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Traffic Behavior of Local Area Network Based on
M/M/1 Queuing Model Using Poisson and
Exponential Distribution
Amirali Rezaei
School of Postgraduate Studies
Asia Pacific University of Technology & Innovation (APU)
Technology Park Malaysia,
Bukit Jalil, 57000, Malaysia
Saadiah Yahya
Faculty of Computer and Mathematical science
Universiti Teknologi MARA (UiTM)
Shah Alam, Malaysia
Alireza Erfanian
Faculty of Computer Science and Information Technology
Universiti Putra Malaysia (UPM), 43400, Serdang
Selangor Darul Ehsan
Kayvan Atefi
Faculty of Computer and Mathematical sciences
Universiti Teknologi MARA (UiTM)
Shah Alam, Malaysia
Presented by:
Neeraj Baghel
2016 IEEE Region 10 Symposium (TENSYMP), Bali,
Indonesia
OUTLINE
• INTRODUCTION
• BACKGROUND OF STUDY
• NETWORK PERFORMANCE MEASUREMENT AND ANALYSIS
• DESIGN
• RESULTS
• SUGGESTIONS AND FUTURE WORK
• REFERENCES
INTRODUCTION
• Local Area Networks (LAN) is one of the most popular networks in which
its performance is very important for operators.
• The LAN method has been an essential infrastructure of numerous
companies and organizations for a long time.
• Thus, the issue to create the network topological structure continues to be
elevated for scientists and engineers to resolve, due to its foundational
status within the network application [14].
• LAN as a high-speed data network that spans in a small physical area.
Usually, workstations, printers, personal computers, servers, and other
network devices are connected through LAN.
• This provides a lot of advantages such as file transferring between users
communicating by Email and accessing shared devices and applications
BACKGROUND OF STUDY
A. Queuing Model
• In queuing theory, a queuing model can be used to approximate a genuine queuing situation
or system.
• Therefore, the queuing behavior could be analyzed mathematically.
• Queuing models allow numerous helpful steady condition performance measures to be
determined.
B. Single-Server Queues (M/M/1)
• The central element of the Single-Server Queues (M/M/1) is a server which provides some
service to items.
• Items from some population of items arrive at the system to be served.
• If the server is idle, an item is served immediately.
• Otherwise, an arriving item joins waiting in line.
• When the server has completed serving an item, the item departs.
• If there are items waiting in the queue, one is immediately dispatched to the server.
• The server in this model can represent anything that performs some function or service for a
collection of items.
For instance, a processor provides service to processes; a transmission line provides a
transmission service to packets or frames of data; an I/O device provides a read or write service for
I/O requests [8].
NETWORK PERFORMANCE
MEASUREMENT AND ANALYSIS
• It is very difficult to understand the cause-effect relationships of the Internet due to its size,
complexity and diversity. Measurement is essential for comprehending the behavior of the
system.
• A careful analysis is needed after measurement. Data analyzing in network also differs from
other disciplines.
• Developing and evaluating new techniques use measurement or analysis methods including
Queuing models and Simulation models.
• The different types of queuing systems are analyzed to determine performance measures from
the description of the system. Since a queuing model represents a dynamic system, the values
of the performance measures vary with time.
DELAY (w)
• One of the most important performance characteristics of network is delay.
• Delay is defined as the time consumed for a bit of data to travel through the network from one
node to another.
• The end-to-end delay is the average time consumed from sending a packet by the source and
receiving it at the destination.
• The waiting time is the time that a job spends in a queue waiting to be serviced.
• Delay in queuing WQ is given by:
UTILIZATION (ρ)
• The queuing system is considered in server utilization.
• The percentage of time in which the server is busy to process jobs during a simulation is
known as server utilization.
• If the queuing system consists of a single server, then the utilization is the fraction of the time
in which the server is busily occupied.
• The server utilization is given by
THROUGHPUT (λ)
• Network throughput is the average rate of a successful message transfer through a media.
• The system throughput is also known as the sum of data rates delivered to all terminals in the
network.
• The system throughput can be evaluated using mathematical queuing theory.
• In this theory, the arrival rate λ indicates the load in packets per time unit and departure rate μ
indicates the throughput in packets per time unit.
• Since the departure rate is equal to the arrival rate λ for a queuing system in statistical
equilibrium.
• The throughput is given by:
Network load
• Network load is principally caused by the bandwidth used, buffer availability and processing
time at intermediate nodes.
• Indeed, network load is really a key parameter that has a potentially large effect on network
methods.
• Besides, network load accounts for traffic delay and it is sometimes wrongly identified as
congestion.
Distribution
There are some distributions in this study that will focus on Poisson and Exponential
distribution:
A Poisson distribution
• Poisson distribution in probability theory and statistics can be defined as a discrete probability
distribution that shows the probability of a number of events with a known average rate
occurring in a fixed period of time and independent of the last event time.
