Research Article
Classification-Based Reputation Mechanism for Master-Worker Computing System
@INPROCEEDINGS{10.1007/978-3-319-78078-8_24, author={Kun Lu and Jingchao Yang and Haoran Gong and Mingchu Li}, title={Classification-Based Reputation Mechanism for Master-Worker Computing System}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 13th International Conference, QShine 2017, Dalian, China, December 16 -17, 2017, Proceedings}, proceedings_a={QSHINE}, year={2018}, month={4}, keywords={Node classification Reinforcement learning Reputation system}, doi={10.1007/978-3-319-78078-8_24} }- Kun Lu
Jingchao Yang
Haoran Gong
Mingchu Li
Year: 2018
Classification-Based Reputation Mechanism for Master-Worker Computing System
QSHINE
Springer
DOI: 10.1007/978-3-319-78078-8_24
Abstract
Master-worker computing is a parallel computing scheme, which makes master and worker collaborate. Due to its high reliability availability and serviceability, it is widely used in scientific computing fields. However, lack of cooperation and malicious attack in Master-worker computing can greatly reduce the efficiency of parallel computing. In this paper, we consider a reputation system based on individual classification to inducing worker nodes returning true answer and separate malicious worker nodes. By introducing reinforcement learning, rational workers are induced to behave cooperatively and auditing rate of the master decreases. Our model is based on evolutionary game theory. Simulation results show that our reputation system can not only effectively guarantee eventual correctness, separate malicious worker nodes, but also save the master node’s auditing cost.
