Abstract: An embodiment may involve server devices arranged into pods, each server device hosting computational instances, and a central computational instance configured to: (i) obtain per-pod lists of the instances hosted by the pods; (ii) determine a maximum number of the instances to arrange into batches; (iii) determine a group size for groups of the instances that are to be placed into the batches; (iv) execute a first phase that involves removing per-pod groups from the per-pod lists and adding them to the batches, until less of the instances than the group size remains in each of the per-pod lists; (v) execute a second phase that involves removing one of the instances from the per-pod lists and adding it to the batches, until none of the instances remains in any of the per-pod lists; and (vi) schedule one or more of the automations to take place in the data center.

Abstract: The present approach relates to the use of time series analyses to estimate times or time intervals when a user of IT resources is likely to schedule or request that an operation is run on those services. In certain implementations, the present approach performs forecasting using time series data and supervised machine learning techniques. These techniques may be used to help predict future times when an operation or operations may be requested for execution. Based on these predicted future time, automations (e.g., the automated execution of operations) may be scheduled so as to effectively utilize available resources and efficiently perform the operations.

Abstract: The present approach relates to the use of time series analyses to estimate times or time intervals when a user of IT resources is likely to schedule or request that an operation is run on those services. In certain implementations, the present approach performs forecasting using time series data and supervised machine learning techniques. These techniques may be used to help predict future times when an operation or operations may be requested for execution. Based on these predicted future time, automations (e.g., the automated execution of operations) may be scheduled so as to effectively utilize available resources and efficiently perform the operations.

Abstract: The presently disclosed systems and methods collect information associated with client instances, and use the information associated with the instances. In particular, information related to database servers and/or applications servers providing the instance may be retrieved from databases that store information relating to instances, and written and saved in an instance configuration file. Moreover, instances may be deployed or provisioned by reading an instance configuration file and setting properties of the instance using the parameters saved in the instance configuration file. Two instance configuration files may be compared to determine differences between the two files. Additionally, the instance may be reverted back to the previously healthy version of the instance using the previous instance configuration file.

Abstract: The present approach relates to the use of time series analyses to estimate times or time intervals when a user of IT resources is likely to schedule or request that an operation is run on those services. In certain implementations, the present approach performs forecasting using time series data and supervised machine learning techniques. These techniques may be used to help predict future times when an operation or operations may be requested for execution. Based on these predicted future time, automations (e.g., the automated execution of operations) may be scheduled so as to effectively utilize available resources and efficiently perform the operations.

Abstract: The present approach relates to the use of time series analyses to estimate times or time intervals when a user of IT resources is likely to schedule or request that an operation is run on those services. In certain implementations, the present approach performs forecasting using time series data and supervised machine learning techniques. These techniques may be used to help predict future times when an operation or operations may be requested for execution. Based on these predicted future time, automations (e.g., the automated execution of operations) may be scheduled so as to effectively utilize available resources and efficiently perform the operations.

Abstract: The presently disclosed systems and methods collect information associated with client instances, and use the information associated with the instances. In particular, information related to database servers and/or applications servers providing the instance may be retrieved from databases that store information relating to instances, and written and saved in an instance configuration file. Moreover, instances may be deployed or provisioned by reading an instance configuration file and setting properties of the instance using the parameters saved in the instance configuration file. Two instance configuration files may be compared to determine differences between the two files. Additionally, the instance may be reverted back to the previously healthy version of the instance using the previous instance configuration file.

Abstract: An embodiment may involve server devices arranged into pods, each server device hosting computational instances, and a central computational instance configured to: (i) obtain per-pod lists of the instances hosted by the pods; (ii) determine a maximum number of the instances to arrange into batches; (iii) determine a group size for groups of the instances that are to be placed into the batches; (iv) execute a first phase that involves removing per-pod groups from the per-pod lists and adding them to the batches, until less of the instances than the group size remains in each of the per-pod lists; (v) execute a second phase that involves removing one of the instances from the per-pod lists and adding it to the batches, until none of the instances remains in any of the per-pod lists; and (vi) schedule one or more of the automations to take place in the data center.

Abstract: The presently disclosed systems and methods collect information associated with client instances, and use the information associated with the instances. In particular, information related to database servers and/or applications servers providing the instance may be retrieved from databases that store information relating to instances, and written and saved in an instance configuration file. Moreover, instances may be deployed or provisioned by reading an instance configuration file and setting properties of the instance using the parameters saved in the instance configuration file. Two instance configuration files may be compared to determine differences between the two files. Additionally, the instance may be reverted back to the previously healthy version of the instance using the previous instance configuration file.

Abstract: The presently disclosed systems and methods collect information associated with client instances, and use the information associated with the instances. In particular, information related to database servers and/or applications servers providing the instance may be retrieved from databases that store information relating to instances, and written and saved in an instance configuration file. Moreover, instances may be deployed or provisioned by reading an instance configuration file and setting properties of the instance using the parameters saved in the instance configuration file. Two instance configuration files may be compared to determine differences between the two files. Additionally, the instance may be reverted back to the previously healthy version of the instance using the previous instance configuration file.