Stochastic Petri Net-based performance evaluation of hybrid traffic for social networks system

A social network is a social structure made up of a set of social actors (such as individuals or organizations) and a set of the dyadic ties between these actors. By contrast, for the fixed time duration the size of digital video would be much bigger than that of digital sound. Consequently, providers of social network services can offer real-time chatting among users which could offer satisfactory experiences for users. As one of the most popular content-based social network services (SNS), chatting service plays an important role in current big data era. Also average data packets' transmission via networks is another significant traffic. So how to offer satisfactory Quality of Service (QoS) for users is the key problem which will be solved for SNS provider. For real time communication among users, end-to-end time delay seems to be critical in user's experience. Therefore modeling and evaluating social network systems is an important and urgent issue which offers quantitative basis of SNS with high quality for users. For social network system, the scalability and robust are important for both service provider and users under the circumstance of a large number of users. On the basis of performance evaluation of social network system of one user case, we construct the SPN model and conduct numerical analysis to discover and report the performance with the addition of users. By taking hybrid traffic containing voice and data into account, this paper constructed a Stochastic Petri Net (SPN) model for data and ON/OFF voice traffic for social network system. Then, average time delay of the systemwas analyzed and model-based simulation is conducted with Stochastic Petri Net Package (SPNP) 6.0. Furthermore, for different parameters of burst rate, idle rate, number of data packets, traffic load and buffer size, variation trends on average time delay are derived thereby. On the basis of the work in this paper, further research on heterogeneous objects of social network systems can be carried on. & 2016 Elsevier B.V. All rights reserved.