Abstract: A network computer system receives request data from computing devices of requesting users in a sub-region of a service area. The system further receives location data from computing devices of drivers operating in the sub-region. Based on the request data and the location data, the system determines a service condition for the sub-region. Based on the service condition indicating that the sub-region is in a driver oversupply state, the system transmits a service instruction to computing devices of a plurality of drivers within the sub-region, the service instruction being associated with a target outside the sub-region and a set of progress conditions. The system then periodically determines, for each driver of the plurality of drivers, an estimated time of arrival (ETA) to the target from a current position of the driver to determine whether the driver is satisfying the set of progress conditions of the service instruction.

Abstract: A network computer system receives request data from computing devices of requesting users in a sub-region of a service area. The system further receives location data from computing devices of drivers operating in the sub-region. Based on the request data and the location data, the system determines a service condition for the sub-region. Based on the service condition indicating that the sub-region is in a driver oversupply state, the system transmits a service instruction to computing devices of a plurality of drivers within the sub-region, the service instruction being associated with a target outside the sub-region and a set of progress conditions. The system then periodically determines, for each driver of the plurality of drivers, an estimated time of arrival (ETA) to the target from a current position of the driver to determine whether the driver is satisfying the set of progress conditions of the service instruction.

Abstract: A network computer system receives request data from computing devices of requesting users in a sub-region of a service area. The system further receives location data from computing devices of drivers operating in the sub-region. Based on the request data and the location data, the system determines a service condition for the sub-region. Based on the service condition indicating that the sub-region is in a driver oversupply state, the system transmits a service instruction to computing devices of a plurality of drivers within the sub-region, the service instruction being associated with a target outside the sub-region and a set of progress conditions. The system then periodically determines, for each driver of the plurality of drivers, an estimated time of arrival (ETA) to the target from a current position of the driver to determine whether the driver is satisfying the set of progress conditions of the service instruction.

Abstract: A system can provide a set of expedition proposals that are each selectable by a service provider on a user interface of a computing device to commit the service provider to an expedition. Each expedition can be associated with a configured sequence of tasks to be performed for a given amount of time. Based on a selection of an expedition proposal from the set of expedition proposals, the system can initiate a corresponding expedition for the service provider by (i) matching the service provider with a sequence of tasks, and (ii) transmitting route data to the computing device to cause the user interface on the computing device to display route information for sequential destination locations for the sequence of tasks.

Abstract: A network computing system can configure sets of expedition proposals for service providers, which are each selectable to commit the service provider to a dynamic expedition coordinated in real-time by the network computing system. Partitioned service areas may be scored in accordance with utilization conditions, and a dynamic trajectory can be generated based on the scored service areas for individual service providers. The network computing system can provide navigation instructions to the service provider along an updated recommended route based on the dynamic trajectory, until expiration of the dynamic expedition.

Abstract: Service providers can be identified to fulfill service requests of a network-based service. A network system is configured to generate, based on historical data associated with the network-based service, a machine-learned service provider optimization (MLSPO) model for generating service provider optimizations. The optimizations can include action recommendations that optimize one or more service metrics. The MLSPO model can be a reinforcement learning model generated by performing a plurality of simulations utilizing one or more virtual agents. A provider device of a service provider can transmit a set of data to the network system that indicates a current location of the service provider. Based on the current location and the MLSPO model, the network system can generate service provider optimizations. Optimization data can be transmitted to the provider device so that the provider device can display information corresponding to the service provider optimizations.

Abstract: A network computer system receives request data from computing devices of requesting users in a sub-region of a service area. The system further receives location data from computing devices of drivers operating in the sub-region. Based on the request data and the location data, the system determines a service condition for the sub-region. Based on the service condition indicating that the sub-region is in a driver oversupply state, the system transmits a service instruction to computing devices of a plurality of drivers within the sub-region, the service instruction being associated with a target outside the sub-region and a set of progress conditions. The system then periodically determines, for each driver of the plurality of drivers, an estimated time of arrival (ETA) to the target from a current position of the driver to determine whether the driver is satisfying the set of progress conditions of the service instruction.

Abstract: Service providers can be identified to fulfill service requests of a network-based service. A network system is configured to generate, based on historical data associated with the network-based service, a machine-learned service provider optimization (MLSPO) model for generating service provider optimizations. The optimizations can include action recommendations that optimize one or more service metrics. The MLSPO model can be a reinforcement learning model generated by performing a plurality of simulations utilizing one or more virtual agents. A provider device of a service provider can transmit a set of data to the network system that indicates a current location of the service provider. Based on the current location and the MLSPO model, the network system can generate service provider optimizations. Optimization data can be transmitted to the provider device so that the provider device can display information corresponding to the service provider optimizations.

Abstract: A network computer system provides a service instruction to a computing device. The service instruction can include offers, such as a service request to pick up and transport a user, and recommendations, such as a movement recommendation encouraging the service provider to relocate to another geographic area. The network computer system remotely monitors the computing device to receive a current position of the computing device as the service provider travels within a geographic area. The network computer system remotely monitors the computing device to receive a service state of the service provider. The network computer system periodically determines whether the service provider is making progress towards a target of the service instruction based on the current position of the computing device and a set of progress conditions, including determining whether the service provider satisfied the set of progress conditions in response to a change in the service state.

Abstract: An embodiment of the invention includes a method for executing commands in a distributed computing environment. The method receives a plurality of distributed commands from one or more devices. The method determines a global command execution order for executing the received plurality of distributed commands. The method dispatches the received plurality of distributed commands to a plurality of servers hosting a plurality of corresponding shards in the distributed computing environment, where a given distributed command corresponds to one or more of a given shard if the given distributed command pertains to a state hosted by the one or more given shard. The method executes, by the one or more given shard, the given distributed command, where the execution is deterministic, and where a result of deterministic execution of the given distributed command is unanimous among the one or more given shard.

