Ca, Inc.

Avago Technologies International Sales Pte. Limited

Brocade Communications Systems, Inc.

Balaji Varadaraju

John Wiley Ashby, Jr

Douglas Neuse

Laurence E Clay

David McDaniel

Anil Vishnoi

Vasu Srinivasan

Kiran Koushik Agrahara Sreenivasa

Techniques for performing flow-based host discovery in an SDN network are provided. According to one embodiment, an SDN controller can select, based on network topology information that is stored on the controller, a leaf network device in the network, and can determine a traffic flow corresponding to a flow rule installed on the leaf network device. The determining of the traffic flow can be based on flow rule information that is stored on the SDN controller. The SDN controller can further determine a forward path for the traffic flow through the network, where the determining of the forward path is performed by associating the flow rule with other flow rules for the traffic flow that are included in the flow rule information, and where the other flow rules are installed on other network devices in the network. The SDN controller can then determine, based on at least the forward path, host information for the source and destination hosts of the traffic flow.

Flow-based host discovery in SDN networks

A method includes retrieving capacity utilization data for a plurality of resources and applying a linear regression analysis on the capacity utilization data. The method further includes projecting, using a processor, the capacity utilization data through a future time based on results of the linear regression analysis. The method additionally includes determining a deviation from a predetermined threshold range in the projected capacity utilization data for a first resource, and, in response to determining the deviation, determining, for each of a plurality of resource configurations, future capacity utilization of the first resource based on a non-linear capacity consumption model corresponding to the resource configuration. The method also includes applying a selected resource configuration from the plurality of resource configurations to the first resource to prevent the first resource from deviating from the predetermined threshold range.

Selecting resources for automatic modeling using forecast thresholds

Techniques for facilitating port mirroring in virtual networks are provided. In one embodiment, a computer system can receive, from a user, port mirroring configuration information for enabling port mirroring within a virtual network, the port mirroring configuration information including an identity of a port to be mirrored and an address of a traffic receiver intended to receive traffic mirrored from the port. The computer system can further power-on a virtual machine (VM) within the virtual network in response to receiving the port mirroring configuration information, where the VM executes an instance of a virtual bridge. The computer system can then transmit one or more configuration commands to the virtual bridge, where the one or more configuration commands cause the virtual bridge to perform port mirroring in accordance with the port mirroring configuration information received from the user.

Techniques for facilitating port mirroring in virtual networks

A method includes receiving an expected growth rate for modeling future capacity utilization for a resource hosting a plurality of processes, each process being associated with a respective workload. The method also includes modifying, in a non-linear capacity utilization model for the resource, the respective workload for a particular process in the plurality of processes based on the expected growth rate, and, in response to modifying the respective workload, determining a change in total capacity utilization for the resource using the non-linear capacity utilization model. The method further includes determining a ratio between the change in the total capacity utilization for the resource and the modification to the respective workload. The method additionally includes modifying, based on the ratio, a configuration of the resource with respect to the particular process in anticipation of the expected growth rate.

Modeling real capacity consumption changes using process-level data

A method includes accessing a database comprising a respective non-linear scalability model for each of a plurality of physical resources and each of a plurality of virtual resources in a virtualization environment. The method also includes generating a respective capacity consumption model for each of the plurality of virtual resources based on the non-linear scalability models. The method further includes determining a deviation from a predetermined threshold range in the respective capacity consumption model for a first virtual resource in the plurality of virtual resources. The method additionally includes determining a slope of the respective capacity consumption model for the first virtual resource, and determining a sizing recommendation for components of the first virtual resource based on the slope and the deviation. The method also includes modifying at least one of the components of the first virtual resource based on the sizing recommendation.

Automatic modeling of virtual resources with sizing recommendations

A method includes determining historical resource metrics for a host, and determining a workload forecast for the host based on the historical resource metrics. The method also includes determining a first series of virtual resource configurations based on the workload forecast. Each virtual resource configuration corresponds to a respective virtual machine of a plurality of virtual machines from the host. Each virtual resource configuration includes a time interval of the workload forecast, a processing specification of the corresponding virtual machine, and a host assignment indicative of a corresponding target host of the plurality of hosts on which to run the corresponding virtual machine. The method further includes configuring each respective virtual machine according to each corresponding virtual resource configuration in the first series of virtual resource configurations.

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Austin, TX, United States of America

Balaji Varadaraju