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AoI-Aware Scheduling for Air-Ground Collaborative Mobile Edge Computing
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DOI：10.1109/TWC.2022.3215795.png)
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摘要
En 中文
As a way of providing users flexible computing services, networks exist that can make full use of air and ground computing resources. Such networks are called air-ground collaborative mobile edge computing (AGC-MEC) networks. AGC-MEC supports numerous emerging real-time applications for which timely computed results are critical. Researchers have developed a novel metric age of information (AoI) that can capture the freshness of computed results. This is the first paper to study the problem of AoI-aware scheduling for Air-ground Collaborative mobile Edge computing (i.e., IACE). So as to minimize the weighted AoI of all the terrestrial user equipments (UEs), we have jointly optimized task scheduling, computing resource allocation, and unmanned aerial vehicle (UAV) trajectory taking into account the constraints on the computing resources and the available energy of the UAV. The formulated problem, which is a challenge to solve, is a mixed-integer nonlinear programming (MINLP) problem. To obtain an effective solution, we propose an iterative algorithm based on the alternating optimization approach, which entails dividing the considered problem into three subproblems. Extensive simulations show that the proposed algorithm can achieve lower weighted AoI than five benchmark algorithms, while satisfying the resource constraints. Furthermore, simulation results demonstrate two interesting insights. First, the introduction of an aerial MEC server facilitates a flexible offloading design of the UEs which is critical to guaranteeing the freshness of computed results. Second, by optimizing the scheduling, the proposed design can unlock performance gains, especially in the resource-limited regime.
Keyword:
Task analysis
Processor scheduling
Energy consumption
Autonomous aerial vehicles
Servers
Resource management
Trajectory
Age of information (AoI)
air-ground collaborative
mobile edge computing (MEC)
task scheduling
resource allocation
trajectory optimization
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
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