Dynamic Partitioning and Task Scheduling for Complex Workflow Healthcare Application in Mobile Edge Cloud Architecture

Abstract

In this paper, we are investigating the energy consumption of mobile devices and cloud resources (i.e., virtual machines) while viewing the application partitioning and task scheduling problem in the offloading system. However, offloading system enables mobile device to separate the application tasks into local execution parts and cloud server execution parts. To minimize the energy consumption of mobile devices along with cloud resources in the considered problem we have nominated a new dynamic application partitioning task scheduling (DAPTS) algorithm. Since, DAPTS aims is to efficiently dynamically partitioning the application into tasks and schedule them on the mobile device and cloud resources that one may minimize energy consumption of both mobile devices as well as cloud resources simultaneously. Experimental results show that propose DAPTS outperforms as compared to the baseline approaches.