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

Abstract

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 ${\mathcal{ O}}\left ({{{1 \mathord {\left /{ {\vphantom {1~V}} }\right. } V}} }\right)$ . Simulation results show that our proposed scheme achieves better performance when compared with existing schemes.