An input-centric performance model for computational offloading of mobile applications

Abstract Computational offloading frameworks are a widely-researched technology for optimising mobile applications through the use of cloud resources. Existing frameworks fail to fully account for the effect of input data characteristics on application behaviour. Comprehensive timing models exist in the literature, but feature information requirements and performance overheads that preclude use on mobile devices. In this paper, we propose a conceptual model for an input-centric view of application performance. Our proposed model simplifies the existing count-and-weights and pipeline timing models to significantly reduce their information and processing requirements, facilitating use on resource-constrained mobile devices. Our proposed model also utilises symbolic execution techniques to account for the effects of application input data characteristics. Validation with both synthetic and real device datasets demonstrates that our model provides an extremely accurate approximation of the count-and-weights model. Results demonstrate the predictive power of our model for linear execution paths with no loops or recursion. Further work with improved symbolic execution techniques may look to expand application of our proposed model to real-world use cases. The proposed input-centric approach provides a promising foundation for incorporating a deeper level of application-specific knowledge into computational offloading framework cost models, with the potential to contribute to higher-quality offloading decisions.