Migrating Large-Scale Air Traffic Modeling to the Cloud

Coordinating nationwide air traffic flow is a large-scale problem. The modeling process generally involves analysis of massive flight data, and its optimization involves computationally expensive algorithms. This paper uses Hadoop MapReduce, a big data processing model, to facilitate air traffic flow modeling and optimization, where computationally intensive tasks are automatically spread to Hadoop clusters for concurrent executions. The overall wall-clock time of computation is reduced. A nationwide traffic flow management problem that has been previously studied was restructured under the MapReduce framework. The problem aims at minimizing flight delays while respecting system capacities. Due to its temporal and spatial scope, the size of this problem grows to an extent where it is too big to be solved on standalone computers. Lagrangian relaxation was applied to decompose the original problem into a collection of solvable subproblems. The optimization proceeds in two iterative stages: solving subproblems and Lagrange multiplier updates. These two processes are encapsulated in the mapper and reducer functions, respectively. As a result, the optimization is automatically scheduled to run in parallel tasks. The cloud-based air traffic modeling and optimization were validated through running nationwide air traffic optimization instances on a small Hadoop cluster with six nodes. The modeling processing is eight times faster and the optimization is 16 times faster than that running on standalone computers.