A predictive context aware collaborative offloading framework for compute-intensive applications

Abstract

The resourceful mobile devices with augmented capabilities around human pave the way for utilizing it as delegators for resource-constrained devices to run compute-intensive applications. Such collaborative resource sharing policy among mobile devices throws challenges like identifying competent alternatives for offloading and diminishing time consumption of pre-offload process to accomplish remarkable offloading. This paper presents a Mobile Cloud Computing framework with Predictive Context-Aware Collaborative Offloading Process (PCA-COP) that fixes these challenges through conductive alternative discovery. This context-aware discovery adapts a multi-criteria decision making model of Analytic Hierarchy Process (AHP) accompanied with Fuzzy categorization to rank the alternatives and classify them into Highly, Fairly, Less offload-suitable devices. Moreover, to make alternative selection optimal, a Dataset Curtailment enabled Artificial Neural Network (DCANN) prediction is incorporated on AHP-Fuzzy model, which truncates training dataset using Conditioned Stratified Sampling (CSS). The prototype framework is evaluated with mobile applications in the classroom under dynamic context environments.