Vmware, Inc.

Ambarish Prashant Pande

Avinash Nigam

Prahalad Gowardhan Deshpande

Sandip K Shah

Amol Manohar Vaikar

Abhijit Sharma

Rohan Gandhi

Philip Brighten Godfrey

Shashank Ranjan

Devraj Narendra Baheti

Vishal Ranjan

Gourab Ghosh

Prashant Jain

Ravi Singhal

Amarjit Gupta

Madan Singhal

Somil Bhandari

Pratiksha Ishwarlal Gugale

Shubhrajyoti Mohapatra

Embodiments described herein generally involve identifying workloads in a multi-site networking environment. Embodiments include determining that a given network is stretched across a first network segment at a first site and a second network segment at a second site. Embodiments include creating a stretched administrative domain for the given network and mapping an address of the given network to the stretched administrative domain in a lookup table for an administrative domain associated with the first network segment. Embodiments include receiving a flow record from an observation point in the first network segment, the flow record having a source IP address associated with the second network segment and a destination IP address associated with the first network segment. Embodiments include identifying a source workload and destination workload of the flow record using the lookup table and a workload identification table that maps combinations of IP addresses and administrative domains to workloads.

Workload identification for network flows over stretched layer 2 networks

Some embodiments provide a method for evaluating locations of applications in a multi-cloud network with applications located in different cloud datacenters of one or more cloud providers. The method receives data for flows collected from the cloud datacenters. The data for each flow indicates a source and destination of the flow and applications to which at least one of the source and destination of the flow belong. The method uses network topology data to identify a subset of the flows as egress flows, each of which is charged by a cloud provider at which the flow source is located. Based on data from the cloud providers, the method determines a cost for each of the egress flows and a cost associated with each of the applications. The method provides these costs to users of the network for the users to optimize locations of the applications in the different cloud datacenters.

Egress cost optimization for multi-cloud network

Some embodiments provide a novel method for assessing the suitability of network links for connecting compute nodes located at different geographic sites. The method of some embodiments identifies and analyzes sample packets from a set of flows exchanged between first and second compute sites that are connected through a first network link in order to identify attributes of the sampled packets. The method also computes attributes of predicted packets between the identified samples in order to identify attributes of each flow in the set of flows. The method then uses the identified and computed attributes of each flow in the set of flows to emulate the set of flows passing between the two compute sites through the second network link in order to assess whether a second network link should be used for future flows (e.g., future flows exchanged between the first and second compute sites).

Emulating packet flows to assess network links for SD-WAN

An example method of identifying a hybrid connection associated with a network flow in a multi-cloud computing system includes: obtaining, by a network analyzer, network information from a plurality of data centers in the multi-cloud computing system, the plurality of data centers corresponding to a respective plurality of namespaces; identifying Internet Protocol (IP) subnetworks associated with the hybrid connection from the network information; generating connection indexes for the namespaces relating source IP subnetworks of the IP subnetworks, destination IP subnetworks of the IP subnetworks, and an identifier for the hybrid connection; searching a source IP address and a destination IP address of a flow record for the network flow in the connection indexes to obtain the identifier for the hybrid connection; and tagging the flow record with the identifier for the hybrid connection.

Identifying hybrid connectivity for traffic flows in a multi-cloud environment

A system and method for data route discovery through cross-connection tunnels uses routing configurations from a local edge router in a private cloud that include learnt and advertised subnets, as well as cross-connection tunnels for the learnt and advertised subnets, to populate a public cloud table of public cloud learnt subnets and a public cloud table of public cloud advertised subnets. The public cloud tables of public cloud learnt subnets and public cloud advertised subnets are applied to discover data routes through the cross-connection tunnels of the hybrid cloud environment.

System and method for data route discovery through cross-connection tunnels

The disclosure provides an approach for service deployment. Embodiments include receiving an indication of user intent for deployment of one or more services in a network from a user that is not an administrator of the network, wherein the indication of the user intent comprises a domain specific language (DSL). Embodiments include parsing the indication of the user intent to determine one or more constraints for deploying the one or more services. Embodiments include receiving topology information for the network, wherein the topology information comprises associations between racks and machines in the network. Embodiments include receiving network performance information for the network. Embodiments include determining one or more deployment rules for the one or more services based on the one or more constraints, the topology information, and the network performance information. Embodiments include deploying the one or more services in the network based on the one or more deployment rules.

Rack-aware and network performance-aware service deployment

A system and method for topology construction in hybrid cloud environments includes receiving connection information from a local perimeter edge router of a local network that has a known topology and receiving connection information from a remote perimeter edge router of a remote network that is provided by a cloud provider. The received local perimeter edge router connection information is associated with the received remote perimeter edge router connection information. The associated connection information is normalized to a generic model, the generic model representing connections having different link layer policies in a generic way and the normalized connection information is applied to a network topology of the local network and the remote network.

Growing community of inventors

Akola, India

Ambarish Prashant Pande