Abstract: The disclosure provides an approach for selecting a path to use in transferring a virtual computing instance such as a virtual machine (VM), across data centers. In one embodiment, throughput and throttling information are sampled (e.g., per second) when VMs are transferred from a source data center to a destination data center, and the sampled information is used to construct histograms that provide performance profiles of the VM transfers. Such histogram information across a number of VM transfers can then be mined to determine trends. In turn, trends determined from historical histogram information may be extrapolated into the future to predict performance of a subsequent VM transfer operation and to select a path and/or destination storage and host pair to use for the VM transfer operation.

Abstract: The disclosure provides an approach for preventing the failure of virtual computing instance transfers across data centers. In one embodiment, a flow control module collects performance information primarily from components in a local site, as opposed to components in a remote site, during the transfer of a virtual machine (VM) from the local site to the remote site. The performance information that is collected may include various performance metrics, each of which is considered a feature. The flow control module performs feature preparation by normalizing feature data and imputing missing feature data, if any. The flow control module then inputs the prepared feature data into machine learning model(s) which have been trained to predict whether a VM transfer will succeed or fail, given the input feature data. If the prediction is that the VM transfer will fail, then remediation actions may be taken, such as slowing down the VM transfer.

Abstract: A method of transferring data between local and remote computing systems includes the step of transferring data between the local and remote computing systems via a local buffer in the local computing system and a series of steps carried out during transferring of data from the local to the remote computing system. The steps include receiving a statistic from the remote computing system, computing an average transfer rate of the data transfer between the local and remote computing systems based on the statistic, determining whether or not a throttle condition is in effect based on the computed average transfer rate, and upon determining that the throttle condition is in effect, throttling the transferring of data into the local buffer.

Abstract: The disclosure provides an approach for managing and diagnosing middleboxes in a cloud computing system. In one embodiment, a network operations center, that is located remote to a virtualized cloud computing system and communicates with the cloud computing system via a wide area network, controls network middleboxes in the cloud computing system through a secure routing module inside a gateway of the cloud computing system. The secure routing module is configured to receive, from an authenticated management application and via a secure communication channel, packets intended for managing network middleboxes. In turn, the secure routing module establishes secure communication channels with the target middleboxes, translates the identified packets to protocols and/or application programming interfaces (APIs) of the target middleboxes, and transmits the translated packets to the target middleboxes.

Abstract: The disclosure provides an approach for preventing the failure of virtual computing instance transfers across data centers. In one embodiment, a flow control module collects performance information primarily from components in a local site, as opposed to components in a remote site, during the transfer of a virtual machine (VM) from the local site to the remote site. The performance information that is collected may include various performance metrics, each of which is considered a feature. The flow control module performs feature preparation by normalizing feature data and imputing missing feature data, if any. The flow control module then inputs the prepared feature data into machine learning model(s) which have been trained to predict whether a VM transfer will succeed or fail, given the input feature data. If the prediction is that the VM transfer will fail, then remediation actions may be taken, such as slowing down the VM transfer.

Abstract: The disclosure provides an approach for selecting a path to use in transferring a virtual computing instance such as a virtual machine (VM), across data centers. In one embodiment, throughput and throttling information are sampled (e.g., per second) when VMs are transferred from a source data center to a destination data center, and the sampled information is used to construct histograms that provide performance profiles of the VM transfers. Such histogram information across a number of VM transfers can then be mined to determine trends. In turn, trends determined from historical histogram information may be extrapolated into the future to predict performance of a subsequent VM transfer operation and to select a path and/or destination storage and host pair to use for the VM transfer operation.

Abstract: The disclosure provides an approach for selecting a path to use in transferring a virtual computing instance such as a virtual machine (VM), across data centers. In one embodiment, throughput and throttling information are sampled (e.g., per second) when VMs are transferred from a source data center to a destination data center, and the sampled information is used to construct histograms that provide performance profiles of the VM transfers. Such histogram information across a number of VM transfers can then be mined to determine trends. In turn, trends determined from historical histogram information may be extrapolated into the future to predict performance of a subsequent VM transfer operation and to select a path and/or destination storage and host pair to use for the VM transfer operation.

Abstract: The disclosure provides an approach for selecting a path to use in transferring a virtual computing instance such as a virtual machine (VM), across data centers. In one embodiment, throughput and throttling information are sampled (e.g., per second) when VMs are transferred from a source data center to a destination data center, and the sampled information is used to construct histograms that provide performance profiles of the VM transfers. Such histogram information across a number of VM transfers can then be mined to determine trends. In turn, trends determined from historical histogram information may be extrapolated into the future to predict performance of a subsequent VM transfer operation and to select a path and/or destination storage and host pair to use for the VM transfer operation.

Abstract: The disclosure provides an approach for managing and diagnosing middleboxes in a cloud computing system. In one embodiment, a network operations center, that is located remote to a virtualized cloud computing system and communicates with the cloud computing system via a wide area network, controls network middleboxes in the cloud computing system through a secure routing module inside a gateway of the cloud computing system. The secure routing module is configured to receive, from an authenticated management application and via a secure communication channel, packets intended for managing network middleboxes. In turn, the secure routing module establishes secure communication channels with the target middleboxes, translates the identified packets to protocols and/or application programming interfaces (APIs) of the target middleboxes, and transmits the translated packets to the target middleboxes.

Abstract: A method of transferring data between local and remote computing systems includes the step of transferring data between the local and remote computing systems via a local buffer in the local computing system and a series of steps carried out during transferring of data from the local to the remote computing system. The steps include receiving a statistic from the remote computing system, computing an average transfer rate of the data transfer between the local and remote computing systems based on the statistic, determining whether or not a throttle condition is in effect based on the computed average transfer rate, and upon determining that the throttle condition is in effect, throttling the transferring of data into the local buffer.