Abstract: An autonomous pile driving system identifies using a pile plan map a target location to install a pile. The system autonomously navigates towards the target location. The system autonomously detects using an object sensor system an orientation and location of the pile located within a threshold distance of the system. The system autonomously positions a tool of the autonomous pile driving system based on the detected orientation and location of the pile. The system autonomously picks up the pile using the positioned tool of the autonomous pile driving system. The system autonomously positions the pile based on the target location. The system autonomously drives the pile into ground at the target location.

Abstract: A method, computer system, and a computer program product are provided for backup and restoration of data. Data is obtained from a plurality of resources during a plurality of tasks. The resources are located in one or more computing networks. A plurality of parameters are extracted from obtained data. A table is generated and stored that includes the parameters extracted and an associated related task. A predict time is calculated based on the table for recovery of data when any of the resources become unavailable. A task priority is established based on calculated predict time and the table to optimize and improve the predict recovery time. A restoration model is generated based on updated restoration recommendation. The restoration model is to be used during resource unavailability or failure of one or more of the resources.

Abstract: A autonomous off-road vehicle (AOV) accesses a pile plan map indicating a plurality of locations in a geographic area at which piles are to be installed. The AOV selects a first location and a second location from the plurality of locations using the pile plan map. The AOV autonomously navigates to the first location. The AOV autonomously loads a first pile onto a driving tool of the AOV. The AOV autonomously drives the first pile into the ground at the first location using the driving tool. The AOV autonomously navigates to the second location. the AOV autonomously loads a second pile onto the driving tool. And the AOV autonomously drives the second pile into the ground at the second location using the driving tool.

Abstract: An autonomous off-road vehicle (AOV) autonomously performs a pile driving operation by driving a pile into the ground at a location identified by a pile plan map. The AOV detects one or more attributes of the pile using one or more sensors during or after the pile driving operation. The AOV determines whether the one or more attributes of the pile exceed respective tolerance thresholds. The AOV performs a quality control action in response to determining that the one or more attributes of the pile exceed the respective tolerance thresholds. The one or more attributes include one or more of a location and an orientation of the pile, and the quality control action is performed in response to determining that the location or the orientation of the pile exceeds the respective tolerance threshold.

Abstract: Some embodiments of the invention provide a method of performing end-user monitoring. At a health monitor that executes on a first host computer along with a client machine and a load balancer, to monitor health of a set of two or more servers that are candidate servers for processing packets from the client machine, the method exchanges health monitoring messages with each server in the set of servers to assess health of the servers in the set. At the health monitor, the method provides health data expressing health of the servers to the load balancer to use in determining how to distribute packets from the client machine between the servers in the set of servers.

Abstract: A pile plan map indicating a plurality of locations in a geographic area at which piles are to be installed is accessed. A first set of locations is identified from the plurality of locations and a first set of piles to be driven into the ground at the first set of locations using the pile plan map is identified. An order for driving the first set of piles into the ground is identified and a pile type for each of the first set of piles is identified. Basket assembly instructions are generated for assembling the first set of piles into a basket based on the identified order and the identified pile types. The first set of piles are assembled autonomously or manually into the basket based on the generated basket assembly instructions.

Abstract: Some embodiments of the invention provide a method of implementing a virtualization software-based service mesh for a network that includes multiple host computers, each host computer including a set of virtualization software executing a set of application instances. For each host computer, the method deploys, to the set of virtualization software, an application service agent and an application service data plane that includes a set of data plane service mesh levels. The method configures the application service agent to apply policy rules defined for flows associated with the set of application instances to the flows on the application service data plane, and configures the application service data plane to forward the flows for the set of application instances to and from services provided at each data plane service mesh level in the set of data plane service mesh levels according to the policy rules applied by the application service agent.

Abstract: An autonomous off-road vehicle (AOV) autonomously performs a pile driving operation by driving a pile into the ground at a location identified by a pile plan map. The AOV detects one or more attributes of the pile using one or more sensors during or after the pile driving operation. The AOV determines whether the one or more attributes of the pile exceed respective tolerance thresholds. The AOV performs a quality control action in response to determining that the one or more attributes of the pile exceed the respective tolerance thresholds. The one or more attributes include one or more of a location and an orientation of the pile, and the quality control action is performed in response to determining that the location or the orientation of the pile exceeds the respective tolerance threshold.

Abstract: A pile plan map indicating a plurality of locations in a geographic area at which piles are to be installed is accessed. A first set of locations is identified from the plurality of locations and a first set of piles to be driven into the ground at the first set of locations using the pile plan map is identified. An order for driving the first set of piles into the ground is identified and a pile type for each of the first set of piles is identified. Basket assembly instructions are generated for assembling the first set of piles into a basket based on the identified order and the identified pile types. The first set of piles are assembled autonomously or manually into the basket based on the generated basket assembly instructions.

Abstract: An autonomous pile driving system identifies using a pile plan map a target location to install a pile. The system autonomously navigates towards the target location. The system autonomously detects using an object sensor system an orientation and location of the pile located within a threshold distance of the system. The system autonomously positions a tool of the autonomous pile driving system based on the detected orientation and location of the pile. The system autonomously picks up the pile using the positioned tool of the autonomous pile driving system. The system autonomously positions the pile based on the target location. The system autonomously drives the pile into ground at the target location.

