Abstract: Systems and methods for automatic network health check are disclosed herein. A method for performing an automatic health check includes determining to perform a health check on a portion of a communications network, the communications network including a plurality of hosts that each include a routing agent and an advertising agent. The method includes adding a test route indicated as applicable to every host and pointing to an IP address to a database, and receiving the test route from the database with the routing agents of at least some of the plurality of hosts.

Abstract: Systems and methods for performing an automatic route flip are disclosed herein. The method can include receiving a request to flip a primary route and a secondary route in a communications network including at least a first host and a second host, each including a routing agent and an advertising agent. The method includes identifying the first host as having a dynamic path length and the second host as having a static path length, updating routing information in a database accessible by the first host to change the path length of the first host from a first path length to a second path length, receiving the updated routing information from the database with the routing agent of the first host, and advertising the updated routing information with the first host to at least one switch within the communications network.

Abstract: Systems and methods for route mismatch identification are disclosed herein. A method of route mismatch identification can create in cache an expected routing table based on expected routing information received by a routing agent of a host from a database accessible by each of the plurality of hosts. The method can include creating in cache an actual routing table based on actual routing information received by the routing agent of the host from an advertising agent of the host, comparing the actual routing table and the expected routing table, and taking an action based on the comparison of the actual routing table and the expected routing table.

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: A disclosed cloud service (CS) enables cross-cloud access to data resources and cross-cloud execution of orchestrations, including automations. The CS includes a peer-to-peer (P2P) cloud orchestrator service (COS) and a cloud broker service (CBS). The COS enables P2P identification and communication routing between different cloud computing environments. The CBS enables cross-cloud access to data and orchestrations (e.g., one or more scripts, workflows, and/or scheduled jobs) from different cloud computing environments. Additionally, the data center may be configured to route all orchestration calls of a data center through the CS, such that the CS can ensure that references to local data and orchestrations are handled within the data center, while references to data and orchestrations of a different data center are suitably routed to be handled by a corresponding CS of the appropriate data center.

Abstract: Various approaches are described to manage the execution of operations. Such operations may be performed without human intervention and may help maintain functionality of a cloud platform or client instances. In one aspect of the present approach, the number and/or type of automations starting in a given time frame may be limited to maintain an even or consistent distribution of resource usage. In a further aspect, the number and/or type of concurrent automations may be limited to a defined threshold to maintain an even or consistent distribution of resource usage.

Abstract: Systems and methods are disclosed that acquire server resources at the time of scheduling an automated instance-related task, such as an instance migration task, and prior to starting the automated task (e.g., prior to determining scheduling conflicts, creating a change request, or creating a move context associated with starting the instance migration task). Advantageously, if acquiring the server resources fails, an orchestration server performing the automated task can simply retry acquiring the server resources, thus avoiding restarting the automated task and re-performing steps of the automated task, thus avoiding unnecessary overhead.

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: Various approaches are described to manage the execution of operations. Such operations may be performed without human intervention and may help maintain functionality of a cloud platform or client instances. In one aspect of the present approach, the number and/or type of automations starting in a given time frame may be limited to maintain an even or consistent distribution of resource usage. In a further aspect, the number and/or type of concurrent automations may be limited to a defined threshold to maintain an even or consistent distribution of resource usage.

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: A disclosed cloud service (CS) enables cross-cloud access to data resources and cross-cloud execution of orchestrations, including automations. The CS includes a peer-to-peer (P2P) cloud orchestrator service (COS) and a cloud broker service (CBS). The COS enables P2P identification and communication routing between different cloud computing environments. The CBS enables cross-cloud access to data and orchestrations (e.g., one or more scripts, workflows, and/or scheduled jobs) from different cloud computing environments. Additionally, the data center may be configured to route all orchestration calls of a data center through the CS, such that the CS can ensure that references to local data and orchestrations are handled within the data center, while references to data and orchestrations of a different data center are suitably routed to be handled by a corresponding CS of the appropriate 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: 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: A disclosed cloud service (CS) enables cross-cloud access to data resources and cross-cloud execution of orchestrations, including automations. The CS includes a peer-to-peer (P2P) cloud orchestrator service (COS) and a cloud broker service (CBS). The COS enables P2P identification and communication routing between different cloud computing environments. The CBS enables cross-cloud access to data and orchestrations (e.g., one or more scripts, workflows, and/or scheduled jobs) from different cloud computing environments. Additionally, the data center may be configured to route all orchestration calls of a data center through the CS, such that the CS can ensure that references to local data and orchestrations are handled within the data center, while references to data and orchestrations of a different data center are suitably routed to be handled by a corresponding CS of the appropriate data center.

Abstract: Various approaches are described to manage the execution of operations. Such operations may be performed without human intervention and may help maintain functionality of a cloud platform or client instances. In one aspect of the present approach, the number and/or type of automations starting in a given time frame may be limited to maintain an even or consistent distribution of resource usage. In a further aspect, the number and/or type of concurrent automations may be limited to a defined threshold to maintain an even or consistent distribution of resource usage.

Abstract: Systems and methods are disclosed that acquire server resources at the time of scheduling an automated instance-related task, such as an instance migration task, and prior to starting the automated task (e.g., prior to determining scheduling conflicts, creating a change request, or creating a move context associated with starting the instance migration task). Advantageously, if acquiring the server resources fails, an orchestration server performing the automated task can simply retry acquiring the server resources, thus avoiding restarting the automated task and re-performing steps of the automated task, thus avoiding unnecessary overhead.

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.