Elastic Virtual Machine Scheduling for Continuous Air Traffic Optimization

Abstract

As we are facing ever increasing air traffic demand, it is critical to enhance air traffic capacity and alleviate humancontrollers' workload by viewing air traffic optimization as acontinuous/online streaming problem. Air traffic optimizationis commonly formulated as an integer linear programming(ILP) problem. Since ILP is NP-hard, it is computationallyintractable. Moreover, a fluctuating number of flights changescomputational demand dynamically. In this paper, we presentan elastic middleware framework that is specifically designedto solve ILP problems generated from continuous air trafficstreams. Experiments show that our VM scheduling algorithmwith time-series prediction can achieve similar performanceto a static schedule while using 49% fewer VM hours for arealistic air traffic pattern.