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Network in the Air

Published: 09 December 2019 Publication History

Abstract

Today, on-board passengers request Internet-based services, such as video streaming, Voice over IP, etc., at low cost. Many of those services have stringent QoS requirements, e.g., low end-to-end delay. To offer these services efficiently, some problems need to be solved jointly: placement of services (i.e., Virtual Machines (VMs)), at Datacenters (DCs) on the ground, assigning airplanes to services on DCs, and the routing from the flight to the associated VM considering the dynamic position of the flights over time. Further, dynamic VM migrations can be employed for guaranteeing the QoS requirements and/or improving resource utilization. In this work, we introduce and evaluate two heuristic solutions to jointly determine VM placement, routing, and migration decisions for flying airplanes with the objective of minimizing total operational costs. The first results indicate that while reducing the runtime from hours to seconds, the heuristics are able to achieve near-optimal solutions.

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Cited By

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  • (2022)Homa: Online In-Flight Service Provisioning With Dynamic Bipartite MatchingIEEE Transactions on Network and Service Management10.1109/TNSM.2022.316793419:3(3174-3187)Online publication date: Sep-2022
  • (2020)Figo: Mobility-Aware In-Flight Service Assignment and Reconfiguration with Deep Q-LearningGLOBECOM 2020 - 2020 IEEE Global Communications Conference10.1109/GLOBECOM42002.2020.9322493(1-7)Online publication date: Dec-2020

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Published In

cover image ACM Conferences
CoNEXT '19 Companion: Proceedings of the 15th International Conference on emerging Networking EXperiments and Technologies
December 2019
93 pages
ISBN:9781450370066
DOI:10.1145/3360468
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 09 December 2019

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CoNEXT '19 Companion Paper Acceptance Rate 34 of 52 submissions, 65%;
Overall Acceptance Rate 198 of 789 submissions, 25%

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View all
  • (2022)Homa: Online In-Flight Service Provisioning With Dynamic Bipartite MatchingIEEE Transactions on Network and Service Management10.1109/TNSM.2022.316793419:3(3174-3187)Online publication date: Sep-2022
  • (2020)Figo: Mobility-Aware In-Flight Service Assignment and Reconfiguration with Deep Q-LearningGLOBECOM 2020 - 2020 IEEE Global Communications Conference10.1109/GLOBECOM42002.2020.9322493(1-7)Online publication date: Dec-2020

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