# Research on Blockchain Data Availability and Storage Scalability
## 5 Model Validation
### The Impact of Network Transmission Performance on Data Availability  
Firstly, we verified the applicability of the DAMMB for degraded nodes. We select SSFL, with a 10% proportion of SPV nodes and total numbers of nodes set to 10, 100, and 500, consecutively. The number of routing nodes was set to 5, and the offline probability was \(e^{-0.01t}\). Then, we requested 1000 blocks and plotted the theoretical and practical curves of data availability under different numbers of nodes, as shown in Figure 7.  
The experimental results suggest that the theoretical data availability is in good agreement with the actual changes. When the number of nodes is small, the data availability remains unchanged due to the failure to obtain target data. However, as the number of nodes increases, the number of situations where data availability remains unchanged due to not obtaining target data diminishes. It is worth noting that the theoretical change curve of data availability only represents the most likely trend of change, and that there may be some differences in the theoretical change curve due to actual probability differences. The time when the actual data availability reaches 100% in Figure 7b is advanced, and the reason for this situation is that the frequency of not obtaining the target data is relatively low. As the number of nodes increases, the theoretical change curve of data availability gradually matches the actual change curve, and thus, the DAMMB for degraded nodes has certain applicability for measuring data availability.  
Figure 6: Time taken to achieve 100% data availability.  
According to BitcoinVisuals [24], the average number of hops from one node to another in Bitcoin networks in the past three months was 9. We set the number of hops in the simulated blockchain to 9 and the online probability of routing nodes to 1, and used the normal distribution N (0,0.64). We then requested 1000 blocks and obtained the changes in data availability as shown in Figure 8.  
Figure 8: Impact of network transmission performance on data availability.  
Figure 7: Changes in data availability under different number of nodes. (a) 10 nodes; (b) 100 nodes; (c) 500 nodes.  
The experimental results illustrate that curve 1 represented the control group. The addition of a routing node prolonged the time from requesting information to receiving data. The data availability increased linearly after receiving data. Curve 2 represents the routing nodes that conformed to the actual online situation. The trend of curve 2 shows that the routing node initially went offline, causing data to be transmitted through the backup routing node and resulting in a delay time. Horizontally, when receiving data, curve 2 experiences a certain time delay when it reaches the same data availability as curve 1. Vertically, at the same time, the data availability level represented by curve 2 is lower than that represented by curve 1.  
Based on the experimental results above, it can be concluded that network transmission performance in blockchain has a significant impact on the level of data availability. In actual blockchain systems, it is not possible to guarantee that full nodes are always online. Therefore, when aiming to achieve a certain data availability level without considering the offline situations of nodes, it is necessary to consider the impact of delay time and adjust the time limit accordingly.