Abstract
Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exists a large optimization space to be explored. In this paper, we propose a trust-aware adaptive DAG tasks scheduling algorithm using the reinforcement learning and Monte Carlo Tree Search (MCTS) methods. The scheduling problem is defined using the reinforcement learning model. Efficient scheduling state space, action space and reward function are designed to train the policy gradient-based REINFORCE agent. The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities. Leveraging the algorithm’s capability of exploring long term reward, the proposed algorithm could achieve good scheduling policies while guaranteeing trusted tasks scheduled within trusted entities. Experimental results showed the effectiveness of the proposed algorithm compared with the classic HEFT/CPOP algorithms.
Highlights
Modern organizations are increasingly concerned with their trust management
We propose a trust-aware adaptive Directed Acyclic Graph (DAG) Tasks Scheduling algorithm using deep reinforcement learning and Monte Carlo tree search
(3) We proposed a trust-aware single-player Monte Carlo Tree Search (MCTS) method integrated with the DAG tasks scheduling algorithm
Summary
Modern organizations are increasingly concerned with their trust management. As the cloud computing paradigm prevails, more and more data security and trust issues are arising due to the public cloud infrastructures being under control of the providers but not the organizations themselves [1,2]. Scheduling security sensitive and non-sensitive tasks between the trusted and untrusted entities is one of the research challenges in the trust management When these tasks have sequential and parallel connections, the scheduling problem becomes further complicated in distributed heterogeneous computing systems. It is important to study the practical way of integrating trust management into the DAG tasks scheduling algorithm in distributed heterogeneous computing systems. We propose a trust-aware adaptive DAG Tasks Scheduling (tADTS) algorithm using deep reinforcement learning and Monte Carlo tree search. Action space and reward function are designed to train the policy gradient-based REINFORCE agent.The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities.
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