Abstract: A system comprises a processor and a non-transitory computer-readable medium to store instructions for execution by the processor. The instructions are configured to discover a topology of a network in an on-premises datacenter, where the topology includes hardware and software components in the on-premises datacenter and in one or more hops from an external network to a gateway of the network. The instructions are configured to determine a plurality of parameters of the components that affect performance of applications and services running on the network. The instructions are configured to determine, based on the discovered topology and the parameters, an optimal topology for the network that optimizes one or more of the parameters to improve the performance of applications and services running on the network.

Abstract: Improved network optimization and scaling are provided by combining optimization of traffic engineering and network topologies. Sets of minimal link capacities are determined based on the constraints of network topology, network demand, and failure modes. The optimization problem is reframed to allow for simultaneous optimization across all failure modes, reducing or eliminating overprovisioning in link capacity allocation by utilizing convex optimization solvers and techniques.

Abstract: A system comprises a processor and a non-transitory computer-readable medium to store instructions for execution by the processor. The instructions are configured to discover a topology of a network in an on-premises datacenter, where the topology includes hardware and software components in the on-premises datacenter and in one or more hops from an external network to a gateway of the network. The instructions are configured to determine a plurality of parameters of the components that affect performance of applications and services running on the network. The instructions are configured to determine, based on the discovered topology and the parameters, an optimal topology for the network that optimizes one or more of the parameters to improve the performance of applications and services running on the network.

Abstract: The present technology is generally directed to updating a cloud network. This technology may include generating a set is inequalities based network topology information, shared risk link group information, demand information, and traffic solver parameters. The set of inequalities may also be based upon a model of traffic routing that is based on selection of routes based on dynamic and global traffic engineering. A capacity plan may also be generated based on the set of inequalities and at least one objective. The capacity plan may include a target capacity for each link of the plurality of links in the network.

Abstract: Improved network optimization and scaling are provided by combining optimization of traffic engineering and network topologies. Sets of minimal link capacities are determined based on the constraints of network topology, network demand, and failure modes. The optimization problem is reframed to allow for simultaneous optimization across all failure modes, reducing or eliminating overprovisioning in link capacity allocation by utilizing convex optimization solvers and techniques.

Abstract: The present technology is generally directed to updating a cloud network. This technology may include generating a set is inequalities based network topology information, shared risk link group information, demand information, and traffic solver parameters. The set of inequalities may also be based upon a model of traffic routing that is based on selection of routes based on dynamic and global traffic engineering. A capacity plan may also be generated based on the set of inequalities and at least one objective. The capacity plan may include a target capacity for each link of the plurality of links in the network.