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Optimal Resource Procurement and the Price of Causality

机译：最佳资源采购和因果关系的价格

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In this article, we study the problem of procuring diverse resources in a forward market to cover a set E of uncertain demand signals e. We consider two scenarios: 1) e is revealed all at once by an oracle and 2) e reveals itself causally. Each scenario induces an optimal procurement cost. The ratio between these two costs is defined as the price of causality. It captures the additional cost of not knowing the future values of the uncertain demand signal. We consider two application contexts: Procuring energy reserves from a forward capacity market, and purchasing virtual machine instances from a cloud service. An upper bound on the price of causality is obtained, and the exact price of causality is computed for some special cases. The algorithmic basis for all these computations is set containment linear programming. A mechanism is proposed to allocate the procurement cost to consumers who in aggregate produce the demand signal. We show that the proposed cost allocation is fair, budget-balanced, and respects the cost causation principle. The results are validated through numerical simulations.
机译：在本文中，我们研究了在向前市场中采购各种资源的问题，以覆盖不确定需求信号e的集合E.我们考虑两种情况：1）e由Oracle和2）揭示所有的概念，2）e会因果揭示。每种情况都会引起最佳采购成本。这两种成本之间的比率被定义为因果关系的价格。它捕获不知道不确定需求信号的未来值的额外成本。我们考虑两个应用程序背景：从前行能力市场采购能源储备，并从云服务购买虚拟机实例。获得了因果关系价格上的上限，为某些特殊情况计算了因果关系的确切价格。所有这些计算的算法基础是设定容纳线性编程。提出了一种机制来分配给总产量的消费者的采购成本产生需求信号。我们表明，拟议的成本分配是公平的，预算平衡，尊重成本原则。结果通过数值模拟验证。

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