BD-TTS: A blockchain and DRL-based framework for trusted task scheduling in edge computing

Edge computing migrates tasks to the edge of the network for execution, which can provide users with lower latency and better quality of service (QoS). However, due to the complexity and dynamism of edge environments, many task scheduling algorithms in edge computing face challenges in achieving real-time scheduling, while there are also trust issues in heterogeneous and dynamic edge environments. To tackle these issues, we introduce a novel framework for trusted task scheduling in edge computing, leveraging blockchain and deep reinforcement learning (DRL) technologies, named BD-TTS. Specifically, we design a blockchainbased trust management scheme tailored for task scheduling in edge computing. The scheme uses blockchain to store, propagate, and update trust information in a decentralized manner to evaluate the trustworthiness of edge servers. In addition, to assign tasks to edge servers with higher trust values for execution, we introduce a DRL-driven task scheduling algorithm. The algorithm dynamically schedules tasks in real-time based on fluctuations in the trust values of edge servers. The experimental results show that compared to other baseline approaches, BD-TTS effectively reduces the number of tasks assigned to malicious edge servers by over 64.4%, reduces the average task response time by at least 13.9%, and improves the success rate by more than 14.7%.