Abstract: Methods, apparatus, systems and articles of manufacture to establish a data pipeline between cloud computing platforms. An apparatus includes at least one memory, machine readable instructions in the apparatus, and processor circuitry to execute the machine readable instructions to at least extract a data producer name from data, the data to be provided from a data producer to a data consumer, identify a buffer identifier based on a mapping of the data producer name to the buffer identifier, cause transmission of the data to a buffer associated with the buffer identifier, and cause transmission of the data from the buffer to the data consumer based on an association between the buffer identifier and a data consumer name, the data consumer name corresponding to the data consumer.

Abstract: An example apparatus comprises service manager circuitry to provide a service option to a user of a virtualized computing component, the service option including a first option to request inventory data and a second option to deploy a data collector agent, obtain a request corresponding to the second option, the request including two or more locations to deploy the data collector agent, and deploy the data collector agent at the two or more locations, and network connectivity selector circuitry to obtain a second request corresponding to the second option, determine connectivity factors for the data collector agent, and determine to collect inventory data from the data collector agent based on the connectivity factors.

Abstract: An example apparatus comprises at least one memory, instructions in the apparatus, and at least one processor to execute the instructions to identify an agent identifier from a plurality of network command responses, the plurality of network command responses including a plurality of network parameters, apply weighted values to the plurality of network parameters, and determine a total weighted sum of the plurality of network parameters, the total weighted sum corresponding to a connectivity factor of an agent associated with the agent identifier.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to establish a data pipeline between cloud computing platforms. An example apparatus includes at least one memory, machine readable instructions in the apparatus, and processor circuitry to execute the machine readable instructions to at least extract a data producer name from data, the data to be provided from a data producer to a data consumer, identify a buffer identifier based on a mapping of the data producer name to the buffer identifier, cause transmission of the data to a buffer associated with the buffer identifier, and cause transmission of the data from the buffer to the data consumer based on an association between the buffer identifier and a data consumer name, the data consumer name corresponding to the data consumer.

Abstract: System and method for virtual agent management in a cloud architecture deploys multiple virtual appliances with proxy services in the cloud architecture, instantiates virtual agents in the virtual appliances, communicates with a cloud framework using the proxy services, and controls the virtual agents based on communications with the cloud framework.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to establish a data pipeline between cloud computing platforms. An apparatus includes a producer registration controller to register a data producer with a data pipeline service in a public cloud network, the data producer associated with a private cloud network, a consumer registration controller to register a data consumer with the data pipeline service, and a communication controller to, in response to the registration of the data consumer, transmit data generated by the public cloud network from the data consumer to the data buffer via a first data plane gateway, and, in response to a validation of the data consumer, transmit the data from the data buffer to the data consumer via a second data plane gateway, the first data plane gateway different from the second data plane gateway.

Abstract: An example apparatus comprises at least one memory, instructions in the apparatus, and at least one processor to execute the instructions to identify an agent identifier from a plurality of network command responses, the plurality of network command responses including a plurality of network parameters, apply weighted values to the plurality of network parameters, and determine a total weighted sum of the plurality of network parameters, the total weighted sum corresponding to a connectivity factor of an agent associated with the agent identifier.

Abstract: An example apparatus comprises a variable determiner to: parse a plurality of network command responses from a first data collector agent and from a second data collector agent; initialize a value for network connectivity parameters corresponding to the network command responses, the value corresponding to the parsed plurality of network command responses; and assign weighted values to the network connectivity parameters; a connectivity analyzer to determine a first network connectivity factor for the first data collector agent and a second network connectivity factor for the second data collector agent; and a recommender system to: determine whether the first network connectivity factor is a smaller value than the second network connectivity factor; and when the first network connectivity factor is the smaller value, initiate the first data collector agent corresponding to the first network connectivity factor to begin collecting data.

Abstract: System and method for virtual agent management in a cloud architecture deploys multiple virtual appliances with proxy services in the cloud architecture, instantiates virtual agents in the virtual appliances, communicates with a cloud framework using the proxy services, and controls the virtual agents based on communications with the cloud framework.

