Energy-Aware Graph Task Scheduling in Software-Defined Air-Ground Integrated Vehicular Networks

TL;DR

This paper proposes an energy-aware graph-based task scheduling method for SD-AGV networks, optimizing UAV task completion time, energy use, and data exchange costs through a novel decoupled algorithm approach.

Contribution

It introduces a new graph-based task scheduling framework with an efficient subgraph isomorphism search and power allocation algorithms for SD-AGV networks, addressing NP-hard challenges.

Findings

Proposed method outperforms benchmarks in simulation.

Efficient subgraph search algorithm reduces computational complexity.

Power allocation improves energy efficiency and task performance.

Abstract

The Software-Defined Air-Ground integrated Vehicular (SD-AGV) networks have emerged as a promising paradigm, which realize the flexible on-ground resource sharing to support innovative applications for UAVs with heavy computational overhead. In this paper, we investigate a vehicular cloud-assisted task scheduling problem in SD-AGV networks, where the computation-intensive tasks carried by UAVs, and the vehicular cloud are modeled via graph-based representation. To map each component of the graph tasks to a feasible vehicle, while achieving the trade-off among minimizing UAVs' task completion time, energy consumption, and the data exchange cost among moving vehicles, we formulate the problem as a mixed-integer non-linear programming problem, which is Np-hard. Moreover, the constraint associated with preserving task structures poses addressing the subgraph isomorphism problem over dynamic vehicular topology, that further complicates the algorithm design. Motivated by which, we propose an efficient decoupled approach by separating the template (feasible mappings between components and vehicles) searching from the transmission power allocation. For the former, we present an efficient algorithm of searching for all the isomorphic subgraphs with low computation complexity. For the latter, we introduce a power allocation algorithm by applying $p$-norm and convex optimization techniques. Extensive simulations demonstrate that the proposed approach outperforms the benchmark methods considering various problem sizes.