• It can be applied to events with other definite intervals such as distance, area or volume.
• On the other hand, [13] discussed that Poisson distribution arises when a number of events are
counted across time or over an area.
• The Poisson distribution is commonly used when the occurrences are independent.
• Therefore, when the average frequency of occurrence is known, a sample occurrence cannot
change the chance of other occurrences.
Distribution
B Exponential Distribution
• The exponential distribution in probability theory of statistics is a kind of continuous
probability distributions.
• The time between events in a Poisson process can be defined by exponential distribution.
• Furthermore the exponential distribution is really a group of continuous probability
distributions.
• It describes time between occasions inside a Poisson process, i.e. a procedure by which
occasions occur continuously and individually in a constant average rate [6].
Network load
• Network load is principally caused by the bandwidth used, buffer availability and processing
time at intermediate nodes.
• Indeed, network load is really a key parameter that has a potentially large effect on network
methods.
• Besides, network load accounts for traffic delay and it is sometimes wrongly identified as
congestion.
DESIGN
Table I illustrates the parameters used in testing the packet
size of Poisson and Exponential which is 320(bits). The type
of model is Ethernet station while the type of connection is
100BaseT with its data rate as 100,000,000.
RESULTS
This phase compares and analyzes the two scenarios together which are
Scenario 1 (Poisson) and Scenario 2 (Exponential).
The mentioned five parameters are analyzed in the following section.
A. Delay
B. Load
C. Traffic Received
D. Utilization
E. Throughput
A. Delay
• Figure 1 shows the comparison
results between two scenarios
in the field of delay. Scenario 1
is on Poisson distribution while
scenario 2 is Exponential.
• Maximum delay recorded in
scenario 1 is at 15 minutes that is
0.0001075, and for scenario 2
the maximum delay stated is
6.5 minutes that is 0.0001045.
• Figure 1 shows that the delay in
Poisson distribution is higher
than that in exponential
distribution.
Exponential
Poisson
Time
Fig 1 Delay
Delay
/ Sec
B. Load
• Figure 2 shows the load
arameter for the scenarios. As
can be seen in the figure, the
maximum load recorded for
scenario 2 is 280 bits/second
while the minimum is 99
bits/second.
• This is different for scenario 1
where the maximum load is
only 99 bit/second while the
minimum is 30 bits/second.
• According to Figure 2,
Exponential had a higher load
compared to that of Poisson
Exponential
Poisson
Time
Fig 2 Load
Bits /
Sec
C. Traffic Received
• The following Figure 3
illustrates that traffic received
for Exponential is higher
compared to Poisson.
• The maximum traffic received
for scenario 2 (Exponential) is
362 bits/second compared to
265 bits/second for Poisson
(scenario 1).
• But in the 5th minute, the same
volumes of traffic are received
for both.
Exponential
Poisson
Time
Fig 3 Traffic
Recieved
Bits/
Sec
D. Utilization
• Figure 4 shows the utilization
for the scenarios. As can be
seen, the maximum utilization
for scenario 2 is 0.00140 while
the minimum is 0.00030.
• This is different for scenario 1
where the maximum utilization
is only 0.00080 while the
minimum is 0.00030.
• According to Figure
4,Exponential had a higher
utilization compared to
Poisson.
Exponential
Poisson
Time
Fig 4 Utilization
Bits /
Sec
E. Throughput
• Figure 5 shows the throughput
for the scenarios.
• As can be seen, the maximum
throughput for scenario 2 is 0.5
(packet/sec) while the
minimum is 0.12 (packet/sec).
• This is different for scenario 1
where the maximum
throughput is only 0.31
(packet/sec) while the
minimum is 0.12 (packet/sec).
• According to Figure 5,
Exponential had a higher
throughput compared to
Poisson.
Exponential
Poisson
Time
Fig 5 Throughput
Bits /
Sec
SUGGESTIONS AND FUTURE WORK
There are many distributions that have not yet been studied, such as Bernoulli
distribution, binomial distribution, geometric distribution, negative binomial
distribution and discrete uniform distribution.
In addition, other parameters can also be applied such as traffic send, data loss
and jitter.
REFERENCES
[1] Periklis C., A. C. B. (2007). Performance Analysis of IEEE 802.11 DCF in
Presence of Transmission Errors. IEEE Communications Society.
[2] Wu Bin, Z. H., Sun He. (2008). Research on Multilevel Control Model of
Network Performance Based on Multi-Agent. International Conference
on Advanced Computer Control/IEEE.
[3] Shu-Gang L., H. F., Jin-Jiang L. (2003). Application of Event Graph in
Network Performance Analysis. Proceedings of the Second International
Conference on Machine Learning and Cybernedcs.
[4] Bradley W. S, S. W. H., Jeffery A. K, and Stephen W. P. (2011).
Diagnosing Anomalous Network Performance with Confidence.
International Symposium on Cluster, Cloud and Grid Computing/IEEE.
