Homa: Online In-Flight Service Provisioning With Dynamic Bipartite Matching

• Published in: IEEE eTransactions on network and service management Vol. 19; no. 3; pp. 3174 - 3187
• Main Authors: Varasteh, Amir, Amiri, Saeed Akhoondian, Mas-Machuca, Carmen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-09-2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aircraft; Airplanes; Algorithms; Data centers; Delays; dynamic service allocation; Europe; Heuristic algorithms; Integer programming; Matching; mobile users; mobility-aware; Optimization; Passenger services; Polynomials; Provisioning; reconfiguration; Routing; Satellites; service migration; Service selection; Shortest-path problems
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2022.3167934

Airline companies are currently investigating means to improve in-flight services for passengers. Given emerging Air-to-Ground (A2G) communication technologies and the high desire of passengers for in-flight services, the servers providing in-flight services can be moved from the airplane to Data Centers (DCs) on the ground. In this scenario, network nodes (airplanes) demanding network services move over a ground core network. Therefore, the selection of DCs to connect to, as well as the underlying routing decisions are challenging. In particular, to keep a low-delay in-flight connection during the flight, airplanes connections can be reconfigured from a DC to another one, which comes at a delay cost. This paper presents a formal model for the in-flight service provisioning problem, also as an Integer Linear Program (ILP). We show that the problem is NP-hard and hence propose an efficient online heuristic, HOMA, which addresses the above challenges in polynomial time. HOMA models the problem as a dynamic matching with special properties, and then efficiently solves it by a transformation into the shortest-path routing problem. Our simulation results indicate that HOMA can achieve near-optimal performance and outperform the baseline and state-of-the-art algorithms by up to 15% while reducing the runtime from hours to seconds.