Delay Sensitive Application Partitioning and Task Scheduling in Mobile Edge Cloud Prototyping

Abstract

Mobile Edge Cloud Prototype (MEC) allows resource-constraint mobile device to execute computation intensive and delay sensitive applications (i.e., Augmented Reality, Healthcare, Virtual Reality and so on) in a collaborative manner. The offloading system in the MEC is a technique which divides the application execution into local execution and cloud execution in order to augment the user quality of experience (QOS). In this paper, we are formulating an application partitioning and task scheduling problem for delay sensitive healthcare application. To cope up with the aforementioned problem we have proposed a novel Dynamic Aware Application Partitioning Task Scheduling Algorithm (DAPTS) which determine the following phases: (i) partition the application into local and remote execution via static analysis and profiling technology, (ii) schedule a local task on the mobile device, (iii) schedule the cloud tasks via the wireless channel band, (iv) schedule the offloaded tasks on the cloud resources. Simulation results show that propose DAPTS outperforms as compared to baseline approaches in the context of average response time of the application.