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首页> 外文会议>Asilomar Conference on Signals, Systems Computers >Exploring the tradeoff between waiting time and service cost in non-asymptotic operating regimes
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Exploring the tradeoff between waiting time and service cost in non-asymptotic operating regimes

机译：探索非渐近运行状态下等待时间与服务成本之间的权衡

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Motivated by the problem of demand management in smart grids, we study the problem of minimizing a weighted-sum of the mean delay of user demands and the power generation cost, where the latter metric increases with both the mean and the variance of the service demand. The state-of-the-art algorithms for this problem are asymptotically optimal, i.e., they are optimal only when the mean delay of user demands increases to infinity or decreases to zero. Yet, these algorithms may perform poorly for moderate delay, which is the regime in which most applications operate. Hence, there is a pressing need for the design of algorithms that can operate efficiently in the moderate delay regime. We attack this challenging problem in a generic framework by first proposing two classes of parameterized algorithms, which include some existing policies as special instances. Then, we obtain the optimal designs by explicitly characterizing the mean delay and the power generation cost as a function of the algorithmic parameters. The proposed algorithms with the optimal parameters not only are asymptotically optimal but also outperform the existing algorithms uniformly for all cases.
机译：基于智能电网中需求管理的问题，我们研究了使用户需求和发电成本的平均延迟的加权和最小化的问题，其中后者的度量随着服务需求的均值和方差的增加而增加。此问题的最新算法是渐近最优的，即，仅当用户需求的平均延迟增加到无穷大或减少到零时，它们才是最佳的。但是，这些算法在适度的延迟下可能效果不佳，这是大多数应用程序运行的方式。因此，迫切需要能够在中等延迟范围内有效运行的算法的设计。通过首先提出两类参数化算法，其中包括一些作为特殊实例的现有策略，我们在通用框架中解决了这一难题。然后，通过将平均延迟和发电成本作为算法参数的函数进行显式表征，从而获得最佳设计。所提出的具有最优参数的算法不仅渐近最优，而且在所有情况下均优于现有算法。

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