Abstract
The demand for running complex applications on smart mobile devices is rapidly increasing. However, the limitations of resources are restricting the development of intensive applications on these devices. The restrictions can be overcome by offloading the computation of an application in the powerful cloud servers. The objective of the computation offloading is to offload the parts of an application to the cloud server to minimize the response time, energy consumption and monetary cost of the application. Unlike prior work in computation offloading, this work considers the effect of parallel execution—on different devices (external parallelism) and on the different cores of a single device (internal parallelism). This work models each device as a multi-server queueing station. It uses genetic algorithm to determine the near-optimal offloading allocation. The results show that considering the effect of parallel execution yields better pareto-optimal solution for the allocation problem compared to excluding parallelism.
Highlights
Our work goes beyond existing approaches by considering parallel execution of tasks during offloading decision in contrast to others who primarily focused on sequential executions
Single-Site offloading: In this case, we assume that there is one cloud server d1 available for computation offloading so each task which is offloadable can be executed either in mobile device d0 or the cloud server d1
For each evaluation, Genetic Algorithm (GA) optimizes the problem based on three objectives, reduced response time, and energy consumption with no additional processing cost
Summary
The demand of mobile devices is continuously increasing in our daily lives through their new impressive features such as face recognition, augmented reality and interactive gaming. To the best of our knowledge, the current research in the models of code offloading is still performing the computation of an application among different resources sequentially by adding the execution time of all parallel tasks This assumption of sequential execution of the parallel tasks dramatically affects the prediction of overall response time and energy consumption of the mobile application. This research proposes a unique multi-site code offloading model by considering external and internal parallel execution of the application tasks with the consideration of multiple objectives, i.e. the response time, the energy consumption and the monetary cost to provide a user with more realistic and near-optimal code offloading allocation. The results of this analysis are incorporated into a research manuscript and submitted to the 26th IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2018) in Milwaukee, Wisconsin, USA [29]
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