Google Inc.

Matthew Johnston

Anurag Sharma

Wenjie Jiang

Rodolfo Enrique Alvizu Gomez

Jinjie He

Min Wen

Ray Bennett

Robert Nygaard

Robert Withrow

The subject matter described herein provides systems and techniques for a network planning and optimization tool that may allow for network capacity planning using key network failures for an arbitrary pair of network topology and demands. Performing network capacity planning with key network failures, instead of using other techniques, may avoid over-building the topology of a network. In particular, key network failures may be generated from the probabilistic failures, and the impact of these failures on a network may be computed. Expected flow availability SLO or a function thereof may be computed, using this information, and used by the tool to design a robust network. With an embedded flow availability calculation and updated risk framework, the capacitated cross-layer network topologies output by the tool may meet network demands/flows with their respective SLO type at the lowest cost.

Availability SLO-aware network optimization

Methods, systems, and apparatus, including computer-readable storage media, optimizing interior gateway protocol (IGP) metrics using reinforcement learning (RL) for a network domain. The system can receive a topology (G) of a network domain, a set of flows (F), and an objective function. The system can optimize, using reinforcement learning, the objective function based on the received topology and the one or more flows F. The system can determine updated IGP metrics based on the optimization of the objective function. The IGP metrics for the metric domain may be updated with the updated IGP metrics.

Assigning routing paths based on interior gateway protocol metric optimization

Determining an upgrade path from a starting topology to a target topology of a network is computationally intense and does not guarantee a steadily increasing usable capacity of the network at each stage within the upgrade path. The disclosed technology allows for a sequence of stages related to network upgrades to be generated. The technology ensures that networks can be upgraded in a sequential manner, where each step in the sequence does not violate service level objectives related to the network, ensures operational continuity of the network by users of the network, and ensures that the available network resources increase as the sequential upgrades are rolled out. The pathway determined is determined in a computationally efficient manner.

Sequenced capacity deployment for WAN and datacenter networks

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Palo Alto, CA, United States of America

Matthew Johnston