Article: Elastic allocation and recovery of airport computing resources in response to strong disturbances in flight flows Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.17 pp.48 - 66 Abstract: To address the problems of slow response and large-scale computational power collapse of airport computing nodes under strong disturbances, this paper proposes a novel computing resource joint scheduling system with dynamic perception and elastic recovery capabilities. This system first constructs a network graph structure covering flight status changes and physical space distribution, and then uses a reinforcement learning algorithm that allows computers to continuously try and error to learn the optimal allocation strategy. Simulation experiments show that in the face of extreme flight flow disturbances, this system not only reduces the overall processing delay of computing tasks by 14.2%, but also shortens the adaptive recovery time of the overall computing resource allocation from an average of 28 seconds to 22 seconds after some computing nodes temporarily fail due to overload, effectively ensuring the continuous operation of core business data of the airport under adverse conditions. Inderscience Publishers - linking academia, business and industry through research

Title: Elastic allocation and recovery of airport computing resources in response to strong disturbances in flight flows

Authors: Wenliang Wang

Addresses: Management College, Guangzhou Civil Aviation College, Guangzhou, 510403, China

Abstract: To address the problems of slow response and large-scale computational power collapse of airport computing nodes under strong disturbances, this paper proposes a novel computing resource joint scheduling system with dynamic perception and elastic recovery capabilities. This system first constructs a network graph structure covering flight status changes and physical space distribution, and then uses a reinforcement learning algorithm that allows computers to continuously try and error to learn the optimal allocation strategy. Simulation experiments show that in the face of extreme flight flow disturbances, this system not only reduces the overall processing delay of computing tasks by 14.2%, but also shortens the adaptive recovery time of the overall computing resource allocation from an average of 28 seconds to 22 seconds after some computing nodes temporarily fail due to overload, effectively ensuring the continuous operation of core business data of the airport under adverse conditions.

Keywords: flight flow disturbance; computational resource allocation; multi-agent reinforcement learning; spatio-temporal graph network.

DOI: 10.1504/IJRIS.2026.155023

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.17, pp.48 - 66

Received: 01 Apr 2026
Accepted: 18 May 2026
Published online: 22 Jul 2026 *

Title: Elastic allocation and recovery of airport computing resources in response to strong disturbances in flight flows

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