In this paper, a fuzzy logic controller is proposed for an application of HVDC link to stabilize the frequency oscillation in a parallel AC – DC interconnected power systems. When an interconnected AC power system is subjected to a load disturbance, system frequency may be considerably disturbed and becomes oscillatory. By utilizing the system interconnections as the control channels of HVDC link, the tie line power modulation of HVDC link through interconnections is applicable for stabilizing the frequency oscillation of AC system. The conventional Integral controller does not yield adequate control performance. To overcome this problem Fuzzy Logic Controller (FLC) is employed with a set of control rules. The proposed control technique is studied for a two area non reheat thermal power systems. From simulation results, the performance of the FLC is better during load disturbances.

In this paper we analyze the requirements for the complex information system used to support a modern global university. We outline the architecture for this system based on the emerging cloud computing platforms and present an example of managing a university course by incorporating different Google cloud services within the Google Sites.

This roundtable proposal deals with the challenges lecturers face when including new course material into an existing module as a result of new developments in the technology or knowledge covered by the module. While the new technologies are gaining ever more grounds, the “old” technology or knowledge still remains important for the students to master, as much of the existing products and research activities still will be based on this former knowledge. The challenge lies in keeping the total amount of new learning material relatively constant, implying that some of the existing materials have to be removed from the module. What new learning material can be brought into the module, and which parts must be removed from the module? With references to Analogue Electronics and Control theory modules, we search for strategies, and inclusion and exclusion methods that should be utilized to ensure both the quality of the module itself, and the competitiveness of the students completing the module. The ultimate goal is to develop an “objective” approach for developing best practice when introducing new learning material.

In this paper we analyze the requirements for the complex information system used to support a modern global university. We outline the architecture for this system based on the emerging cloud computing platforms and present a use case based on the Google cloud.

This paper presents the comprehensive stochastic model of the TCP (Transmission Control Protocol) that aggregates all TCP states. The model provides the TCP connection throughput for a given packet loss probability, link capacity and link delay. The derived model incorporates assumption that packets coming after the first lost packet are not necessarily lost. Such assumption led to the model which adequately describes TCP behavior. The model derivation required determination of the transitional probabilities between TCP states, number of acknowledged packets and time intervals required for transitions. The obtained stochastic model was confirmed by averaged packet-level simulation results in ns2.

This paper presents one approach to modeling of TCP connection during the slow start phase. Such modeling can be used for TCP connection analysis with reduced computation complexity compared to the packet-level simulators. Proposed model is validated by comparing the results obtained from ns-2 simulations. Introduction Application protocols mainly used on the Internet, such as HTTP or SMTP, use TCP protocol for reliable transport. TCP connection performance analysis can be carried out in two different ways. First one is simulation of TCP connection at the packet-level. This approach leads to accurate results but also requires long simulation time. Alternative approach is to model the TCP behaviour analytically, significantly reducing simulation time while simultaneously keeping accuracy at an acceptable level. This paper reviews models available in the literature and proposes an alternative analytical TCP model during the slow-start phase. TCP behavior can be described with different models. Packet-level models are the most accurate since they employ full TCP stack implementation. However, when analyzing large-scale networks or simple networks with high throughputs, packetlevel simulators are impractical due to long simulation time. An alternative approach is to use mathematical abstractions to model the TCP behavior. One way to abstract the TCP behavior through mathematical tools is to use differential equations. Another approach is based on probability analysis. In this approach, statistical formulas are used to describe TCP behaviour in different stages. Aggregating these models, the full TCP behavior could be obtained. In this paper, a probability model for the slow-start TCP stage is derived. The derived model is validated by comparing the results with the packet-level simulation tool ns-2 [7]. Finally, future directions for employment of probability models of other TCP stages are given. 1 TCP Protocol TCP is a reliable connection-oriented transport protocol for packet-switched networks. Reliability is achieved by employing acknowledgements (ACKs) [2]. Using ACKs and sequence numbers, the transmitter keeps the track of packets that are successfully delivered to the receiver. TCP operates in different stages: Slow Start, Congestion Avoidance, Fast Retransmit, Fast Recovery and Timeout. Transition between stages is determined by packet loss or acknowledgement of predefined number of packets. The window size determines the maximum number of packets that a transmitter may send before receiving the first acknowledgement. In the Slow Start phase, window size is incremented with every received ACK. Time interval, from departure of the first packet to the last packet in a window, represents round. The window size varies with the rate of the packet loss in the network. Hence, the packet loss probability increases with the number of sent packets due to the congestion in the network. Generally, window size wi grows in rounds and can be expressed as: 1 0 1 1 1 1 1 1 1 − − − − − ⋅ = ⋅ = + ⋅ = + = i i i i i i w w b w w b w w γ γ (1) where b is number of packets acknowledge by one ACK, and i is the round number and w0 is the initial window size. Total number of packets sent in slow start including the round i is 1 1 0 0 1 2 0 0 − − ⋅ = ⋅ + + + ⋅ + = − γ γ γ γ γ i i i w w w w ssdata (2) When a packet loss occurs during the slow start stage, there are two mechanisms to detect it. The first mechanism detects packet loss by using timeouts (TO). The second mechanism detects packet loss upon receiving three duplicate ACKs. SNE Simulation Notes Europe – Print ISSN 2305-9974 | Online ISSN 2306-0271 SNE 21(1), 2011, 45-48 | doi: 10.11128/sne.21.sn.10047 A Gogic et al. Probability Model for TCP Slow Start 46 SNE 21(1) – 4/2011 TN 2 Approaches for Modelling the Packet Switched Networks There are several approaches for modeling packet switched networks. The most accurate network models are packet-level models that keep track of individual packets. These models are implemented in network simulators, such as ns-2 [7]. The main drawback of packet-level model is a large computational effort required to keep track of each virtual packet in large scale simulations. Analytical models are based on idea to model the TCP network mathematically in order to reduce complexity involved in simulations [1, 6]. These models do not consider the network dynamics. They assume that round-trip time and loss probability are constant and there are no interactions between TCP flows. Fluid models are intended for simulation of packet networks. They overcome network scalability problem by keeping track of average quantities for relevant network parameters [5, 4]. Additionally, they use assumption that the bit rates are piecewise constant. Hybrid models use continuous time state variables with discrete time events [3]. Hybrid simulations require significantly less computational resources than packet level simulators. However, solution of hybrid equations is still necessary in order to simulate networks. 3 Probability Model for TCP Slowstart Stage There are two approaches for creating probability models of TCP behaviour. The source centric model assumes that packets leave the source with a certain loss probability [1]. The assumption taken by the second model is that the network generates loss probabilities for each packet [5]. Thus, the arrival process is represented by Poisson random process. In this paper we use the first approach with the further assumptions: the packet losses in two successive rounds are not correlated, packet loss occurs only in the forward direction; packet losses are independent of window size. Model detects packet losses by triple duplicates and TOs. Based on the source-centric model [1,6], we derived the expected value of number of packets sent in the slow start phase as a function of packet loss probability p, the initial size of congestion window wi and the number of packets acknowledged by one ACK. We further distinguish two boundary cases related to the position where packet was lost. The first case is characterized by the packet loss occurring at the beginning of the round (Figure 1). For the second case, the packet loss occurs at the end of the round (Figure 2). Figure 1. Slow-start phase, packet drop occurred at beginning of the round i. Figure 1 shows the number of acknowledge packets -1. Taking into consideration equation (2), we can write

