2023 journal article
Graph-Represented Computation-Intensive Task Scheduling Over Air-Ground Integrated Vehicular Networks
IEEE TRANSACTIONS ON SERVICES COMPUTING, 16(5), 3397–3411.
author keywords: Air-ground integrated vehicular network; power allocation; task scheduling; undirected graph; vehicular cloud
TL;DR:
It is revealed that satisfying constraints related to graph task structure requires addressing the non-trivial subgraph isomorphism problem over a dynamic vehicular topology, and a decoupling approach is proposed by segregating template searching from transmission power allocation.
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UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(OpenAlex)
Source: Web Of Science
Added: November 13, 2023
This article investigates vehicular cloud (VC)-assisted task scheduling in an air-ground integrated vehicular network (AGVN), where tasks carried by unmanned aerial vehicles (UAVs) and resources of VCs are both modeled as graph structures. We consider a scenario in which resource-limited UAVs carry a set of computation-intensive graph tasks, which are offloaded to resource-abundant vehicles for processing. We formulate an optimization problem to jointly optimize the mapping between task components and vehicles, and transmission powers of UAVs, while addressing the trade-off between i) completion time of tasks, ii) energy consumption of UAVs, and iii) data exchange cost among vehicles. We show that this problem is a mixed-integer non-linear programming, and thus NP-hard. We subsequently reveal that satisfying constraints related to graph task structure requires addressing the non-trivial subgraph isomorphism problem over a dynamic vehicular topology. Accordingly, we propose a decoupling approach by segregating template searching from transmission power allocation, where a <italic>template</italic> denotes a mapping between task components and vehicles. For template search, we introduce a low-complexity algorithm for isomorphic subgraphs extraction. For power allocation, we develop an algorithm using <inline-formula><tex-math notation="LaTeX">$p$</tex-math><alternatives><mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq1-3270169.gif"/></alternatives></inline-formula>-norm and convex optimization techniques. Extensive simulations demonstrate that our approach outperforms baseline methods in various network settings.
