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公开(公告)号：US20130117454A1
公开(公告)日：2013-05-09
申请号：US13348465
申请日：2012-01-11
IPC分类号： G06F15/173
CPC分类号： G06F9/5011 , G06Q10/0631 , H04L29/06 , H04L29/08144 , H04L29/08153
摘要： Various embodiments provide offline algorithms for resource allocation. A known set of “offline” requests may be matched to available resources using an online resource allocation algorithm that models the offline resource allocation problem as though the requests were received stochastically. Requests may be scaled and then sampled to provide random, stochastic input for the online resource allocation algorithm. For each request, resources are allocated to the request by evaluating multiple options based upon shadow costs assigned to resources associated with the different options. After each request is processed, an adjustment is made to the shadow costs for remaining resources to reflect differences in rates for allocation and/or consumption of the resources and the updated shadow costs are used for a subsequent request. A scaled resource allocation determined using sampled requests in this manner may be scaled back up to obtain a solution for the offline resource allocation problem.
摘要翻译： 各种实施例提供用于资源分配的离线算法。 可以使用在线资源分配算法将已知的一组“离线”请求与可用资源相匹配，所述在线资源分配算法对离线资源分配问题进行建模，就像随机地接收请求一样。 可以对请求进行缩放，然后对其进行采样，以提供在线资源分配算法的随机输入。 对于每个请求，通过根据分配给与不同选项相关联的资源的影子成本评估多个选项，将资源分配给请求。 在处理每个请求之后，对剩余资源的影子成本进行调整，以反映资源的分配和/或消耗率的差异，并且更新的影子成本用于后续请求。 以这种方式使用采样请求确定的缩放资源分配可以缩小以获得用于脱机资源分配问题的解决方案。
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公开(公告)号：US09009318B2
公开(公告)日：2015-04-14
申请号：US13348465
申请日：2012-01-11
IPC分类号： G06F15/173 , G06F9/50 , H04L29/08 , G06Q10/06 , H04L29/06
CPC分类号： G06F9/5011 , G06Q10/0631 , H04L29/06 , H04L29/08144 , H04L29/08153
摘要： Various embodiments provide offline algorithms for resource allocation. A known set of “offline” requests may be matched to available resources using an online resource allocation algorithm that models the offline resource allocation problem as though the requests were received stochastically. Requests may be scaled and then sampled to provide random, stochastic input for the online resource allocation algorithm. For each request, resources are allocated to the request by evaluating multiple options based upon shadow costs assigned to resources associated with the different options. After each request is processed, an adjustment is made to the shadow costs for remaining resources to reflect differences in rates for allocation and/or consumption of the resources and the updated shadow costs are used for a subsequent request. A scaled resource allocation determined using sampled requests in this manner may be scaled back up to obtain a solution for the offline resource allocation problem.
摘要翻译： 各种实施例提供用于资源分配的离线算法。 可以使用在线资源分配算法将已知的一组“离线”请求与可用资源相匹配，所述在线资源分配算法对离线资源分配问题进行建模，就像随机地接收请求一样。 可以对请求进行缩放，然后对其进行采样，以提供在线资源分配算法的随机输入。 对于每个请求，通过根据分配给与不同选项相关联的资源的影子成本评估多个选项，将资源分配给请求。 在处理每个请求之后，对剩余资源的影子成本进行调整，以反映资源的分配和/或消耗率的差异，并且更新的影子成本用于后续请求。 以这种方式使用采样请求确定的缩放资源分配可以缩小以获得用于脱机资源分配问题的解决方案。
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公开(公告)号：US20130117062A1
公开(公告)日：2013-05-09
申请号：US13288650
申请日：2011-11-03
IPC分类号： G06Q10/06
CPC分类号： G06Q10/06
摘要： Various embodiments provide online algorithms for resource allocation. In one or more embodiments, requests for resources from a service provider are received stochastically. For each request, different options for satisfying the request are evaluated based in part upon shadow costs (e.g., unit costs) that are assigned to resources associated with the different options. One of the options may be selected by optimizing an objective function that accounts for the shadow costs. Resources for the selected option are allocated to the request and an adjustment is made to the shadow costs for remaining resources to reflect differences in rates for allocation and/or consumption of the resources. Thereafter, resources may be allocated to a subsequent request using the updated shadow costs and the costs are adjusted again. By updating shadow costs iteratively in this manner, an increasingly more accurate analysis of the objective function is achieved.
摘要翻译： 各种实施例提供用于资源分配的在线算法。 在一个或多个实施例中，随机地接收来自服务提供商的资源请求。 对于每个请求，满足请求的不同选项将部分地基于分配给与不同选项相关联的资源的影子成本（例如，单位成本）进行评估。 可以通过优化考虑影子成本的目标函数来选择其中一个选项。 所选选项的资源分配给请求，并对剩余资源的影子成本进行调整，以反映资源分配和/或消耗率的差异。 此后，可以使用更新的影子成本将资源分配给后续请求，并且再次调整成本。 通过以这种方式迭代地更新影子成本，实现了对目标函数的越来越准确的分析。
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