Abstract: A network computer system provides a service instruction to a computing device. The service instruction can include offers, such as a service request to pick up and transport a user, and recommendations, such as a movement recommendation encouraging the service provider to relocate to another geographic area. The network computer system remotely monitors the computing device to receive a current position of the computing device as the service provider travels within a geographic area. The network computer system remotely monitors the computing device to receive a service state of the service provider. The network computer system periodically determines whether the service provider is making progress towards a target of the service instruction based on the current position of the computing device and a set of progress conditions, including determining whether the service provider satisfied the set of progress conditions in response to a change in the service state.

Abstract: Service providers can be identified to fulfill service requests of a network-based service. A network system is configured to generate, based on historical data associated with the network-based service, a machine-learned service provider optimization (MLSPO) model for generating service provider optimizations. The optimizations can include action recommendations that optimize one or more service metrics. The MLSPO model can be a reinforcement learning model generated by performing a plurality of simulations utilizing one or more virtual agents. A provider device of a service provider can transmit a set of data to the network system that indicates a current location of the service provider. Based on the current location and the MLSPO model, the network system can generate service provider optimizations. Optimization data can be transmitted to the provider device so that the provider device can display information corresponding to the service provider optimizations.

Abstract: A network computer system provides a service instruction to a computing device. The service instruction can include offers, such as a service request to pick up and transport a user, and recommendations, such as a movement recommendation encouraging the service provider to relocate to another geographic area. The network computer system remotely monitors the computing device to receive a current position of the computing device as the service provider travels within a geographic area. The network computer system remotely monitors the computing device to receive a service state of the service provider. The network computer system periodically determines whether the service provider is making progress towards a target of the service instruction based on the current position of the computing device and a set of progress conditions, including determining whether the service provider satisfied the set of progress conditions in response to a change in the service state.

Abstract: A network computer system operates to monitor a plurality of requester devices to detect activities of requesters, and activities of transportation providers. Based on the monitored activities, the system forecasts a number of requesters that may be present in each of multiple subregions of a given geographic region, during an upcoming time interval. The system further estimates a target number of transportation providers to have available for requesters in each of the subregions. The system determines a supplemental value set for crediting transportation providers, in connection with each individual transport provider performing one or more activities that make the transport provider available to one or more of the multiple subregions during the upcoming time interval.

Abstract: A network computer system provides a service instruction to a computing device. The service instruction can include offers, such as a service request to pick up and transport a user, and recommendations, such as a movement recommendation encouraging the service provider to relocate to another geographic area. The network computer system remotely monitors the computing device to receive a current position of the computing device as the service provider travels within a geographic area. The network computer system remotely monitors the computing device to receive a service state of the service provider. The network computer system periodically determines whether the service provider is making progress towards a target of the service instruction based on the current position of the computing device and a set of progress conditions, including determining whether the service provider satisfied the set of progress conditions in response to a change in the service state.

Abstract: A network computing system can configure sets of expedition proposals for service providers, which are each selectable to commit the service provider to a dynamic expedition coordinated in real-time by the network computing system. Partitioned service areas may be scored in accordance with utilization conditions, and a dynamic trajectory can be generated based on the scored service areas for individual service providers. The network computing system can provide navigation instructions to the service provider along an updated recommended route based on the dynamic trajectory, until expiration of the dynamic expedition.

Abstract: In various embodiments a distributed computing node in a plurality of distributed computing nodes executes transactions in a distributed processing system. In one embodiment, a transaction commit message is received from a client computing node for a transaction. The transaction commit message includes at least an identifier of the transaction and a transaction sequence for the transaction. The transaction sequence indicates a sequence of execution for the transaction on the plurality of distributed computing nodes. An entry within the transaction sequence associated with the distributed computing node is identified. The entry includes a sequence number for executing the transaction on the distributed computing node with respect to other transactions. The transaction is executed based on the sequence number in the entry.

Abstract: In various embodiments a distributed computing node in a plurality of distributed computing nodes logs transactions in a distributed processing system. In one embodiment, a set of information associated with at least one transaction is recorded in a transaction log. At least a portion of memory in at least one information processing system involved in the transaction is accessed. The portion of memory is directly accessed without involving a processor of the at least one information processing system. The set of information from the transaction log is written to the portion of memory. The set of information is directly written to the portion of memory without involving a processor of the at least one information processing system.

Abstract: A method to share remote DMA (RDMA) pointers to a key-value store among a plurality of clients. The method allocates a shared memory and accesses the key-value store with a key from a client and receives an information from the key-value store. The method further generates a RDMA pointer from the information, maps the key to a location in the shared memory, and generates a RDMA pointer record at the location. The method further stores the RDMA pointer and the key in the RDMA pointer record and shares the RDMA pointer record among the plurality of clients.

Abstract: An embodiment of the invention includes a method for executing commands in a distributed computing environment. The method receives a plurality of distributed commands from one or more devices. The method determines a global command execution order for executing the received plurality of distributed commands. The method dispatches the received plurality of distributed commands to a plurality of servers hosting a plurality of corresponding shards in the distributed computing environment, where a given distributed command corresponds to one or more of a given shard if the given distributed command pertains to a state hosted by the one or more given shard. The method executes, by the one or more given shard, the given distributed command, where the execution is deterministic, and where a result of deterministic execution of the given distributed command is unanimous among the one or more given shard.