Name
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. |
Year | Venue | Field |
|---|---|---|
2017 | QSHINE | Serviceability (structure),Audit,Reputation system,Computer science,Computer security,Correctness,Evolutionary game theory,Computing systems,Reinforcement learning,Reputation |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Lu | 1 | 15 | 4.00 |
| Jingchao Yang | 2 | 0 | 0.34 |
| Haoran Gong | 3 | 0 | 0.34 |
| Mingchu Li | 4 | 469 | 78.10 |