Optimal Assignment of Augmented Reality Tasks for Edge-Based Variable Infrastructures

Abstract

In the last few years, the number of devices connected to the Internet has increased considerably; so has the data interchanged between these devices and the Cloud, as well as energy consumption and the risk of network congestion. The problem can be alleviated by reducing communication between Internet-of-Things devices and the Cloud. Recent paradigms, such as Edge Computing and Fog Computing, propose to move data processing tasks from the Cloud to nearby devices to where data is produced or consumed. One of the main challenges of these paradigms is to cope with the heterogeneity of the infrastructures where tasks can be offloaded. This paper presents a solution for the optimal allocation of computational tasks to edge devices, with the aim of minimizing the energy consumption of the overall application. The heterogeneity is represented and managed by using Feature Models, widely employed in Software Product Lines. Given the application and infrastructure configurations, our Optimal Tasks Assignment Framework generates the optimal task allocation and resources assignment. The resultant deployment represents the most energy efficient configuration at load-time, without compromising the user experience. The scalability and energy saving of the approach are evaluated in the domain of augmented reality applications.

Supporting the evolution of applications deployed on edge-based infrastructures using multi-layer feature models

“…The data structure presented, along with the information of the infrastructure, allows us to assure the functional and non-functional application requirements (as we widely explain in other works [9]). Thus, we define four functions that will be used in Module 2:…”

“…The adoption of a SPL approach provides the means to manage all the variability present both at the application and the infrastructure levels, and determine where is the best assignment of processing resources for optimal performance, and then weigh the options available in an edge-based infrastructure [8,9]. With this aim, we propose an approach based on multi layer (multiview) feature models (FM) to manage the configuration of both the application and the infrastructure independently.…”