PartFlow: A Visualization Tool for Application Partitioning and Workload Offloading in Mobile Edge Computing

In mobile edge computing (MEC), one optimization strategy for mobile applications is to offload heavy computing tasks to cloud and edge servers. Constructing partitioning algorithms involves modeling individual methods through static code profilers, but exploiting dynamic user-driven execution patterns is also crucial. This paper introduces PartFlow, an interactive visualization system that supports comprehensive analysis of mobile application components and aids researchers in developing partitioning and offloading algorithms using real human behavioral data. PartFlow collects application component data remotely through binary instrumentation of mobile applications. Interactive diagrams are designed to evaluate component performance and illustrate transition patterns using the collected data. Additionally, PartFlow integrates a deep learning (DL)-based approach for multi-step forecasting of component states to improve accuracy and user experience in algorithm design. A case study and user feedback demonstrate PartFlow’s effectiveness in assisting researchers and engineers in creating offloading strategies.