Teal: Learning-Accelerated Optimization of WAN Traffic Engineering
Zhiying Xu, Francis Y. Yan, Rachee Singh, Justin Chiu, Alexander M. Rush, Minlan Yu
Abstract
The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale. Existing acceleration strategies decompose TE optimization into concurrent subproblems but realize limited parallelism due to an inherent tradeoff between run time and allocation performance.
Topics & Concepts
Computer scienceCloud computingDistributed computingTraffic engineeringAccelerationScale (ratio)Optimization problemMathematical optimizationComputer networkAlgorithmQuantum mechanicsOperating systemMathematicsClassical mechanicsPhysicsSoftware-Defined Networks and 5GNetwork Traffic and Congestion ControlAdvanced Optical Network Technologies