The traditional network architecture cannot meet the growing needs of increasingly diverse network applications. The new generation network architectures, software defined network (SDN) which have features of high openness, flexibility, scalability and high controllability have widely been studied. However, a series of new problems are also produced in this new architecture, such as security problem resulted from the high openness, and performance problem resulted from the high flexibility. Currently, the researchers are mainly concentrated in the implementation and standardisation of SDN. There is no good approach to evaluate and optimise the system performance. This studies the performance problems unresolved in SDN research area, and proposed an idea and method to analyse the control channel of ForCES (Forwarding and Control Element Separation)‐based SDN by using the stochastic network calculus theory. First, the architecture of ForCES‐based SDN is introduced. Then, the performance model of the channel is given. Based on the model, an optimisation method to performance is detailed. This study also gives a simulation by using NS‐2 (Network Simulator, Version 2) to verify the correctness and reliability of the performance model.

With the progress of wearable sensor technologies, more wearable health sensors have been made available on the market, which enables not only people to monitor their health and lifestyle in a continuous way but also doctors to utilise them to make better diagnoses. Continuous measurement from a variety of wearable sensors implies that a huge amount of data needs to be collected, stored, processed and presented, which cannot be achieved by traditional data processing methods. Visualisation is designed to promote knowledge discovery and utilisation via mature visual paradigms with well‐designed user interactions and has become indispensable in data analysis. In this study the authors introduce the role of visualisation in wearable sensor‐assisted health analysis platforms by case studies of two projects funded by the European Commission: MyHealthAvatar and CARRE. The former focuses on health sensor data collection and lifestyle tracking while the latter aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The roles of visualisation components including timeline, parallel coordinates, map, node‐link diagrams, Sankey diagrams, etc. are introduced and discussed.

Quality of experience (QoE) has been a user-centric performance metric for multimedia communications. Existing communication protocols over heterogeneous networks are mostly based on quality of service, which ignores the user satisfaction and subjective evaluation. In this paper, the authors propose a novel QoE-aware multimedia communication framework for heterogeneous wireless networks. Specifically, the authors make three main contributions in this paper: (i) a general QoE model has been established by jointly characterising the multimedia applications and heterogeneous networks, (ii) the issue of multimedia communications over heterogeneous networks has been transformed as a stochastic optimisation problem, and (iii) the optimal solution is provided by designing a QoE-aware multimedia scheduling scheme. Importantly, the proposed scheme is simply enough for online implementation. Numerical simulations have demonstrated the efficiency and robustness of the proposed scheme.

At present, research on users’ preferences is becoming increasingly widespread. Traditional research used a simple cosine similarity algorithm to calculate the similarity between users and just focused on one factor. So, a more complete method is described for researching preferences by considering more factors of the network to analyse data. Two personalisation recommendation algorithms are given based on users’ preferences. These two algorithms mainly work on two aspects: one is the content-based recommendation algorithm. It uses a cosine similarity algorithm to calculate the similarity between users, uses LDA to obtain improvement and uses screens according to a user impact factor. The other is a relation-based recommendation algorithm. It uses the PersonalRank algorithm to calculate the similarity, and uses the weight of users’ communication behaviours to improve the algorithm. Finally, micro-blog users’ data are analysed in detail, and the consequences of the two recommendation methods are fused based on the content of the results and recommendations. As a result, we obtained more accurate recommendation results and verified the feasibility of the algorithm by experimental analysis.

With the rapid growth in the number of mobile phone users, mobile payments have become an important part of mobile e‐commerce applications. Secure payment systems directly affect the security of e‐commerce systems. This study proposes an anomaly detection mechanism supported by an information entropy method combined with neural network to improve mobile payments security. As the entropy value is sensitive and have much difference between normal and abnormal traffic flow in the mobile payment system, the abnormal traffic data will be detected. The simulation result shows that it can realise the effective monitoring of abnormal flow in the mobile payment system.

Cloud-based virtual networking environments are required to provide fine-grained quality of service (QoS) control without sacrificing scalability. However, no single approach can currently achieve these two goals simultaneously. FlowVisor is a building block to virtualise networks with fine-grained QoS support; however, scalability issues caused by the OpenFlow protocol and the centralised control model are a major concern. This study introduces a distributed FlowVisor (DFVisor) platform to address these scalability issues. The proposed DFVisor uses a layered overlay mechanism to improve network addressing space and switch capacity. DFVisor uses a distributed synchronised two-level database system with a synchronisation mechanism to enable the centralised control functions in the current FlowVisor platform in distributed control modules within the virtual network controllers. Therefore it removes a single point of failure in the network and reduces the flow setup latency without sacrificing the centralised network configuration and management capabilities. More importantly, the proposed DFVisor platform enables an advanced push-based flow setup and statistics collection mechanism to address scalability issues caused by the current pull-based flow setup and statistics collection method. A DFVisor prototype and an evaluation of this distributed synchronised two-level database are presented, and key issues for future research are discussed.

OpenFlow (OF) is one of the most widely used protocols for controller‐to‐switch communication in a software defined network (SDN). Performance analysis of OF‐based SDN using analytical models is both highly desirable and challenging. There already exists a very elegant analytical model based on M/M/1 queues to estimate the packet sojourn time and probability of lost packets for the case in which a controller is responsible for only a single node in the data plane. However the literature falls short when it comes to the multiple node case, i.e. when there is more than one node in the data plane. In this work we propose a model to address this challenge by approximating the data plane as an open Jackson network with the controller also modeled as an M/M/1 queue. The model is then used to evaluate the system in the light of some of the metrics, such as; how much time a packet spends on average in an OF‐based network and how much data we can pump into the network given the average delay requirements. Finally the PDF and the CDF of the time spent by the packet in an OF‐based SDN for a given path is derived.

In this study, the authors investigate epidemic failure spreading in large-scale transport networks under generalised multi-protocol label switching control plane. By evaluating the effect of the epidemic failure spreading on the network, they design several strategies for cost-effective network performance improvement via differentiated repair times. First, they identify the most vulnerable and the most strategic nodes in the network. Then, via extensive event-driven simulations they show that strategic placement of resources for improved failure recovery has better performance than randomly assigning lower repair times among the network nodes. They believe that the event-driven simulation model can be highly beneficial for network providers, since it could be used during the network planning process for facilitating cost-effective network survivability design.

Packet level measurement is now routinely used to evaluate the loss and delay performance of broadband networks. In active measurement, probe packets provide samples of the loss and delay and from these samples the performance of the traffic as a whole can be deduced. However this is prone to errors: inaccuracy due to taking insufficient samples, self-interference due to injecting too many probe packets, and possible sample-correlation induced bias. In this paper we consider the optimisation of probing rate by treating all measurements as numerical experiments which can be optimally designed by using the statistical principles of design of experiments. We develop an analytical technique that quantifies an overall utility function associated with: (i) the disruption caused per probe packet, (ii) the bias and (iii) the variance as a function of the probing (sampling) rate. Our numerical results show that the optimal probing rate depends strongly on what parameter the network engineer seeks to measure.