Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting surplus capacity on a set of server nodes and determining a quantity of deferrable virtual machines (VMs) that may be scheduled over an upcoming period of time. This determination of VM quantity may be determined while minimizing risks associated with allocation failures on the set of server nodes. This disclosure described systems that facilitate features and functionality related to improving utilization of surplus resource capacity on a plurality of server nodes by implementing VMs having some flexibility in timing of deployment while also avoiding significant risk caused as a result of over-allocated storage and computing resources. In one or more embodiments, the quantity of deferrable VMs is determined and scheduled in accordance with rules of a scheduling policy.

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: 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: 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 for predicting deployment growth on one or more node clusters and selectively permitting deployment requests on a per cluster basis. For example, systems disclosed herein may apply tenant growth prediction system trained to output a deployment growth classification indicative of a predicted growth of deployments on a node cluster. The system disclosed herein may further utilize the deployment growth classification to determine whether a deployment request may be permitted while maintaining a sufficiently sized capacity buffer to avoid deployment failures for existing deployments previously implemented on the node cluster. By selectively permitting or denying deployments based on a variety of factors, the systems described herein can more efficiently utilize cluster resources on a per-cluster basis without causing a significant increase in deployment failures for existing customers.

Abstract: A method for minimizing allocation failures in a cloud computing system without overprovisioning may include determining a predicted supply for a virtual machine series in a system unit of the cloud computing system during an upcoming time period. The predicted supply may be based on a shared available current capacity and a shared available future added capacity for the virtual machine series in the system unit. The method may also include predicting an available capacity for the virtual machine series in the system unit during the upcoming time period. The predicted available capacity may be based at least in part on a predicted demand for the virtual machine series in the system unit during the upcoming time period and the predicted supply. The method may also include taking at least one mitigation action in response to determining that the predicted demand exceeds the predicted supply during the upcoming time period.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting surplus capacity on a set of server nodes and determining a quantity of deferrable virtual machines (VMs) that may be scheduled over an upcoming period of time. This determination of VM quantity may be determined while minimizing risks associated with allocation failures on the set of server nodes. This disclosure described systems that facilitate features and functionality related to improving utilization of surplus resource capacity on a plurality of server nodes by implementing VMs having some flexibility in timing of deployment while also avoiding significant risk caused as a result of over-allocated storage and computing resources. In one or more embodiments, the quantity of deferrable VMs is determined and scheduled in accordance with rules of a scheduling policy.

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 invention is directed towards a method 200 and system 100 to automate the entire lifecycle of Apigee API Management deployment via a templated approach, the method 200 comprising steps of receiving data related to application programming interfaces from the customer device 102a at the developer device 102b, validating the data for knowing the state of existing application programming interfaces (APIs), creating a template based on customer requirement, wherein the template is creating by generating an APIGEE® application programming interface proxy 412 and customer integration or customer delivery (CI/CD) configuration 414 based on the data, storing the APIGEE® application programming interface proxy and customer integration or customer delivery configuration at a data repository, generating a repeatable build job for deployment of the APIGEE R application programming interface proxy and customer integration or customer delivery configuration and deploying and executing the job on the on premises

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 method for evaluating at least one potential policy for an IaaS system may include determining a predicted workload for the IaaS system based on at least one generative model corresponding to the IaaS system. The at least one potential policy for the IaaS system may be simulated based on the predicted workload, thereby producing one or more simulation metrics that indicate effects of the at least one potential policy. The performance of the IaaS system may be optimized based on the one or more simulation metrics.

Abstract: Embodiments relate to determining whether to take a resource distribution unit (RDU) of a datacenter offline when the RDU becomes faulty. RDUs in a cloud or datacenter supply a resource such as power, network connectivity, and the like to respective sets of hosts that provide computing resources to tenant units such as virtual machines (VMs). When an RDU becomes faulty some of the hosts that it supplies may continue to function and others may become unavailable for various reasons. This can make a decision of whether to take the RDU offline for repair difficult, since in some situations countervailing requirements of the datacenter may be at odds. To decide whether to take an RDU offline, the potential impact on availability of tenant VMs, unused capacity of the datacenter, a number or ratio of unavailable hosts on the RDU, and other factors may be considered to make a balanced decision.

