# Systematic Evaluation of LibreSocial--A Peer-To-Peer Framework for Online Social Networks
## 5 Evaluation of Results
### Stability and Scalability  
To show the ability of the network to reach a stable state, we look at the systems reaction to both network growth and churn. From the results we can deduce how stable the network is and also make remarks on the ability to scale up and down. As the test for both growth and churn are carried out in the same experiment, it is easy to make observations and deductions on system behavior in both situations and contrast them. We discuss the results shown in Figure 9 in the following. Figure 8: Realistic user behavior. * Node count: Throughout the experiment, the node count is stable both in the growth and churn phases as depicted in Figure 9a. There is also seamless transition from the growth phase into the churn phase. As the network is able to support up to 1000 nodes, we can deduce that the system can be easily scale up. During the churn phases, we notice that despite massive churn, (of up to 50%), the network remains operational. * Routing table size: Figure 9b shows the maximum and average table size for a single node through the experimental duration. Observable is a corresponding adjustment of the routing table during the growth phase as well as during churn. The maximum value recorded is 39 nodes, but on average, the maximum is 30 nodes. Despite the relatively high growth (as well as churn) the routing table size does not significantly alter. If we take 250 nodes in the network as the base, hence a maximal value of 29 nodes and average of 22 nodes for the routing table size, the routing table maximally adds about 10 nodes and on average up to 9 nodes during the entire growth phase, and looses the same number of nodes during the entire churn phase. This means the the routing table adjusts accordingly to meet the demands of the network. * Leafset size: From Figure 9c, we observe that the leafset is maintained at 25 nodes. The experimental setting for the leafset size is 24 nodes. The additional value is because the first  
Figure 9: Stability and scalability analysis. and last value in the leafset is the node's own ID. As the network has more nodes than 25, it is expected that the average and maximum values for the leafset size will be the same. d) Stored data items and replicas: The replication factor for the experiment is set to 4. It would be therefore expected that the number of replicas should be at least 3 times more than the local items. However, each node also stores some replica, which become local to it. That notwithstanding, we note that generally, there is about twice as many replicas as locally stored data items. This holds true throughout the experimental duration, except at the last churn, for which the network is reduced by 50%. This may have caused a drastic reduction in the number of replicas, but nonetheless, some are still present. e) Messages sent: The maximum and average messages sent in a single node are shown in Figure 9e,f respectively. There is a steady rise in messages sent per node in the growth phase, evident in both figures, with the maximum value recorded being about 1.1 million messages by a single node corresponding to the end of the growth phase. On average, during the growth phase, the maximum value is about 25,000 messages, which is acceptable. During the churn phase, we observe different characteristics from the two figures. While the maximum messages per node shows a sharp decline to a value of about 200,000 messages, the average messages show a sharp increase to a value of 58,000 messages. This rise in average messages may be due to route adjustment queries as nodes are removed from the network and new routes have to be established. f) Message send rate: The maximum and average rates are shown in Figure 9g,h respectively. The maximum recorded message rate is about 430 messages/sec corresponding to the last increment of growth phase. Essentially, the maximum message rate during growth oscillates around 100 messages/sec.