Abstract: An example method of entitling software in a cloud includes: receiving, from an entitlement agent of the software, an entitlement request at a first entitlement proxy of an entitlement service executing in the cloud; determining, by the entitlement proxy, an entitlement of the software in response to the entitlement request based on an entitlement specification, the entitlement specification provided by an entitlement root of the entitlement service; and sending, by the entitlement proxy, the entitlement to the entitlement agent for application to the software.

Abstract: Data usage by networking and data processing services is measured using a timeslot system. The timeslots have multiple states for collecting, collecting with processing, and expired timeslots. Data from upstream components is reported to local manager clusters and placed into timeslots corresponding to a timestamp of the data. Data can be reported from local managers to an entitlement service and/or a cloud service portal. Timing inconsistencies due to latency or processing time can be resolved by accounting for a timestamp difference using a timestamp difference value between the timeslot time and the reporting time. Data can be deduplicated, cleaned, and/or compacted. Data can be also be version controlled, with timeslots maintaining a version number. Complete and accurate tracking of data usage and associated costs is improved by reporting and collecting usage data using state-based timeslots.

Abstract: An autonomous off-road vehicle (AOV) includes a vehicle body and a vehicle arm coupled to the vehicle body and including a set of prongs and a clamp extending outward from an end of the vehicle arm. A controller causes the vehicle arm to pick up a pile by: aligning the set of prongs with an end of the pile such that a first prong aligns with an opening on a first side of a web of the pile and a second prong aligns with a space on a second side of the web, inserting the set of prongs into the opening on the first side and the space on the second side, and compressing the clamp into an exterior surface of a flange of the pile such that the set of prongs are reciprocally compressed into an interior surface of the flange on either side of the web.

Abstract: An autonomous pile driving system comprises an arm of an autonomous offroad vehicle (AOV) configured to hold and drive a pile, a chute configured to stabilize the pile as the pile is being driven. The autonomous pile driving system further comprises a controller configured to identify a location in the geographic area where a pile is to be driven (e.g., installed) and position the chute at the location. The controller aligns the pile secured by the AOV with an opening of the chute and inserts the pile into the chute by actuating an arm (or other suitable component) of the AOV securing the pile.

Abstract: A processing equipment and a control method thereof are provided. The processing equipment includes a furnace body, a temperature sensor, and a heater unit. A furnace cavity of the furnace body divided into main heating zones and auxiliary heating zones. The heater unit includes main heating elements located in the main heating zones and auxiliary heating elements located in the auxiliary heating zones. The control method includes obtaining an actual temperature detected by the temperature sensor; determining a first heating power according to a first temperature difference and a first preset adjustment rule; determining a second heating power according to the first heating power and a preset adjustment parameter; adjusting an actual working power of the main heating elements to the first heating power, adjusting an actual working power of the auxiliary heating elements to the second heating power.

Abstract: An example method of configuring an entitlement service that manages entitlement of software in a virtualized computing system includes: receiving, at a plug-in of the entitlement service, software component data that specifies a component type and offerings of the component type, the offerings associated with feature sets, the entitlement service executing on a cloud platform in the virtualized computing system; storing, by the plug-in, the software component data in a database; and notifying, by the plug-in, the entitlement service to support entitlement of components of the software having the component type.

Abstract: An example method of distributed load balancing in a virtualized computing system includes: configuring, at a logical load balancer, a traffic detector to detect traffic to a virtual internet protocol address (VIP) of an application having a plurality of instances; detecting, at the traffic detector, a first request to the VIP from a client executing in a virtual machine (VM) supported by a hypervisor executing on a first host; sending, by a configuration distributor of the logical load balancer in response to the detecting, a load balancer configuration to a configuration receiver of a local load balancer executing in the hypervisor for configuring the local load balancer to perform load balancing for the VIP at the hypervisor using the load balancer configuration.

Abstract: In some implementations, the EMV uses a calibration to inform autonomous control over the EMV. To calibrate an EMV, the system first selects a calibration action comprising a control signal for actuating a control surface of the EMV. Then, using a calibration model comprising a machine learning model trained based on one or more previous calibration actions taken by the EMV, the system predicts a response of the control surface to the control signal of the calibration action. After the EMV executes the control signal to perform the calibration action, the EMV system monitors the actual response of the control signal and uses that to update the calibration model based on a comparison between the predicted and monitored states of the control surface.

Abstract: An earth moving vehicle (EMV) autonomously performs an earth moving operation within a dig site. If the EMV determines that a state of the EMV or the dig site triggers a triggering condition associated with a pause in the autonomous behavior of the EMV, the EMV determines a risk associated with the state or triggering condition. If the risk is greater than a first threshold, the EMV continues the autonomous performance and notifies a remote operator that the triggering condition was triggered. If the risk is greater than the first threshold risk but less than a second threshold risk, the EMV is configured to operate in a default state before continuing and notifying the remote operator. If the risk is greater than the second threshold risk, the EMV notifies the remote operator of the state pauses the performance until feedback is received from the remote operator.

Abstract: An autonomous earth moving system can determine a desired state for a portion of the EMV including at least one control surface. Then the EMV selects a set of control signals for moving the portion of the EMV from the current state to the desired state using a machine learning model trained to generate control signals for moving the portion of the EMV to the desired state based on the current state. After the EMV executes the selected set of control signals, the system measures an updated state of the portion of the EMV. In some cases, this updated state of the EMV is used to iteratively update the machine learning model using an online learning process.