Abstract: The present disclosure relates to systems, methods, and computer readable media for predicting deployment growth on one or more node clusters and selectively permitting deployment requests on a per cluster basis. For example, systems disclosed herein may apply tenant growth prediction system trained to output a deployment growth classification indicative of a predicted growth of deployments on a node cluster. The system disclosed herein may further utilize the deployment growth classification to determine whether a deployment request may be permitted while maintaining a sufficiently sized capacity buffer to avoid deployment failures for existing deployments previously implemented on the node cluster. By selectively permitting or denying deployments based on a variety of factors, the systems described herein can more efficiently utilize cluster resources on a per-cluster basis without causing a significant increase in deployment failures for existing customers.

Abstract: A method for minimizing allocation failures in a cloud computing system without overprovisioning may include determining a predicted supply for a virtual machine series in a system unit of the cloud computing system during an upcoming time period. The predicted supply may be based on a shared available current capacity and a shared available future added capacity for the virtual machine series in the system unit. The method may also include predicting an available capacity for the virtual machine series in the system unit during the upcoming time period. The predicted available capacity may be based at least in part on a predicted demand for the virtual machine series in the system unit during the upcoming time period and the predicted supply. The method may also include taking at least one mitigation action in response to determining that the predicted demand exceeds the predicted supply during the upcoming time period.

Abstract: Aspects extend to methods, systems, and computer program products for clustering streamed or batch data. Aspects of the invention include dynamic clustering and labeling of streamed data and/or batch data, including failures and error logs (user, platform, etc.), latency logs, warning logs, information logs, Virtual Machine (VM) creation data logs, template logs, etc., for use in analysis (e.g., error log analysis). A clustering system can learn from previously identified patterns and use that information to group newer information dynamically as it gets generated. The clustering system can leverage streamed data and/or batch data domain knowledge for preprocessing. In one aspect, a clustering system uses a similarity measure. Based on (e.g., users' configuration of) a similarity threshold, the cluster system (e.g., automatically) assigns/clusters streamed data and/or batch data into groups.

Abstract: A method for evaluating at least one potential policy for an IaaS system may include determining a predicted workload for the IaaS system based on at least one generative model corresponding to the IaaS system. The at least one potential policy for the IaaS system may be simulated based on the predicted workload, thereby producing one or more simulation metrics that indicate effects of the at least one potential policy. The performance of the IaaS system may be optimized based on the one or more simulation metrics.

Abstract: Techniques for automated capacity managed in distributed computing systems are disclosed herein. In one embodiment, a method includes receiving predicting one or more future usage levels of a computing resource in the distributed computing system based on received data representing historical usage levels of the computing resource and determining whether a currently available capacity of the computing resource in the distributed computing system is depleted beyond a threshold time period based on the one or more future usage levels. In response to determining that the currently available capacity of the computing resource in the distributed computing system is depleted before the threshold time period, the method includes immediately rebooting, reimaging, or performing other recovery actions on one or more out-for-repair hosts that provide the computing resource.

Abstract: Embodiments relate to determining whether to take a resource distribution unit (RDU) of a datacenter offline when the RDU becomes faulty. RDUs in a cloud or datacenter supply a resource such as power, network connectivity, and the like to respective sets of hosts that provide computing resources to tenant units such as virtual machines (VMs). When an RDU becomes faulty some of the hosts that it supplies may continue to function and others may become unavailable for various reasons. This can make a decision of whether to take the RDU offline for repair difficult, since in some situations countervailing requirements of the datacenter may be at odds. To decide whether to take an RDU offline, the potential impact on availability of tenant VMs, unused capacity of the datacenter, a number or ratio of unavailable hosts on the RDU, and other factors may be considered to make a balanced decision.

Abstract: A method of detecting random telegraph noise defects in a memory includes initializing a first bit cell of the memory to a first value and reading the first value from the first bit cell. The method also includes writing a second value to the first bit cell and performing back to back read operations on a second bit cell adjacent to the first bit cell, after writing the second value. The method further includes attempting to read the second value from the first bit cell and determining whether the first bit cell is defective based on whether the second value was read from the first bit cell.