Deadline-aware Scheduling in Cloud-Fog-Edge Systems

Abstract

Nowadays robots are one of the most important technologies and face recognition is crucial for human-robot interaction. Face recognition has direct benefits in the commercial and law enforcement fields and it enables robots to perform a wide variety of roles such as assistance, search and rescue, military and so on. In this paper we propose a Cloud-Edge architecture for robots that enables the use case of face recognition in deadline constrained environments. We formulate a mathematical model for a Data Capsule which represents structured units of data in a time series. We design the components on each layer of the architecture. We propose a deadline aware scheduler in the Fog that acts as a proxy for the processing platforms in the Cloud and we design two face recognition applications, one in the Edge for robots that is implemented with eigenfaces and one in the Cloud for the processing platforms with deep neural networks (DNN). We evaluate the performance of the face recognition applications by running a workload that consists of a well-known labelled image data set. We test the ability of the Fog scheduler to launch jobs on time when strict deadlines are in place and it runs a heavy workload of jobs.