而有利于限制重构。此外，本文还设计了一种调度FSR和DSR的算法，使DSR能够根据业务动态和资源可用性及时触发，提高切片的收益。此外，我们扩展HSR的资源预留机制，保留部分资源，为不久的将来的流量，以减少潜在的重新配置。数值结果验证了该重构框架在降低重构开销和实现切片高利润方面的有效性。
Network slicing enables diversified services to be accommodated by isolated slices in network function virtualization-enabled software-defined networks. To maintain satisfactory user experience and high profit for service providers in a dynamic environment, a slice may need to be reconfigured according to the varying traffic demand and resource availability. However, frequent reconfigurations incur certain cost and might cause service interruption. In this paper, we propose a hybrid slice reconfiguration (HSR) framework, where a fast slice reconfiguration (FSR) scheme reconfigures flows for individual slices at the time scale of flow arrival/departure, while a dimensioning slices with reconfiguration (DSR) scheme is occasionally performed to adjust allocated resources according to the time-varying traffic demand. In order to optimize the slice’s profit, i.e., the total utility minus the resource consumption and reconfiguration cost, we formulate the problems for FSR and DSR, which are difficult to solve due to the discontinuity and non-convexity of the reconfiguration cost function. Hence, we approximate the reconfiguration cost function with ${L} _{1}$ norm, which preserves the sparsity of the solution, thus facilitating restricting reconfigurations. Besides, we design an algorithm to schedule FSR and DSR, so that DSR is timely triggered according to the traffic dynamics and resource availability to improve the profit of slice. Furthermore, we extend HSR with a resource reservation mechanism, which reserves partial resources for near future traffic to reduce potential reconfigurations. Numerical results validate that our reconfiguration framework is effective in reducing reconfiguration overhead and achieving high profit for slices.