With the recent rise of social Web applications, there is an increased interest to bring other services to the Web domain, real-time voice and video in particular. Based on the HTTP model, the Session Initiation Protocol (SIP) [9] was developed to support these services. The telecommunications industry has accepted the SIP protocol, which is now widely implemented and used in telecom application servers.

This is the second of a two-part paper on the SimpleFSM, a domain-specific language, developed to simplify the application develoment for the SIP communication systems. While Part I describes the development of the SimpleFSM and its syntax, Part II gives some details of the SimpleFSM DSL integration with the Java SIP servlet architecture utilizing JRuby, a Ruby implementation for the Java Virtual Machine. Finally, a more complex converged application is developed and implemented using the developed DSL.

This two-part paper demonstrates an application of metaprogramming techniques to the development of domain specific languages (DSL) using the Ruby programming language and its application to SIP communication systems. Part I proposes the SimpleFSM, a DSL for finite-state machines (FSM), together with simple application examples. Part II proposes an approach to the SIP application development using the developed DSL in order to speed up and simplify the development process.

This paper presents a methodology to develop a complete domain specific language (DSL) for simple finite state machine (FSM) modeling, utilizing metaprogramming techniques found in Ruby programming language. Additionally, two libraries for FSM modeling are reviewed. A simple vending machine model is used to demonstrate the effectiveness of the DSL code. The proposed techniques together with the SIP Servlet API can be combined with Ruby's web development environments to develop complex converged telecom applications.

The paper presents an approach to integrate dynamic programming languages with IMS application servers. Existing Java-based platforms for telecom applications are surveyed. Specific steps of integration of Ruby and JEE server are given. A proof of concept application is developed and deployed on an open-source Java Enterprise server.

This paper presents a technique for PSS design which is based on multiobjective optimization. The technique has the ability to tune multiple PSS controllers by simultaneously enhancing the damping based performance index and robustness index specified via the /spl par/H/spl par//sub /spl infin// norm of the system. To illustrate the proposed design technique two case studies are presented, including a two-area benchmark system and a fifty-machine test system. To allow the proposed tools to be applied for PSS control design, the authors developed a MATLAB interface to the microGA optimization solver.

This paper presents a low-order identification technique, which is based on standard Prony analysis. The technique has the ability to extract crucial dynamic characteristics from a system of any size (e.g., a large-scale power system), which then can be used for developing a robust controller. In this paper the proposed identification technique is coupled with a robust controller design technique based on genetic algorithm to simultaneously tune power system stabilizers (PSSs) for multiple operating conditions. To illustrate the proposed identification and controller design techniques three case studies are presented, including a two-area benchmark system, a 50-machine test system and a large-scale (23 300 bus) Eastern Interconnection power system. To allow the proposed tools to be applied to the large-scale power system, the authors developed an interface with a standard production grade transient stability analysis software package PSS/E.