Milan Nikolic’s research while affiliated with Simon Fraser University and other places

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Publications (4)

Figure 1: Network setup for the MBone test session in the Communication Networks Laboratory at SFU.
Figure 2: Network setup for the MBone webcast session at the IFSA/NAFIPS 200 1 conference. 
Figure 3: The sdr , rat , and vic MBone application tools setup for the live session at the IFSA/NAFIPS 200 1 Open Forum workshop chaired by Prof. Lotfi Zadeh. 
Figure 4: The audio/video and measurement equipment setup for the live MBone webcast session. 
MBone Webcast: Network Setup and Data Collection
  • Article
  • Full-text available

August 2003

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160 Reads

Milan Nikolić

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Dan Hoffman

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In this paper, we describe the webcast implementation details and data collection during a multicast session of a technical conference held in Vancouver in Summer 2001. Audio and video signals were multicast using Internet Multicast Backbone (MBone) multimedia conferencing tools. The signals were transmitted to the MBone network using DVMRP tunneling through an ADSL line to the BCnet gigabit-capacity point of presence. Genuine traffic traces were collected during the multicast session and were later used to analyze the impact of traffic on network performance.

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Fig. 2. Topology of a simple CDPD network. 
Fig. 3. Average queueing delay vs. link utilization plots for genuine traffic trace (graph A), traffic generated by ON/OFF models (graphs B, C, and D), and traffic generated by the traditional Poisson model (graph E). For ON/OFF sources the parameters are: (B) a = 0.01 sec and b = 9.99 sec; (C) a = 5.00 sec and b = 5.00 sec; (D) a = 8.00 sec and b = 2.00 sec. 
Fig. 4. Buffer overflow probability vs. buffer size plots for (A) genuine traffic trace and (B) Poisson arrival traffic source. 
Fig. 5. Buffer overflow probability vs. link utilization plots for (A) genuine traffic trace; (B) ON/OFF source model with a = 5.00 sec and b = 5.00 sec; (C) Poisson arrival traffic source. 
Impact Of Self-Similarity On Wireless Data Network Performance

March 2001

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739 Reads

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9 Citations

Michael Jiang

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Milan Nikolic

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Stephen Hardy

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In this paper we investigate the impact of traffic patterns on wireless data networks. Modeling and simulation of the Cellular Digital Packet Data (CDPD) network of Telus Mobility (a commercial service provider) were performed using the OPNET tool. We use trace-driven simulations with genuine traffic trace collected from the CDPD network to evaluate the performance of CDPD protocol. This trace tends to exhibit longrange dependent behavior. Our simulation results indicate that genuine traffic traces, compared to traditional traffic models such as the Poisson model, produce longer queues and, thus, require larger buffers in the deployed network's elements.

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Fig. 2. Topology of a simple CDPD network.
Fig. 3. Average queueing delay vs. link utilization plots for genuine traffic trace (graph A), traffic generated by ON/OFF models (graphs B, C, and D), and traffic generated by the traditional Poisson model (graph E). For ON/OFF sources the parameters are: (B) a = 0.01 sec and b = 9.99 sec; (C) a = 5.00 sec and b = 5.00 sec; (D) a = 8.00 sec and b = 2.00 sec.
Fig. 4. Buffer overflow probability vs. buffer size plots for (A) genuine traffic trace and (B) Poisson arrival traffic source.
Fig. 5. Buffer overflow probability vs. link utilization plots for (A) genuine traffic trace; (B) ON/OFF source model with a = 5.00 sec and b = 5.00 sec; (C) Poisson arrival traffic source.
Impact of self-similarity on wireless data network performance

February 2001

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152 Reads

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78 Citations

M. Jiang

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M. Nikolic

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S. Hardy

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In this paper we investigate the impact of traffic patterns on wireless data networks. Modeling and simulation of the cellular digital packet data (CDPD) network of Telus Mobility (a commercial service provider) were performed using the OPNET tool. We use trace-driven simulations with genuine traffic trace collected from the CDPD network to evaluate the performance of the CDPD protocol. This trace tends to exhibit long-range dependent behavior. Our simulation results indicate that genuine traffic traces, compared to traditional traffic models such as the Poisson model, produce longer queues and, thus, require larger buffers in the deployed network's elements

