A Blockchain-based Profiling System for Exploring Human Factors in Cloud-Edge-End Orchestration

In mobile edge computing (MEC), application partitioning is one of the most effective measures to leverage computing resources. Due to the user's unpredictable behavior pattern, which is an indispensable factor affecting the performance of an offloading system, traditional partitioning algorithms, considering only purely technical QoS, are no longer enough to meet the increasing concern for the user experience of mobile applications. In this paper, in order to explore human factors in modeling partitioning algorithms for cloud-edge-end orchestration under a safe and trusted environment, we present a blockchain-based profiling system to collect behavioral data from several invited subjects. For discovering user-driven relations of method-level components, we propose a clustering algorithm framework to process each subject's data. Based on the disparate results, we illustrate a case study to prove the usefulness of the system and the data for the orchestration by analyzing the variance of user behavior and the feasibility of applying human factors to the partitioning algorithm.