A Trust Evaluation Model for Cloud Computing

3.1. Simulation setup

To evaluate the performance of above model, we performed simulation experiments in Netlogo. In the simulation experiments, the CSPs and the CUs are independent.

The CSPs are classified into 3 types: good CSP, bad CSP and random CSP. Their respective proportions in all CSPs are 80%, 10% and 10%, and they provide different services are as follows.

The CUs are classified into 3 types: honest CU, malicious CU and random CU. The proportional distribution of each kind of the CUs is similar to the CSPs.

For all CSPs and CUs, the initial trust degree follows (0,0,1). This is to say, they are all unknown for the system at first. New interactions are continuously generated with an arrival rate 80 interaction per simulationtime step, between a random CSP and a random CU.

Table 1. Summarizes the parameters used in simulation experiments

3.2. The effectiveness of proposed model

At first, we evaluate the effectiveness of our model. The experimental result is as follows. Fig 2 reveals the changing of the trust degree for three kinds of entities. The credibility of the good/honest entities continues to grow as the steady accumulation of positive interactions. On the contrary, the credibility of the bad/malicious entities decreases as negative interactions. And the trust degree of the malicious entities has no changing when the distrust degree reaches a certain degree. The reason is that the entities would be considered to be malicious if its distrust degree is greater than the assumed threshold value. The entities would not be permitted to interact with any other entities. So the trust degree of the malicious entities would not change any more. For random entities, the change in behavior results in the change of the credibility of random entities. Besides, we can notice that the credibility of entities increases slowly and the incredibility of entities increase quickly. This is contributed to the feature of sliding windows. In sliding window mechanism, positive interactions are valid for a short period of time and negative interactions are valid for a long period of time. So the credibility of entities can increase by recent positive interactions slowly. But early negative interactions continue to have bad effects on the trust degree of entities so that the distrust degree of entities would increase quickly. It is accord with the feature of the trust in human society.

Fig. 2. The changing of trust degree for different entities

3.3. Anti-attack of the system

Success interaction rate is the ratio of successful interactions to overall interactions in the simulation time. It can reflect the anti-attack of the system in a certain extent. Thus we measure anti-attack of the system by success interaction rate. With a help of the trust computing based on evidence theory and sliding windows, we can identify the malicious entities efficiently. Thank to it, we can restrict the interaction of malicious entities further. It can help to increase the success interaction rate of the system. The experiment results are shown in Fig 3. Results show that the success interaction rate with trust computing is higher than that without trust computing. From Figure 3, we can see that the changing of success interaction rate is divided into two stages: decline stage and rise stage. The success interaction rate declines with malicious interactions at the beginning. After a time, the success interaction rate keeps rising. It is because that the system with trust computing has begun to identify the malicious entities and refuse to provide service for them. The result shows that trust computing can enhance the anti-attack of the system because it can help to the system correctly identifying the malicious entities.

Fig. 3. Success interaction rate