A Novel Hybrid Model for Task Dependent Scheduling in Container-based Edge Computing

Abstract

In traditional edge computing, the task from the Internet of Things (IoT) is usually offloaded to edge server. It will be uploaded to the remote cloud if the edge server cannot process it. A task can be processed on the server, only if the server has configured the corresponding function program. However, each edge server can only configure a small number of functions due to the limited computing, storage, and bandwidth resources. Moreover, modern tasks from IoT devices become more and more diverse, which are also accompanied by complex dependencies. It increases the processing time overhead to the task processed in remote cloud due to huge transmission delay. In this paper, we design a container-based edge computing system, where a task can be executed on a server only if the server has configured the corresponding container, if not the server can fetch it from other edge servers or remote cloud. Based on the system, we propose a novel hybrid model, called CBASGA, with the aim to minimize the job complete time, which combines Chaos-based Beetle Antennae Search and Genetic Algorithm. Our experimental results show that the designed system reduces the average job completion time by 4.2% compared with the comparison system, and CBASGA reduces the average job completion time by at least 21.7% compared with baselines.