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Joint Task Offloading and Resource Allocation for Obtaining Fresh Status Updates in Multi-Device MEC Systems

机译：联合任务卸载和资源分配，用于获取多设备MEC系统中的新状态更新

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摘要

To improve the operational efficiency of smart city, smart devices extract informative status updates from sampled image and video data to intelligently monitor the surroundings. Mobile edge computing (MEC) is considered as an emerging technology to provide energy-constrained devices with enhanced computation capability by offloading tasks to nearby servers. In such circumstance, the freshness of obtained status updates is critical to system performance, which can be characterized by the concept of age of information (AoI). Due to resource contention among multiple devices, the problem of how to maintain the timeliness of task executing is not trivial. In this paper, we are interested in minimizing the age of obtained status updates by jointly optimizing task generation, computation offloading as well as communication and computational resource allocation under the average energy constraint at each device. To tackle the time couplings of task generation and computation offloading decisions, we leverage the Lyapunov optimization technique to convert the long-term stochastic optimization problem into a per-time slot deterministic optimization problem. In each time slot, an online algorithm is proposed to determine the task offloading and computation offloading strategy. Moreover, we theoretically prove that the proposed algorithm can be arbitrarily close to the optimal performance with the gap of O(1/V). Simulation results show that our proposed scheme achieves better performance when compared with existing schemes.
机译：为了提高智能城市的运营效率，智能设备从采样图像和视频数据中提取信息状态更新，以智能监控周围环境。移动边缘计算（MEC）被认为是新兴技术，以通过将任务卸载到附近的服务器来提供具有增强的计算能力的能量受限设备。在这种情况下，所获得的状态更新的新鲜度对系统性能至关重要，其能够以信息年龄（AOI）的概念为特征。由于多个设备之间的资源争用，如何保持任务执行的时间性的问题并不重要。在本文中，我们有兴趣通过在每个设备的平均能量约束下共同优化任务生成，计算卸载以及通信和计算资源分配来最大限度地减少获得的状态更新的年龄。为了解决任务生成和计算卸载决策的时间耦合，我们利用Lyapunov优化技术将长期随机优化问题转换为每次时隙确定性优化问题。在每个时隙中，提出了一种在线算法来确定任务卸载和计算卸载策略。此外，我们理论上证明了所提出的算法可以任意接近具有O（1 / V）的间隙的最佳性能。仿真结果表明，与现有方案相比，我们所提出的方案达到更好的性能。

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