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Figure 1. FDDI hub configuration. The network consists of three concentrators (FDDI h ubs) and nine stations. The concentrators are connected with FDDI duplex links in a dual-ring topology. The stations are attached to the ring via concentrators, creating a hub topology. 
Figure 2. FDDI end-to-end delay (sec) plots, with traffic load as a parameter. Load, mean number of frames sent by the source, according to an exponential distribution, was varied from 1 to 100 packets per second. 
Figure 3. FDDI client-server configuration. This network topology is suitable for applications such as FTP. Clients are connected to the network via FDDI hubs. Hubs and servers at different locations are connected via two FDDI switches. 
Figure 4. Throughput (bits/sec) plots of Server 1 (top) and Client 1 (bottom). 
Figure 5. ATM client-server network consisting of two switches, an ATM server, and five clients requesting five distinct ATM service categories. Applications in an ATM network require different quality of service (QoS) and, therefore, different traffic categories. For example, a voice application such as a telephone conversation requires a small transfer delay not noticeable to the users. However, transfer delay is not that important for quality video applications with unidirectional video transfers. In such applications, the delay jitter (variation in delay) is an important QoS parameter and should be kept small. The errors and losses in a voice application or in a video broadcast might not be very noticeable or important. Nevertheless for applications such as data transfers, accuracy is critical. An ATM network has to be able to achieve the required performance for each of the described applications. That is the reason that five different service categories are supported in ATM technology: constant bit rate (CBR), available bit rate (ABR), real time variable bit rate (RT_VBR), non-real-time variable bit rate (NTR_VBR), and unspecified bit rate (UBR) [3]. It is usually difficult to compare and rank the performance of these five service categories. Each service category has its own advantages and disadvantages. CBR and RT_VBR traffic are mostly 

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Simulation Of Packet Data Networks Using Opnet

October 2000

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5,209 Reads

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12 Citations

Nazy Alborz

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Maryam Keyvani

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Milan Nikolic

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In this paper we describe the use of the OPNET simulation tool for modeling and analysis of packet data networks. We simulate two types of high-performance networks: Fiber Distributed Data Interface and Asynchronous Transfer Mode. We examine the performance of the FDDI protocol by varying network parameters in two network configurations. We also model a simple ATM network and measure its performance under various ATM service categories. Finally, we develop an OPNET process model for leaky bucket congestion control algorithm. We examine its performance and its effect on the traffic patterns (loss and burst size) in an ATM network. 1.

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Citations (3)

... Therefore, individuals with common concepts of conduct connect; this provides a significant dimension in which pairing of the device position may be used to recognize resemblance trends of smartphone apps. So, to quantify similarity characteristics among cell phones for this study, we used their interests in spatial, temporal, and preferred word attachments to the sites and the travel frequencies and lengths of those areas [11,12]. ...

Reference:

Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix
Impact of self-similarity on wireless data network performance
M. Jiang

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M. Nikolic

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S. Hardy

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... The reasons for this result will be discussed in more detail when we present our results on another policing mechanism inTable 6. Although the choice of having the token generation rate be equal to the source mean rate has often been used in the literature (e.g., [25]), in many other works (e.g., [8], [10]), the authors have reached the same conclusion as the one denoted by our results: if the token rate is equal or close to the source mean rate, well-behaved sources often have many cells marked as violating. Hence, three solutions can be applied to deal with the problem of high packet dropping from the second policer of the dual token bucket: First solution. ...

Reference:

Dynamic versus Static Traffic Policing: A New Approach for Videoconference Traffic over Wireless Cellular Networks
Simulation Of Packet Data Networks Using Opnet
Nazy Alborz

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Maryam Keyvani

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Milan Nikolic

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... Both properties were discovered in Beran et al (1995) for variable-bit-rate (VBR) video traffic, in Paxson and Floyd (1995) for traffic in wide area networks, in Leland et al (1994) and Willinger et al (1997) for Ethernet networks and in Crovella and Bestavros (1997) for Web traffic. The finding of self-similarity and LRD, in turn, spurred the research on queueing models with 'fractal' input traffic (Likhanov et al, 1995;Norros, 1995), and the assessment of their impact on network performance (Conti et al, 1996;Erramilli et al, 1996;Jiang et al, 2001) as well. As these works demonstrate, the effect of LRD on packet loss and delay may be drastic, leading to subexponential decay of the buffer overflow probability and, consequently, buffer sizes much larger than those predicted by memoryless or short-memory (ie, Markov) processes. ...

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Efficient online generation of the correlation structure of the fGn process
Impact Of Self-Similarity On Wireless Data Network Performance
Michael Jiang

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Milan Nikolic

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Stephen Hardy

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