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To send or not to send: Reducing the cost of data transmission

机译:发送或不发送:降低数据传输成本

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Frequently, ISPs charge for Internet use not based on peak bandwidth usage, but according to a percentile (often the 95th percentile) cost model. In other words, the time slots with the top 5 percent (in the case of 95th percentile) of data transmission volume do not affect the cost of transmission. Instead, we are charged based on the volume of traffic sent in the 95th percentile slot. In such an environment, by allowing a short delay in transmission of some data, we may be able to reduce our cost considerably. We provide an optimal solution to the offline version of this problem (in which the job arrivals are known), for any delay D>0. The algorithm works for any choice of percentile. We also show that there is no efficient deterministic online algorithm for this problem. However, for a slightly different problem, where the maximum amount of data transmitted is used for cost accounting, we provide an online algorithm with a competitive ratio of 2D+1/D+1. Furthermore, we prove that no online algorithm can achieve a competitive ratio better than 2D+1/D+F(D) where F(D) = ∑D+1i=1 i/D+i for any D>0 in an adversarial setting. We also provide a heuristic that can be used in an online setting where the network traffic has a strong correlation over consecutive accounting cycles, based on the solution to the offline percentile problem. Experimental results are used to illustrate the performance of the algorithms proposed in this work.
机译:通常,ISP对互联网使用的收费不是基于峰值带宽使用情况,而是根据百分比(通常为95%)成本模型。换句话说,具有最高5%(在第95个百分位数的情况下)数据传输量的时隙不会影响传输成本。取而代之的是,我们根据第95个百分位槽中发送的流量进行收费。在这样的环境下,通过允许一些数据传输的短暂延迟,我们可能能够大大降低我们的成本。对于D> 0的任何延迟,我们都提供了针对该问题的离线版本(已知作业到达)的最佳解决方案。该算法适用于任何百分比选择。我们还表明,没有有效的确定性在线算法可以解决此问题。但是,对于一个稍有不同的问题,即使用传输的最大数据量进行成本核算,我们提供了一种竞争率为2D + 1 / D + 1的在线算法。此外,我们证明没有在线算法能够获得优于2D + 1 / D + F(D)的竞争率,其中F(D)= ∑ D + 1 i = 1 0的inf> i / D + i。我们还提供了一种启发式方法,可用于脱机百分比问题解决方案的在线环境中,其中网络流量在连续的计费周期内具有很强的相关性。实验结果用于说明本文提出的算法的性能。

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