A Context-Aware Approach to Scheduling of Multi-Data-Source Tasks in Mobile Edge Computing

The multi-data-source tasks are prevalent in the Mobile Edge Computing environment due to the distributed data storage of continuous data streams sent by various mobile devices. Many existing studies on context-aware task scheduling in MEC mainly aim to reduce energy consumption and improve performance by computation offloading among the MEC nodes. However, task context implies not only the locations of data sources but also the receivers of the result of task execution. Therefore, in this paper, we build a context-aware model for the multi-data-source tasks to analyze the objectives of task execution and data management in MEC and propose a related approach to scheduling the multi-data-source tasks. In the proposed approach, the task and environment contexts are used to determine the data sources involved, generate the task execution strategy, and resolve the target receivers. We discuss the task scheduling scheme in detail, including its architecture, metadata and data management, context-aware scheduling algorithm, and task offloading. We evaluate the feasibility and effectiveness of the proposed approach on a dataset of taxi trajectories in a city. The results illustrate that the proposed approach can effectively schedule the context-aware multi-data-source tasks in a MEC environment.