Dependency-Aware Application Assigning and Scheduling in Edge Computing

Mobile-edge computing (MEC) is booming in recent years, as it is expected to fulfill the growing low-latency requirements of offloaded applications on large amounts of end devices. To ensure more applications finish before their deadlines, it is crucial to optimize the assignment of applications among various edge servers and the scheduling in a specific server, both of which have attracted widespread attention. As applications tend to become more complicated, each application may contain multiple interdependent tasks. We find that dependency in applications is another essential factor that would greatly impact the application's time consumption. Unfortunately, no prior study has given a comprehensive consideration involving all three factors, leading to a severe waste of computing and network resources, thus delays application processing. In this article, we model the dependencies among all tasks of an application as a directed acyclic graph (DAG) and jointly optimize the application assigning and scheduling problems to facilitate the execution of each application. To solve this NP-hard problem, we design a novel method named Daas. It first estimates the priority of each task and then effectively tackles the application assigning and scheduling problem based on this attribute in an online manner. Extensive evaluations show that Daas performs well in various experimental settings, enabling 20% more applications to meet their deadlines compared with the other baselines.

Automated summary

Mobile-edge computing (MEC) has experienced rapid growth in fulfilling the low-latency requirements of applications on end devices. This article introduces a novel method named Daas, which models the dependencies among tasks of an application and optimizes the assignment and scheduling to improve application execution. Experimental results demonstrate that Daas outperforms other methods, enabling more applications to meet their deadlines.