Abstract: An example apparatus comprises a variable determiner to: parse a plurality of network command responses from a first data collector agent and from a second data collector agent; initialize a value for network connectivity parameters corresponding to the network command responses, the value corresponding to the parsed plurality of network command responses; and assign weighted values to the network connectivity parameters; a connectivity analyzer to determine a first network connectivity factor for the first data collector agent and a second network connectivity factor for the second data collector agent; and a recommender system to: determine whether the first network connectivity factor is a smaller value than the second network connectivity factor; and when the first network connectivity factor is the smaller value, initiate the first data collector agent corresponding to the first network connectivity factor to begin collecting data.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to establish a data pipeline between cloud computing platforms. An example apparatus includes a producer registration controller to register a data producer with a data pipeline service in a public cloud network, the data producer associated with a private cloud network, a consumer registration controller to register a data consumer with the data pipeline service, and a communication controller to, in response to the registration of the data consumer, transmit data generated by the public cloud network from the data consumer to the data buffer via a first data plane gateway, and, in response to a validation of the data consumer, transmit the data from the data buffer to the data consumer via a second data plane gateway, the first data plane gateway different from the second data plane gateway.

Abstract: Methods and systems allocate storage costs to virtual machines (“VMs”) in a virtual data center. Methods calculate a datastore-base rate based on datastore utilized-storage capacity in each LD and each LD-base rate when the datastore utilized-storage capacity and each LD-base rate are available. Datastore total cost is calculated by multiplying the datastore-base rate by the datastore utilized-storage capacity. Methods also use graph based methods to calculate datastore-base rates when the datastore utilized-storage capacity is unknown for each LD. The datastore-base rate associated with each datastore may then be used to calculate a VM storage cost of each VM hosted by a datastore.

Abstract: Methods and systems that allocate the total cost of virtual storage created from hard disk drives (“HDDs”) and solid state drives (“SSDs”) of server computers and mass-storage devices of a cloud-computing facility are described. The virtual storage is used to form virtual disks (“VDs”) of virtual machines (“VMs”) comprising a virtual datacenter (“VDC”). Methods calculate a total virtual storage cost of the virtual storage from hardware costs and other costs such as labor, maintenance, facilities and licensing costs, which is used to calculate an HDD cost rate and an SSD cost rate. A cost of each VD is calculate based on virtual storage policy parameters, the HDD cost rate, and the SSD cost rate. The costs of the VDs associated with a VM are combined to obtain a VM storage cost. The VM storage costs may be combined to obtain the virtual storage cost of the VDC.

Abstract: Methods and systems are directed to allocating costs of storage arrays to logical disks. A fully loaded cost of storage of a storage area network (“SAN”) is calculated based on capital and operational expenditures of the SAN. Data is stored in the SAN in logical groups partitioned into logical disks. The unit rates of the logical groups are calculated from the capacities and utilization of the storage arrays that form the SAN. The unit rate of a logical disk is the unit rate the logical group to which the logical disk belongs. The storage cost of each logical disk is calculated from the unit rate of the logical disk and the utilized capacity of the logical disk. Methods also calculate cost of unallocated capacities of the logical disks, storage arrays, and the SAN, in order to evaluate efficient use of the SAN storage resources.

Abstract: Methods and systems are directed to allocating costs of storage arrays to logical disks. A fully loaded cost of storage of a storage area network (“SAN”) is calculated based on capital and operational expenditures of the SAN. Data is stored in the SAN in logical groups partitioned into logical disks. The unit rates of the logical groups are calculated from the capacities and utilization of the storage arrays that form the SAN. The unit rate of a logical disk is the unit rate the logical group to which the logical disk belongs. The storage cost of each logical disk is calculated from the unit rate of the logical disk and the utilized capacity of the logical disk. Methods also calculate cost of unallocated capacities of the logical disks, storage arrays, and the SAN, in order to evaluate efficient use of the SAN storage resources.

Abstract: Methods and systems allocate storage costs to virtual machines (“VMs”) in a virtual data center. Methods calculate a datastore-base rate based on datastore utilized-storage capacity in each LD and each LD-base rate when the datastore utilized-storage capacity and each LD-base rate are available. Datastore total cost is calculated by multiplying the datastore-base rate by the datastore utilized-storage capacity. Methods also use graph based methods to calculate datastore-base rates when the datastore utilized-storage capacity is unknown for each LD. The datastore-base rate associated with each datastore may then be used to calculate a VM storage cost of each VM hosted by a datastore.