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Learning-Based Queue-Aware Task Offloading and Resource Allocation for Space-Air-Ground-Integrated Power IoT
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DOI：10.1109/JIOT.2021.3058236.png)
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摘要
En 中文
Space-air-ground-integrated power Internet of Things (SAG-PIoT) can provide ubiquitous communication and computing services for PIoT devices deployed in remote areas. In SAG-PIoT, the tasks can be either processed locally by PIoT devices, offloaded to edge servers through unmanned aerial vehicles (UAVs), or offloaded to cloud servers through satellites. However, the joint optimization of task offloading and computational resource allocation faces several challenges, such as incomplete information, dimensionality curse, and coupling between long-term constraints of queuing delay and short-term decision making. In this article, we propose a learning-based queue-aware task offloading and resource allocation algorithm (QUARTER). Specifically, the joint optimization problem is decomposed into three deterministic subproblems: 1) device-side task splitting and resource allocation; 2) task offloading; and 3) server-side resource allocation. The first subproblem is solved by the Lagrange dual decomposition. For the second subproblem, we propose a queue-aware actor-critic-based task offloading algorithm to cope with dimensionality curse. A greedy-based low-complexity algorithm is developed to solve the third subproblem. Compared with existing algorithms, simulation results demonstrate that QUARTER has superior performances in energy consumption, queuing delay, and convergence.
Keyword:
Task analysis
Resource management
Servers
Optimization
Delays
Satellites
Decision making
Actor– critic
queue awareness
resource allocation
space– air– ground-integrated power Internet of Things (SAG-PIoT)
task offloading
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期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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引用论文
Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems支持无人机的无线移动边缘计算系统中的计算速率最大化
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