Task-Oriented Offloading and Resource Allocation in Edge Computing with Economic Constraint

The deployment of mobile edge computing (MEC) in unmanned aerial vehicle (UAV) networks can provide computing services for mobile users (MU) out of communication infrastructure coverage or during hot spots. However, UAVs typically have limited battery and computation capacities, which makes them hard to satisfy the delay requirement of a huge number of MUs. To address the challenging issue, in this paper we propose a UAV-aided collaborative edge computing framework, where UAVs are equipped with edge servers and deployed as aerial edge nodes. To this end, we formulate a joint resource allocation and offloading decision problem with economic constraint to minimize the average delay of MUs. To handle this non-deterministic polynomial hard (NP-hard) problem, we decouple the optimization problem into two sub-problems. The offloading decision problem involves the economic constraint, which is solved by our improved ant colony algorithm. The optimal resource allocation solution is obtained by using the Lagrange multiplier combined with the Karush-Kuhn-Tucker conditions. Simulation results validate that our proposed scheme can achieve better performance, where users achieve lower average delay under economic constraint.