Izabella Lokshina

Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis

Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.

The Power of Integrated Wireless and Mobile Communication Technologies and Their Impact on the Corporate World

This paper validates the progression and expected trends of integrated wireless and mobile communication technologies. Currently, these breakthroughs have allowed for high quality end-user experience with an acceptable level of usage and efficiency in business organizations. Forthcoming mobile broadband life creates new norms and expectations for end-users from the next business generation, who can utilize integrated wireless and mobile communication technologies in their full potential. Several scenarios from different domains illustrate the power of integrated wireless and mobile communication technologies and their impact on the corporate world. The models and scenarios show that the power of integrated wireless and mobile communication technologies is certainly in several orders of magnitude greater than the simple sum of their separate individual impacts.

Analysis of design requirements for electronic tags from a business perspective

The significance, which businesses and governments have placed on the radio frequency identification RFID and near field communication NFC technologies in electronic article surveillance, of cashless payment systems from credit cards and public transportation tickets to the consumer devices, healthcare, and e-government, involves reviewing requirements for the electronic tags. This paper defines the electronic tag life cycle that follows the product life cycle, considers representative business processes and interacting business entities, classifies the electronic tag usage methods, identifies the electronic tag risks, and summarises the design requirements for the electronic tags from a business perspective to be considered by the RFID and NFC developers in order to come up with better electronic tag designs.

Performance analysis of mobile communication networks with clustering and neural modelling

In this paper, we present the Universal Mobile Telecommunication System (UMTS) core network model with calls that belong to one of four service classes and arrive randomly. Arriving calls are granted service based on specific service class, required maximum and minimum bandwidth and available network resources. Performance of priority-based dynamic capacity allocation, suitable for the Wireless ATM (WATM) system is analysed. Scheduling of the ATM cell transmission in each uplink Time-Division Multiple-Access (TDMA) frame is based on a priority scheme. GoS (blocking probability) and QoS (throughput) parameters for Bandwidth Sharing Policy (BSP) are considered and Partial Overlap Link (POL) is implemented. The clustering procedure for the performance analysis of the mobile communication networks and the blocking probability and throughput measurements are introduced as Markov reward models enhanced with Vector Quantification (VQ) and neural modelling. The optimal link occupancy probability distribution is determined using neural network that was trained on the base of Kohonen rules. Simulation and numerical results are shown.

Accelerated rare event simulation with Markov chain modelling in wireless communication networks

The paper recommends an effective approach to estimate probability of buffer overflow in wireless communication networks. The buffer overflow probability in queuing systems is defined as a rare event and can be estimated using rare event simulation with Markov chains. Two-node queuing networks are considered in this paper; and an event of buffer overflow at the second node is studied. Two efficient rare event simulation algorithms, based on the importance sampling and cross-entropy methods, are developed and applied to accelerate the overflow probability simulation with Markov chain modelling. Numerical examples and simulation results are provided.

Study about effects of self-similar IP network traffic on queuing and network performance

The paper examines self-similar properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced self-similar models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behaviour of IP network traffic processes are developed and applied. Simulations and numerical results are shown and analysed. Furthermore, simulations with stochastic and long range dependent traffic source models are conducted; and efficient algorithms for buffer overflow simulation in finite buffer single server model under self-similar traffic load are developed and applied. Simulations and numerical results are shown and analysed.

Modeling and Simulation of Traffic with Integrated Services at Media Getaway Nodes in Next Generation Networks

This paper is devoted to modeling and simulation of traffic with integrated services at media gateway nodes in the next generation networks, based on Markov reward models MRM. The bandwidth sharing policy with the partial overlapped transmission link is considered. Calls arriving to the link that belong to VBR and ABR traffic classes, are presented as independent Poisson processes and Markov processes with constant intensity and/or random input stream, and exponential service delay time that is defined according to MRM. Traffic compression is calculated using clustering and learning vector quantification e.g., self-organizing neural map. Numerical examples and simulation results are provided for communication networks of various sizes. Compared with the other methods for traffic compression calculations, the suggested approach shows substantial reduction in numerical complexity.

Evaluation of the queueing network equilibrium based on clustering analysis and self-organising map

In this paper a new approach to the network local-balance estimation is presented on the base of clustering and neural modelling, in which the cluster centres can replace the steady states of the Markov chain. The equilibrium of queueing networks is a mean for performance evaluation of real communication systems presented as Markov chains. The new approach was developed for local-balance performance evaluation, which was based on calculations of the steady-state probability vector with use of neural modelling and clustering. The experimental results are obtained with use of clustering analysis and self-organising map, and the proposed method is compared with direct techniques on the base of obtained results.

Data Analysis Services Related to the IoT and Big Data: Strategic Implications and Business Opportunities for Third Parties

The Internet of Things IoT provides the tools for the development of a major, global data-driven ecosystem. When accessible to people and businesses, this information can make every area of life, including business, more data-driven. In this ecosystem, with its emphasis on Big Data, there has been a focus on building business models for the provision of services, the so-called Internet of Services IoS. These models assume the existence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated by any party. Different business models may support opportunities that generate revenue and value for various types of customers. This paper contributes to the literature by considering business models and opportunities for third-party data analysis services and discusses access to information generated by third parties in relation to Big Data techniques and potential business opportunities.

Modeling and simulation of self-similar wireless IP network traffic

The paper examines self-similar properties of real telecommunications network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. The algorithms for modeling fixed-length sequence generators that are used to simulate self-similar behavior of wireless IP network traffic are developed and applied. Numerical examples and simulation results are provided.