On the modeling and optimization of short-term performance for real-time wireless networks
On the modeling and optimization of short-term performance for real-time wireless networks
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实时无线网络短期性能建模与优化
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发表时间：
2016
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通讯作者：
I.
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作者：
I.
本文研究由多个实时流组成的无线网络，这些实时流对所有数据包施加硬延迟限制。与当前大多数关注长期平均及时交付率的研究相比，我们的目标是对每个实时流的短期性能进行建模和优化，这对于大多数安全关键型应用至关重要。我们建议通过过去短窗口内的移动平均线来定义流量的瞬时性能。如果每个流的移动平均值低于某个指定的要求，就会产生一些惩罚，我们的目标是最小化系统的总体惩罚。我们通过布朗运动来近似系统，并制定一个优化问题来最小化总体惩罚。虽然优化问题不是凸的，但我们建立了一种低复杂度算法，通过利用实时无线网络的固有结构来优化解决该问题。我们还提出了一种简单的在线数据包调度策略，并证明它实现了最小的总体惩罚。模拟结果表明，布朗近似在捕捉短期绩效方面是准确的，并且我们的政策取得了比其他政策更好的绩效。此外，他们还证明，具有最佳长期平均交付率的政策实际上可能短期表现不佳。
This paper studies wireless networks consisting of multiple real-time flows that impose hard delay bounds for all packets. In contrast to most current studies that focus on the long-term average rate of timely deliveries, we aim to model and optimize the short-term performance of each real-time flow, which is vital for most safety-critical applications. We propose to define the instantaneous performance of a flow by a moving average within a short window in the past. Each flow incurs some penalty if its moving average is below some specified requirement, and we aim to minimize the overall penalty of the system. We approximate the system by Brownian motions, and formulate an optimization problem for minimizing the overall penalty. While the optimization problem is not convex, we establish a low-complexity algorithm for optimally solving it by leveraging inherent structures of real-time wireless networks. We also propose a simple online packet scheduling policy and prove that it achieves the minimum overall penalty. Simulation results show that Brownian approximations are accurate in capturing short-term performance, and our policies achieve much better performance than other policies. Moreover, they also demonstrate that policies with optimal long-term average delivery rates can actually have poor short-term performance.