GMA：一种Pareto最优的分布式资源分配算法
GMA: A Pareto Optimal Distributed Resource-Allocation Algorithm
摘要：
为了解决日益增长的需求，强大的数据包传输保证网络，我们研究了一种新的方法来执行图资源分配。我们首先介绍分配图，其中节点可以独立地设置本地资源限制的基础上的物理约束或政策决定。在这种情况下，我们正式的分布式路径分配（PADIST）问题，其中包括在分配资源的路径只考虑本地的路径上的信息-重要的是，不知道其他路径可以有一个分配-而在同一时间实现的全局属性，永远不会超过可用资源。我们的核心贡献，全球近视分配（GMA）算法，是解决这个问题。我们证明了GMA可以计算图上的所有路径的无条件分配，而永远不会过度分配资源。此外，我们证明了GMA是帕累托最优的分配大小，它有线性复杂度的输入大小。最后，我们通过模拟表明，这一理论结果确实可以应用于实际场景，因为由此产生的路径分配足够大，以满足实际相关应用的要求。
英文摘要：
To address the rising demand for strong packet delivery guarantees in networking, we study a novel way to perform graph resource allocation. We first introduce allocation graphs, in which nodes can independently set local resource limits based on physical constraints or policy decisions. In this scenario we formalize the distributed path-allocation (PAdist) problem, which consists in allocating resources to paths considering only local on-path information -- importantly, not knowing which other paths could have an allocation -- while at the same time achieving the global property of never exceeding available resources. Our core contribution, the global myopic allocation (GMA) algorithm, is a solution to this problem. We prove that GMA can compute unconditional allocations for all paths on a graph, while never over-allocating resources. Further, we prove that GMA is Pareto optimal with respect to the allocation size, and it has linear complexity in the input size. Finally, we show with simulations that this theoretical result could be indeed applied to practical scenarios, as the resulting path allocations are large enough to fit the requirements of practically relevant applications.
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