Application-aware Task Scheduling in Heterogeneous Edge Cloud

Abstract

As the acceleration gaining for utilizing edge computing in various IoT applications, the demands of effective task scheduling algorithms are rising alarmingly. Some of the real-time applications (e.g., self-driving cars, AR/VR apps) requires real-time responses. On the other hand, for the applications (such as deep learning algorithms and neural networks) demand powerful edge resources. However, most of the studies only focus on low latency improvement and lacks to provide efficient task scheduling. As a result, edge computing paradigm requires a new approach to deal with different applications. In this paper, we propose an adaptive application-aware task scheduling algorithm for running over heterogeneous edge cloud. The proposed scheduling algorithm provides not only the QoS of the applications but also increases the performance of the overall scheduling and utility of edge resources. We conduct an extensive experimental study to show the efficiency of our algorithm. From this research, we improve the overall performance of the task scheduling, considering both task heterogeneity and edge heterogeneity and to maximize the edge resource utilization effectively.