Efficient Task Offloading with Dependency Guarantees in Ultra-Dense Edge Networks

The last decade has witnessed the rapid development of Internet of Things (IoT). The IoT applications are becoming more and more computation-intensive and latency-sensitive, which pose severe challenges for the resource-constrained IoT devices. To empower the computational ability of the IoT systems, edge computing emerges as a promising approach which allows the resource-constrained devices to offload their tasks to edge servers. A major challenge, which has been overlooked by most existing works on task offloading, is the dependencies among tasks and subtasks, which can have a significant impact on the offloading decisions. Besides, the existing works often consider offloading tasks to specific edge servers, which may underutilize the edge resources in the ultra-dense edge networks. In this paper, we investigate the problem of dependency-aware task offloading in ultra-dense edge networks. Specifically, we explicitly analyze the task dependency as directed acyclic graphs (DAGs) and establish full parallelism between edge servers and IoT devices. We further formulate task offloading as a joint optimization problem for minimizing both task latency and energy consumption. We prove the problem is NP-hard and propose a heuristic algorithm, which guarantees the dependency among subtasks and improves the task efficiency. Simulation experiments demonstrate that the proposed work can effectively reduce the task latency in ultra-dense edge networks.