Multi-Objective Optimization for Joint Task Scheduling and Data Placement in Edge-based AIoT Systems: A Learning-Based Approach

Artificial Intelligence of Things (AIoT) systems are playing an important role in scenarios such as smart factories, smart healthcare, and smart logistics. Edge Computing reduces the network latency by pushing compute and storage resources near the IoT devices. However, the massive of data and task requests from IoT devices and datacenters raise the optimization requirement of schedule plans. Existing studies consider either task scheduling or data placement problems. They ignore the complex relationship between data and tasks leading to an increase the task completion time and energy consumption. Therefore, this paper first formalizes the joint task scheduling and data placement problem as a constrained multi-objective optimization model. Then, a Learns to Improve (L2I) algorithm is proposed, which is a reinforcement learning-based algorithm for task scheduling and data placement to minimize the task completion time and transmission energy consumption of IoT devices. In the L2I algorithm, we design a set of low-level improvement operators to generate new schedule plans to speed up the selection process of the optimal schedule plan. The simulation experiments show that the proposed algorithm effectively outperforms traditional strategies in solving task scheduling and data placement problems.