Quantifying Influence of Strategies and Network Properties in Repairing Simultaneous Failures in Smart Grid

The behavior of networks under simultaneous failures has beensubject to various studies in the field of network science. However, themeasures used do usually not take into account the peculiarities of thestudied network. In this paper, we introduce a new measure for powergrids based on the balancing of power and on the accumulated cost ofenergy not supplied (CENS) during an outage. With the help of thismeasure we quantify the performance of seven repair strategies. We findthat both the choice of the right strategy and the topology of the powergrid has a major influence on the outage cost and the survivability ofthe power grid. Additionally, we appraise the potential of smart gridservices and conclude that both distributed energy resources (DER) anddemand response (DR) has a large potential to reduce the cost of anoutage

Empirical Data from Mobile and IP Telephony

Over the recent years enormous changes in the telecommunication services, techniques, regulations and markets have taken place. However, even with many new exciting services the telephony service is still popular. This paper provides empirical traffic data and observations of telephony traffic patterns in mobile and IP telephony. They are presented and compared with old telephony patterns from Public Switched Telephone Networks (PSTN). The question is whether the characterization of telephony traffic should be reconsidered because new technology and markets might have changed the service and usage. How is the telephony usage patterns influenced by technology changes from fixed to mobile phones, changes in quality from fixed-line phone to mobile and IP telephone, changes in tariffs from usage based to flat-rate subscriptions, and appearance of alternative message based communication means? This paper presents a comparison between recent mobile and IP telephony measurements and telephony measurement obtained nearly four decades ago. The traffic patterns are compared and significant changes in the daily and weekly traffic profiles are observed. In particular, the profile of international calls has significantly changed and does not resemblance any of the standard traffic profiles from ITU E.523. The busy hour call holding times are fitted a log-Normal distribution for domestic and Hyper Exponential for international calls. Furthermore, the average call holding times show significant variations over the day in flat-rate subscriptions. Finally, the results indicate that the Short Message Service (SMS) seems to serve as a supplement to phone calls, in particular in the evenings, which might change call holding time distribution and traffic intensities

Impact of SDN Controllers Deployment on Network Availability

Software-defined networking (SDN) promises to improve the programmability and flexibility of networks, but it may bring also new challenges that need to be explored. The purpose of this technical report is to assess how the deployment of the SDN controllers affects the overall availability of SDN. For this, we have varied the number, homing and location of SDN controllers. A two-level modelling approach that is used to evaluate the availability of the studied scenarios. Our results show how network operators can use the approach to find the optimal cost implied by the connectivity of the SDN control platform by keeping high levels of availability.Comment: Department of Telematics, NTNU, Tech. Rep., March 201

From QoS Distributions to QoE Distributions: a System's Perspective

In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system response time, paket loss, etc.) to estimated end user QoE values. Observable QoS conditions in the system may be assumed to follow a certain distribution, meaning that different end users will experience different conditions. On the other hand, drawing from the results of subjective user studies, we know that user diversity leads to distributions of user scores for any given test conditions (in this case referring to the QoS parameters of interest). Our previous studies have shown that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS), quantiles, probability of users rating "good or better", etc.) in a system under given conditions, there is a need to consider rating distributions obtained from user studies, which are often times not available. In this paper we extend these findings to show how to approximate user rating distributions given a QoS-to-MOS mapping function and second order statistics. Such a user rating distribution may then be combined with a QoS distribution observed in a system to finally derive corresponding distributions of QoE scores. We provide two examples to illustrate this process: 1) analytical results using a Web QoE model relating waiting times to QoE, and 2) numerical results using measurements relating packet losses to video stall pattern, which are in turn mapped to QoE estimates. The results in this paper provide a solution to the problem of understanding the QoE distribution in a system, in cases where the necessary data is not directly available in the form of models going beyond the MOS, or where the full details of subjective experiments are not available.Comment: 4th International Workshop on Quality of Experience Management (QoE Management 2020), featured by IEEE Conference on Network Softwarization (IEEE NetSoft 2020), Ghent, Belgiu