Abstract: A computer implemented method includes receiving telemetry data corresponding to capacity health of nodes in a cloud based computing system. The received telemetry data is processed via a prediction engine to provide predictions of capacity health at multiple dimensions of the cloud based computing system. Node recoverability information is received and node recovery execution is initiated as a function of the representations of capacity health and node recoverability information.

Abstract: Various techniques for deferred server recovery are disclosed herein. In one embodiment, a method includes receiving a notification of a fault from a host in the computing system. The host is performing one or more computing tasks for one or more users. The method can then include determining whether recovery of the fault in the received notification is deferrable on the host. In response to determining that the fault in the received notification is deferrable, the method includes setting a time delay to perform a pending recovery operation on the host at a later time and disallowing additional assignment of computing tasks to the host.

Abstract: A method for opportunistically performing an action in a cloud computing system may include detecting a reboot event corresponding to a computing entity in the cloud computing system. The computing entity may be, for example, a host machine in the cloud computing system or a virtual machine in the cloud computing system. The method may also include causing the computing entity to be held in a stopped state and performing the action while the computing entity is being held in the stopped state, thereby eliminating a need to perform the action at a future time subsequent to the reboot event. The nature of the action is such that it would affect the computing entity if the action were performed subsequent to the reboot event. The method may also include causing the computing entity to be started after the action has been performed.

Abstract: Methods, systems, and computer program products are described herein for minimizing the downtime for nodes in a network-accessible server set. The downtime may be minimized by determining an optimal timeout value for which a fabric controller waits to perform a recovery action. The optimal timeout value may be determined for each cluster in the network-accessible server set. The optimal timeout value advantageously reduces the overall downtime for customer workloads running on a node for which contact has been lost. The optimal timeout value for each cluster may be based on a predictive model based on the observed historical patterns of the nodes within that cluster. In the event that an optimal timeout value is not determined for a particular cluster (e.g., due to a lack of observed historical patterns), the fabric controller may fall back to a less than optimal timeout value.

Abstract: Embodiments described herein are directed to migrating affected services away from a faulted cloud node and to handling faults during an upgrade. In one scenario, a computer system determines that virtual machines running on a first cloud node are in a faulted state. The computer system determines which cloud resources on the first cloud node were allocated to the faulted virtual machine, allocates the determined cloud resources of the first cloud node to a second, different cloud node and re-instantiates the faulted virtual machine on the second, different cloud node using the allocated cloud resources.

Abstract: Methods, systems, and computer program products are described herein for minimizing the downtime for nodes in a network-accessible server set. The downtime may be minimized by determining an optimal timeout value for which a fabric controller waits to perform a recovery action. The optimal timeout value may be determined for each cluster in the network-accessible server set. The optimal timeout value advantageously reduces the overall downtime for customer workloads running on a node for which contact has been lost. The optimal timeout value for each cluster may be based on a predictive model based on the observed historical patterns of the nodes within that cluster. In the event that an optimal timeout value is not determined for a particular cluster (e.g., due to a lack of observed historical patterns), the fabric controller may fall back to a less than optimal timeout value.

Abstract: Various techniques for deferred server recovery are disclosed herein. In one embodiment, a method includes receiving a notification of a fault from a host in the computing system. The host is performing one or more computing tasks for one or more users. The method can then include determining whether recovery of the fault in the received notification is deferrable on the host. In response to determining that the fault in the received notification is deferrable, the method includes setting a time delay to perform a pending recovery operation on the host at a later time and disallowing additional assignment of computing tasks to the host.

Abstract: Various techniques for deferred server recovery are disclosed herein. In one embodiment, a method includes receiving a notification of a fault from a host in the computing system. The host is performing one or more computing tasks for one or more users. The method can then include determining whether recovery of the fault in the received notification is deferrable on the host. In response to determining that the fault in the received notification is deferrable, the method includes setting a time delay to perform a pending recovery operation on the host at a later time and disallowing additional assignment of computing tasks to the host.

Abstract: Embodiments described herein are directed to migrating affected services away from a faulted cloud node and to handling faults during an upgrade. In one scenario, a computer system determines that virtual machines running on a first cloud node are in a faulted state. The computer system determines which cloud resources on the first cloud node were allocated to the faulted virtual machine, allocates the determined cloud resources of the first cloud node to a second, different cloud node and re-instantiates the faulted virtual machine on the second, different cloud node using the allocated cloud resources.

Abstract: Embodiments described herein are directed to migrating affected services away from a faulted cloud node and to handling faults during an upgrade. In one scenario, a computer system determines that virtual machines running on a first cloud node are in a faulted state. The computer system determines which cloud resources on the first cloud node were allocated to the faulted virtual machine, allocates the determined cloud resources of the first cloud node to a second, different cloud node and re-instantiates the faulted virtual machine on the second, different cloud node using the allocated cloud resources.

Abstract: Various techniques for deferred server recovery are disclosed herein. In one embodiment, a method includes receiving a notification of a fault from a host in the computing system. The host is performing one or more computing tasks for one or more users. The method can then include determining whether recovery of the fault in the received notification is deferrable on the host. In response to determining that the fault in the received notification is deferrable, the method includes setting a time delay to perform a pending recovery operation on the host at a later time and disallowing additional assignment of computing tasks to the host.

Abstract: Embodiments described herein are directed to migrating affected services away from a faulted cloud node and to handling faults during an upgrade. In one scenario, a computer system determines that virtual machines running on a first cloud node are in a faulted state. The computer system determines which cloud resources on the first cloud node were allocated to the faulted virtual machine, allocates the determined cloud resources of the first cloud node to a second, different cloud node and re-instantiates the faulted virtual machine on the second, different cloud node using the allocated cloud resources.

Abstract: Embodiments described herein are directed to migrating affected services away from a faulted cloud node and to handling faults during an upgrade. In one scenario, a computer system determines that virtual machines running on a first cloud node are in a faulted state. The computer system determines which cloud resources on the first cloud node were allocated to the faulted virtual machine, allocates the determined cloud resources of the first cloud node to a second, different cloud node and re-instantiates the faulted virtual machine on the second, different cloud node using the allocated cloud resources.

Abstract: Embodiments described herein are directed to migrating affected services away from a faulted cloud node and to handling faults during an upgrade. In one scenario, a computer system determines that virtual machines running on a first cloud node are in a faulted state. The computer system determines which cloud resources on the first cloud node were allocated to the faulted virtual machine, allocates the determined cloud resources of the first cloud node to a second, different cloud node and re-instantiates the faulted virtual machine on the second, different cloud node using the allocated cloud resources.