TJCCT: A Two-timescale Approach for UAV-assisted Mobile Edge Computing

TL;DR

This work tackles computation offloading in UAV-assisted MEC under heterogeneous tasks and multi-timescale dynamics. It introduces TJCCT, a two-timescale optimization that combines price-incentive bargaining for on-demand resource allocation, many-to-one matching for offloading, and convex optimization for UAV trajectory control. Theoretical results establish stability, optimality, and polynomial-time complexity, while extensive simulations show that TJCCT outperforms baselines in system utility, processing rate, delay, and completion ratio, and remains effective under heavier workloads and larger numbers of MDs. The proposed hierarchical SDN-enabled framework enables adaptive, energy-efficient, and scalable aerial-terrestrial MEC services in dynamic environments with heterogeneity and mobility.

Abstract

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services in close proximity to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC servers, the demand-supply heterogeneity between MDs and MEC servers, the trajectory control requirements on energy efficiency and timeliness, and the different time-scale dynamics of the network. To address these issues, we first present a hierarchical architecture by incorporating terrestrial-aerial computing capabilities and leveraging UAV flexibility. Furthermore, we formulate a joint computing resource allocation, computation offloading, and trajectory control problem to maximize the system utility. Since the problem is a non-convex and NP-hard mixed integer nonlinear programming (MINLP), we propose a two-timescale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach for solving the problem. In the short timescale, we propose a price-incentive model for on-demand computing resource allocation and a matching mechanism-based method for computation offloading. In the long timescale, we propose a convex optimization-based method for UAV trajectory control. Besides, we theoretically prove the stability, optimality, and polynomial complexity of TJCCT. Extended simulation results demonstrate that the proposed TJCCT outperforms the comparative algorithms in terms of the system utility, average processing rate, average completion delay, and average completion ratio.

Figures (6)

• Figure 1: The architecture of computing resource allocation, computation offloading, and UAV trajectory control for UAV-assisted MEC system.
• Figure 2: System performance with time.
• Figure 3: System performance with average computation size.
• Figure 4: System performance with average computing resources of MEC servers.
• Figure 5: System performance with the number of MDs.

Theorems & Definitions (27)

• Theorem 1
• proof
• Theorem 2
• proof
• Lemma 1
• proof
• Definition 1
• Definition 2
• Lemma 2
• proof