Abstract: Systems and methods are taught for providing customers of a cloud computing service to control when updates affect the services provided to the customers. Because multiple customers share the cloud's infrastructure, each customer may have conflicting preferences for when an update and associated downtime occurs. Preventing and resolving conflicts between the preferences of multiple customers while providing them with input for scheduling a planned update may reduce the inconvenience posed by updates. Additionally, the schedule for the update may be transmitted to customers so that they can prepare for the downtime of services associated with the update.

Abstract: Systems and methods are taught for providing customers of a cloud computing service to control when updates affect the services provided to the customers. Because multiple customers share the cloud's infrastructure, each customer may have conflicting preferences for when an update and associated downtime occurs. Preventing and resolving conflicts between the preferences of multiple customers while providing them with input for scheduling a planned update may reduce the inconvenience posed by updates. Additionally, the schedule for the update may be transmitted to customers so that they can prepare for the downtime of services associated with the update.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Abstract: The present disclosure relates to systems, methods, and computer readable media that utilize a low-impact live-migration system to reduce unfavorable impacts caused as a result of live-migrating computing containers between physical server devices of a cloud computing system. For example, systems disclosed herein evaluates characteristics of computing containers on server devices to determine a predicted unfavorable impact of live-migrating the computing containers between the server devices. Based on the predicted impact, the systems disclosed herein can selectively identify which computing containers to live-migrate as well as carry out live-migration of the select computing containers in such a way the significantly reduces unfavorable impacts to a customer or client device associated with the computing containers.

Abstract: Systems and methods are taught for providing customers of a cloud computing service to control when updates affect the services provided to the customers. Because multiple customers share the cloud's infrastructure, each customer may have conflicting preferences for when an update and associated downtime occurs. Preventing and resolving conflicts between the preferences of multiple customers while providing them with input for scheduling a planned update may reduce the inconvenience posed by updates. Additionally, the schedule for the update may be transmitted to customers so that they can prepare for the downtime of services associated with the update.

Abstract: The present disclosure relates to systems, methods, and computer readable media for rightsizing virtual machine deployments on a cloud computing system. For example, systems disclosed herein may predict utilization of resources for a customer deployment and determine a desired goal state including a deployment of virtual machines having rightsized specifications that align more closely with the predicted utilization. Systems disclosed herein may utilize the goal state in view of the deployment data, policies, and other information to determine an action plan including deployment actions for transitioning a current state of a customer deployment to the goal state. By rightsizing virtual machine deployments, systems described herein may affect more efficient utilization of cloud computing resources and decrease costs associated with over-allocation of cloud computing resources.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting expansion failures and implementing defragmentation instructions based on the predicted expansion failures and other signals. For example, systems disclosed herein may apply a failure prediction model to determine an expansion failure prediction associated with an estimated likelihood that deployment failures will occur on a node cluster. The systems disclosed herein may further generate defragmentation instructions indicating a severity level that a defragmentation engine may execute on a cluster level to prevent expansion failures while minimizing negative customer impacts. By uniquely generating defragmentation instructions for each node cluster, a cloud computing system can minimize expansion failures, increase resource capacity, reduce costs, and provide access to reliable services to customers.

Abstract: Systems and methods are taught for providing customers of a cloud computing service to control when updates affect the services provided to the customers. Because multiple customers share the cloud's infrastructure, each customer may have conflicting preferences for when an update and associated downtime occurs. Preventing and resolving conflicts between the preferences of multiple customers while providing them with input for scheduling a planned update may reduce the inconvenience posed by updates. Additionally, the schedule for the update may be transmitted to customers so that they can prepare for the downtime of services associated with the update.

Abstract: The present disclosure relates to systems, methods, and computer readable media that utilize a low-impact live-migration system to reduce unfavorable impacts caused as a result of live-migrating computing containers between physical server devices of a cloud computing system. For example, systems disclosed herein evaluates characteristics of computing containers on server devices to determine a predicted unfavorable impact of live-migrating the computing containers between the server devices. Based on the predicted impact, the systems disclosed herein can selectively identify which computing containers to live-migrate as well as carry out live-migration of the select computing containers in such a way the significantly reduces unfavorable impacts to a customer or client device associated with the computing containers.

