Abstract: A cloud computing capacity management system can include a fine-grained admission control layer, a policy engine, and an enforcement layer. The fine-grained admission control layer can be configured to ingest capacity signals and create a capacity mitigation policy, based at least in part on the capacity signals, to protect available capacity of a cloud computing system for prioritized users. The capacity mitigation policy can be directed to users of the cloud computing system. The policy engine can be configured to control how the capacity mitigation policy is applied to the cloud computing system. The enforcement layer can be configured to handle incoming resource requests and to enforce resource limits based on the capacity mitigation policy as applied by the policy engine.

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: A cloud computing capacity management system can include a fine-grained admission control layer, a policy engine, and an enforcement layer. The fine-grained admission control layer can be configured to ingest capacity signals and create a capacity mitigation policy, based at least in part on the capacity signals, to protect available capacity of a cloud computing system for prioritized users. The capacity mitigation policy can be directed to users of the cloud computing system. The policy engine can be configured to control how the capacity mitigation policy is applied to the cloud computing system. The enforcement layer can be configured to handle incoming resource requests and to enforce resource limits based on the capacity mitigation policy as applied by the policy engine.

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: Described herein is an automated capacity management system and method. Input information regarding current conditions of the computing system, and, user data requirements are received. Capacity is predicted based upon at least some of the received input information using a machine trained capacity model. Demand is predicted upon at least some of the received input using a machine trained demand model. Logic is applied to determine one or more mitigation actions to be taken with respect to the computing system in accordance with the predicted capacity and predicted demand. An action based upon the one or more determined mitigation actions is then performed.

Abstract: A self-service system and method for credential reset permits an administrator to customize policies for credential reset based on any user or group of users. Administrators may choose to set a more stringent policy for credential reset for users or groups that have higher-level permissions to access sensitive information within the resource protected by the credential. Customizable, plug-in gates are provided to permit administrators fine grained control over reset policy definition. When the user initiates a credential reset, the reset policy applicable to that user is invoked, and the user is presented with gates to pass pursuant to the applicable reset policy. The user's responses are compared to responses presented by the user at registration. If the responses meet the reset policy's threshold for accuracy, the user is permitted to reset the credential.

Abstract: A self-service system and method for credential reset permits an administrator to customize policies for credential reset based on any user or group of users. Administrators may choose to set a more stringent policy for credential reset for users or groups that have higher-level permissions to access sensitive information within the resource protected by the credential. Customizable, plug-in gates are provided to permit administrators fine grained control over reset policy definition. When the user initiates a credential reset, the reset policy applicable to that user is invoked, and the user is presented with gates to pass pursuant to the applicable reset policy. The user's responses are compared to responses presented by the user at registration. If the responses meet the reset policy's threshold for accuracy, the user is permitted to reset the credential.

Abstract: A system and method for controlling access to a resource permits an administrator to make changes to access policies at a server level without having to update client code unless and until such updated code is actually needed by a client. Customizable, plug-in gates are provided to permit administrators fine grained control over access policy definition. The most updated versions of corresponding gate clients used to display the gates are identified to client systems when an access request is made. The updated gate clients are downloaded if and when requested by a client system that has not already stored the updated gate clients locally. The user's responses to gate challenges are compared to responses presented by the user at registration. If the responses meet the access policy's threshold for accuracy, the user is permitted to access the resource.