Abstract: The disclosed embodiments provide a system for forecasting job applications. During operation, the system applies a first machine learning model to features that include a budget for a job to predict a number of impressions of the job. Next, the system applies a second machine learning model to additional features for the job to estimate an application rate for the job. The system then determines a distribution of applications to the job based on the number of impressions and the application rate. Finally, the system outputs one or more values from the distribution of applications to the job as guidance for setting the budget for the job.

Abstract: The disclosed embodiments provide a system for performing dynamic job bidding optimization. During operation, the system obtains historical data containing a time series of interactions with a job. Next, the system uses the historical data to calculate an initial price of a job based on a predicted number of interactions with the job. The system then determines a first dynamic adjustment to the initial price that improves utilization of a budget for the job and a second dynamic adjustment to the initial price that improves a performance of the job. Finally, the system applies the first and second adjustments to the initial price to produce an updated price for the job and delivers the job within an online system based on the updated price.

Abstract: The disclosed embodiments provide a system for predicting successful outcomes. During operation, the system determines interaction features characterizing interaction between a moderator of a job and one or more applicants for the job. Next, the system applies a machine learning model to the interaction features to produce a score representing a likelihood of a positive outcome for the job. The system then applies a threshold to the score to generate a predicted outcome for the job. Finally, the system outputs the predicted outcome in association with the job.

Abstract: The disclosed embodiments provide a system for performing an experiment strategy for member-based ramping. During operation, the system divides members of an online system into a control group and a treatment group. Next, the system configures delivery of content to the control group over a first set of channels that charge for actions related to the content and delivery of the content to the treatment group over a second set of channels that charge for the actions related to the content, wherein the second set of channels is smaller than the first set of channels. The system then performs a first experiment that selects an adjustment factor for content delivered over the second set of channels to the treatment group to achieve revenue neutrality and/or engagement neutrality between the treatment and control groups.

Abstract: The disclosed embodiments provide a system for forecasting job applications. During operation, the system applies a first machine learning model to features that include a budget for a job to predict a number of impressions of the job. Next, the system applies a second machine learning model to additional features for the job to estimate an application rate for the job. The system then determines a distribution of applications to the job based on the number of impressions and the application rate. Finally, the system outputs one or more values from the distribution of applications to the job as guidance for setting the budget for the job.

Abstract: The disclosed embodiments provide a system for performing dynamic job bidding optimization. During operation, the system obtains historical data containing a time series of interactions with a job. Next, the system uses the historical data to calculate an initial price of a job based on a predicted number of interactions with the job. The system then determines a first dynamic adjustment to the initial price that improves utilization of a budget for the job and a second dynamic adjustment to the initial price that improves a performance of the job. Finally, the system applies the first and second adjustments to the initial price to produce an updated price for the job and delivers the job within an online system based on the updated price.

Abstract: A method for migrating a virtual machine executing on a host. The method involves monitoring, by a monitoring agent connected to a device driver, hosts in a network, wherein the device driver is connected to a network interface card, determining a virtual machine to be migrated based on a virtual machine policy, sending, by the host, a request to migrate to at least one of a plurality of target hosts in the network, receiving an acceptance to the request to migrate from at least one of the plurality of target hosts, determining, by the monitoring agent, a chosen target host to receive the virtual machine based on a migration policy, wherein the chosen target host is one of the at least one target hosts that sent the acceptance, sending a confirmation and historical information to the chosen target host, and migrating the virtual machine to the chosen target host.

Abstract: A method for migrating a virtual machine executing on a host. The method involves monitoring, by a monitoring agent connected to a device driver, hosts in a network, wherein the device driver is connected to a network interface card, determining a virtual machine to be migrated based on a virtual machine policy, sending, by the host, a request to migrate to at least one of a plurality of target hosts in the network, receiving an acceptance to the request to migrate from at least one of the plurality of target hosts, determining, by the monitoring agent, a chosen target host to receive the virtual machine based on a migration policy, wherein the chosen target host is one of the at least one target hosts that sent the acceptance, sending a confirmation and historical information to the chosen target host, and migrating the virtual machine to the chosen target host.