Abstract: Some embodiments provide a novel method for receiving a plurality of attribute sets from a set of host computers, each attribute set associated with a group of one or more flows that is created by using a key to associate individual flows into the group of flows. The appliance, in some embodiments, merges two identified attribute sets into one merged attribute set and analyzes the merged attribute set to identify a set of properties of the flows in the groups of flows associated with the two attribute sets. In some embodiments, a visualization process includes identifying machines as members of groups and identifying machines that are connected. The visualization process, in some embodiments, also generates a graphical user interface that can be used to select groups of machines, domains, or individual machines and displays contextual attributes relevant to the selected group, domain, or machine.

Abstract: Some embodiments provide a novel method for collecting and reporting attributes of data flows associated with machines executing on a plurality of host computers to an analysis appliance and providing visual representations of the data to a user. Some embodiments provide a visual representation of the collected data that allows a user to select a set of machines and flows and initiate recommendation generation based on the selected machines and flows. The recommendation generation, in some embodiments, includes identifying flows for which rules have not been defined and filtering the identified rules to remove flows for which rules should not be defined. Some embodiments use the identified rues to identify services and groups associated with the rules and generate recommendations for rules, groups and services based on the identified flows, groups and services. The recommendations, in some embodiments, are implemented as a single PATCH API.

Abstract: Some embodiments provide a novel method for receiving a plurality of attribute sets from a set of host computers, each attribute set associated with a group of one or more flows that is created by using a key to associate individual flows into the group of flows. The appliance, in some embodiments, merges two identified attribute sets into one merged attribute set and analyzes the merged attribute set to identify a set of properties of the flows in the groups of flows associated with the two attribute sets. In some embodiments, a visualization process includes identifying machines as members of groups and identifying machines that are connected. The visualization process, in some embodiments, also generates a graphical user interface that can be used to select groups of machines, domains, or individual machines and displays contextual attributes relevant to the selected group, domain, or machine.

Abstract: Some embodiments provide a novel method for collecting and reporting attributes of data flows associated with machines executing on a plurality of host computers to an analysis appliance and providing visual representations of the data to a user. Some embodiments provide a visual representation of the collected data that allows a user to select a set of machines and flows and initiate recommendation generation based on the selected machines and flows. The recommendation generation, in some embodiments, includes identifying flows for which rules have not been defined and filtering the identified rules to remove flows for which rules should not be defined. Some embodiments use the identified rues to identify services and groups associated with the rules and generate recommendations for rules, groups and services based on the identified flows, groups and services. The recommendations, in some embodiments, are implemented as a single PATCH API.

Abstract: Some embodiments provide a novel method for collecting and reporting attributes of data flows associated with machines executing on a plurality of host computers to an analysis appliance and providing visual representations of the data to a user. Some embodiments provide a visual representation of the collected data that allows a user to select a set of machines and flows and initiate recommendation generation based on the selected machines and flows. The recommendation generation, in some embodiments, includes identifying flows for which rules have not been defined and filtering the identified rules to remove flows for which rules should not be defined. Some embodiments use the identified rues to identify services and groups associated with the rules and generate recommendations for rules, groups and services based on the identified flows, groups and services. The recommendations, in some embodiments, are implemented as a single PATCH API.

Abstract: Some embodiments provide a novel method for collecting and reporting attributes of data flows associated with machines executing on a plurality of host computers to an analysis appliance and providing visual representations of the data to a user. Some embodiments provide a visual representation of the collected data that allows a user to select a set of machines and flows and initiate recommendation generation based on the selected machines and flows. The recommendation generation, in some embodiments, includes identifying flows for which rules have not been defined and filtering the identified rules to remove flows for which rules should not be defined. Some embodiments use the identified rues to identify services and groups associated with the rules and generate recommendations for rules, groups and services based on the identified flows, groups and services. The recommendations, in some embodiments, are implemented as a single PATCH API.

Abstract: Systems, methods, and computer-readable media for designing network performance and configuration include determining one or more use cases for a network to be provisioned, based on at least one or more business verticals related to a customer of the network. A data plane scale is determined from the use cases and an initial data plane scale generated using a linear regression on one or more data plane parameters. The data plane parameters include a platform type, feature set, packet size, or software version of the network. A control plane scale is determined from the use cases and an initial control plane scale generated using a linear regression on one or more control plane parameters of the network. The control plane parameters include a platform type, feature set, or software version of the network. The network is provisioned for the data plane scale and the control plane scale.

Abstract: Some embodiments provide a novel method for collecting and reporting attributes of data flows associated with machines executing on a plurality of host computers to an analysis appliance and providing visual representations of the data to a user. Some embodiments provide a visual representation of the collected data that allows a user to select a set of machines and flows and initiate recommendation generation based on the selected machines and flows. The recommendation generation, in some embodiments, includes identifying flows for which rules have not been defined and filtering the identified rules to remove flows for which rules should not be defined. Some embodiments use the identified rues to identify services and groups associated with the rules and generate recommendations for rules, groups and services based on the identified flows, groups and services. The recommendations, in some embodiments, are implemented as a single PATCH API.

Abstract: Some embodiments provide a novel method for receiving a plurality of attribute sets from a set of host computers, each attribute set associated with a group of one or more flows that is created by using a key to associate individual flows into the group of flows. The appliance, in some embodiments, merges two identified attribute sets into one merged attribute set and analyzes the merged attribute set to identify a set of properties of the flows in the groups of flows associated with the two attribute sets. In some embodiments, a visualization process includes identifying machines as members of groups and identifying machines that are connected. The visualization process, in some embodiments, also generates a graphical user interface that can be used to select groups of machines, domains, or individual machines and displays contextual attributes relevant to the selected group, domain, or machine.

Abstract: Systems, methods, and computer-readable media for designing network performance and configuration include determining one or more use cases for a network to be provisioned, based on at least one or more business verticals related to a customer of the network. A data plane scale is determined from the use cases and an initial data plane scale generated using a linear regression on one or more data plane parameters. The data plane parameters include a platform type, feature set, packet size, or software version of the network. A control plane scale is determined from the use cases and an initial control plane scale generated using a linear regression on one or more control plane parameters of the network. The control plane parameters include a platform type, feature set, or software version of the network. The network is provisioned for the data plane scale and the control plane scale.