Abstract: The techniques disclosed herein provision inter-DC WAN capacity based on network failure statistics and bandwidth demands of a cloud-hosted application. Network capacity is provisioned based on an assumption of runtime cooperation between the application and the network. For example, if the network detects that a link has failed, the application may cooperate with the network to pause a deferrable transfer, reserving bandwidth for non-deferrable transfers. With knowledge that deferrable transfers will be dynamically paused when a primary link fails, backup links may be provisioned with less capacity than the primary link. The ability to dynamically defer transfers also enables a greater degree of bandwidth smoothing, e.g. reducing peak demand by scheduling deferrable transfers for off-peak hours. This allows network links to be provisioned with less capacity than if all transfers were performed immediately.

Abstract: The techniques disclosed herein provision inter-DC WAN capacity based on network failure statistics and bandwidth demands of a cloud-hosted application. Network capacity is provisioned based on an assumption of runtime cooperation between the application and the network. For example, if the network detects that a link has failed, the application may cooperate with the network to pause a deferrable transfer, reserving bandwidth for non-deferrable transfers. With knowledge that deferrable transfers will be dynamically paused when a primary link fails, backup links may be provisioned with less capacity than the primary link. The ability to dynamically defer transfers also enables a greater degree of bandwidth smoothing, e.g. reducing peak demand by scheduling deferrable transfers for off-peak hours. This allows network links to be provisioned with less capacity than if all transfers were performed immediately.

Abstract: The systems and methods may analyze an impact of latency on user activities by leveraging the variation of latency seen in the normal course of user activities with an application. The systems and methods may infer the latency sensitivity of users by comparing a biased latency distribution of user actions to an estimate of the underlying unbiased latency distribution. The systems and methods may compute a normalized latency preference of the users using a biased latency probability distribution function and an unbiased latency probability distribution function. The systems and method may use the normalized latency preference to analyze an impact of latency on the user activities.