Abstract: Described herein is a system and method for overlapping migration to another host node with a customer request to stop or reboot a virtual machine. A customer request is intercepted. Determination whether or not migration of the virtual machine on the source host node is capable of being performed. When the migration of the virtual machine on the source host node is capable of being performed, the virtual machine is stopped on the source host node, and, the virtual machine is migrated to an updated host node. When the customer request is a stop of the virtual machine, the virtual machine is stopped on the updated host node. When the customer request is a reboot of the virtual machine, the virtual machine is rebooted on the updated host node.

Abstract: In various embodiments, methods and systems for optimizing allocation of multi-priority service instances are provided. In embodiments, a quality metric associated with each candidate node to which a service instance could be allocated is determined. An eviction cost or a survival metric associated with at least a portion of the candidate nodes to which the service instance could be allocated are determined. The eviction costs generally indicate a cost to evict a service instance from a corresponding node such that another service instance can be allocated to that node. At least a portion of the quality metrics and either the eviction costs or the survival metrics are used to select a node from the candidate nodes to which to allocate the service instance.

Abstract: The present disclosure relates to systems, methods, and computer readable media that utilize a low-impact live-migration system to reduce unfavorable impacts caused as a result of live-migrating computing containers between physical server devices of a cloud computing system. For example, systems disclosed herein evaluates characteristics of computing containers on server devices to determine a predicted unfavorable impact of live-migrating the computing containers between the server devices. Based on the predicted impact, the systems disclosed herein can selectively identify which computing containers to live-migrate as well as carry out live-migration of the select computing containers in such a way the significantly reduces unfavorable impacts to a customer or client device associated with the computing containers.

Abstract: Techniques are disclosed for providing opportunistic virtual machine migration. In response to determining that a virtual machine will be rebooted, a determination is made whether the virtual machine should be migrated from the computing device that is currently hosting the virtual machine. Based on one or more criteria, a destination computing device for the virtual machine is identified. When a performance threshold is met, subsequent to shutting down the virtual machine at the current host and prior to rebooting the virtual machine at a new host, the virtual machine is migrated to the destination computing device.

Abstract: According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that are to cause the processor to receive an allocation request from an allocator client, determine a resource allocation for the received job allocation request, store a record of the determined resource allocation in a records store, and send the determined resource allocation to the allocator client, in which an acknowledgement to the sent determined resource allocation is to be received from the allocator client. The instructions may also cause the processor to manage a state of the determined resource allocation using the record stored in the records store based on whether the acknowledgement is received from the allocator client.

Abstract: In various embodiments, methods and systems for optimizing allocation of multi-priority service instances are provided. In embodiments, a quality metric associated with each candidate node to which a service instance could be allocated is determined. An eviction cost or a survival metric associated with at least a portion of the candidate nodes to which the service instance could be allocated are determined. The eviction costs generally indicate a cost to evict a service instance from a corresponding node such that another service instance can be allocated to that node. At least a portion of the quality metrics and either the eviction costs or the survival metrics are used to select a node from the candidate nodes to which to allocate the service instance.

Abstract: Systems and methods are taught for providing customers of a cloud computing service to control when updates affect the services provided to the customers. Because multiple customers share the cloud's infrastructure, each customer may have conflicting preferences for when an update and associated downtime occurs. Preventing and resolving conflicts between the preferences of multiple customers while providing them with input for scheduling a planned update may reduce the inconvenience posed by updates. Additionally, the schedule for the update may be transmitted to customers so that they can prepare for the downtime of services associated with the update.

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: In various embodiments, methods and systems for optimizing allocation of dynamic resources are provided. A user service request resource instance is received at an allocator. The user service request resource instance is based on a dynamic resource protocol that supports generating and communicating resource instances between components in a resource allocation platform. The dynamic resource protocol also defines a set of rules for translating and representing resources as logical resource types and logical units. At a node, a node resource instance is generated and communicated, based on the dynamic resource protocol, from the node to the allocator. The node resource instance specifically comprises a resource interaction matrix that indicates dependencies between resource types. A resource allocation instance is generated at the allocator for the user service request resource instance.

Abstract: In various embodiments, methods and systems for optimizing allocation of multi-priority service instances are provided. In embodiments, a packing quality metric associated with each candidate node to which a service instance could be allocated are determined. An eviction cost associated with at least a portion of the candidate nodes to which the service instance could be allocated are determined. The eviction costs generally indicate a cost to evict a service instance from a corresponding node such that another service instance can be allocated to that node. At least a portion of the packing quality metrics and the eviction costs are used to select a node from the candidate nodes to which to allocate the service instance.