REFERENCES
[5] Gunter B, S. G., Hermam D. M., Krisher S. T. (2006). Queuing networks
and Markov chains: modeling and performance evaluation.
[6] Daniel F., Schmidt E. M. (2009). Universal Models for the Exponential
Distribution. Monash University Clayton School of Information
Technology/IEEE.
[7] Merike K., (2003). Designing Network Security, 2nd Edition. Cisco Press
[8] William S., (2000). Data and Computer Communications, 5th Ed.
Universidade do Minho – Dep. Informática - Campus de Gualtar.
[9] Lambert M. S, Miriam T. T, Susan F. M. (2010). Queuing Theory (First
ed.): Betascript Publishing.
REFERENCES
[10] Rajender K. Brahmjit S (2010). EVM as Generic Qos Trigger for
Heterogeneous Wireless Overlay Networks. International Journal of
Wireless & Mobile Networks (IJWMN). DOI: 10.5121/ijwmn.2010.2315
[11] Ivo A., Jacques R. (2002). Queuing Theory. Eindhoven University of
Technology.
[12] Adriano G., Chris G. (2009). A New Metric for Network Load and
Equation of State, In Proceedings of IEEE Computer Society (ICNS09),
Valencia, Spain
[13] Bernard A. R. (2006), Fundamentals of Biostatistics (sixth edition).
Cengage Learning, THOMSON.
[14] Fei T., Gang Z. (2009), the Research of an Approach to Design Local
Area Network Topology Based on Genetic Algorithm. Second
International Symposium on Computational Intelligence and Design.
ISBN: 978-0-7695-3865-5
REFERENCES
[15] Munir B. S., Abhik C., S. L. Nalbalwar, K. T. Subramanian (2010).
Novel Approach to Improve QoS of a Multiple Server Queue. Int. J.
Communications, Network and System Sciences.
doi:10.4236/ijcns.2010.31012
[16] Geoff H., Telstra (2014). Measuring IP Network Performance, Volume 6,
Number 1. Access date 2/12/2014. Retrieved at:
http://www.cisco.com/web/about/ac123/ac147/archived_issues/ipj_6-
1/measuring_ip.html
THANK YOU

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Traffic behavior of local area network based on

  • 1. Traffic Behavior of Local Area Network Based on M/M/1 Queuing Model Using Poisson and Exponential Distribution Amirali Rezaei School of Postgraduate Studies Asia Pacific University of Technology & Innovation (APU) Technology Park Malaysia, Bukit Jalil, 57000, Malaysia Saadiah Yahya Faculty of Computer and Mathematical science Universiti Teknologi MARA (UiTM) Shah Alam, Malaysia Alireza Erfanian Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM), 43400, Serdang Selangor Darul Ehsan Kayvan Atefi Faculty of Computer and Mathematical sciences Universiti Teknologi MARA (UiTM) Shah Alam, Malaysia Presented by: Neeraj Baghel 2016 IEEE Region 10 Symposium (TENSYMP), Bali, Indonesia
  • 2. OUTLINE • INTRODUCTION • BACKGROUND OF STUDY • NETWORK PERFORMANCE MEASUREMENT AND ANALYSIS • DESIGN • RESULTS • SUGGESTIONS AND FUTURE WORK • REFERENCES
  • 3. INTRODUCTION • Local Area Networks (LAN) is one of the most popular networks in which its performance is very important for operators. • The LAN method has been an essential infrastructure of numerous companies and organizations for a long time. • Thus, the issue to create the network topological structure continues to be elevated for scientists and engineers to resolve, due to its foundational status within the network application [14]. • LAN as a high-speed data network that spans in a small physical area. Usually, workstations, printers, personal computers, servers, and other network devices are connected through LAN. • This provides a lot of advantages such as file transferring between users communicating by Email and accessing shared devices and applications
  • 4. BACKGROUND OF STUDY A. Queuing Model • In queuing theory, a queuing model can be used to approximate a genuine queuing situation or system. • Therefore, the queuing behavior could be analyzed mathematically. • Queuing models allow numerous helpful steady condition performance measures to be determined. B. Single-Server Queues (M/M/1) • The central element of the Single-Server Queues (M/M/1) is a server which provides some service to items. • Items from some population of items arrive at the system to be served. • If the server is idle, an item is served immediately. • Otherwise, an arriving item joins waiting in line. • When the server has completed serving an item, the item departs. • If there are items waiting in the queue, one is immediately dispatched to the server. • The server in this model can represent anything that performs some function or service for a collection of items. For instance, a processor provides service to processes; a transmission line provides a transmission service to packets or frames of data; an I/O device provides a read or write service for I/O requests [8].
  • 5. NETWORK PERFORMANCE MEASUREMENT AND ANALYSIS • It is very difficult to understand the cause-effect relationships of the Internet due to its size, complexity and diversity. Measurement is essential for comprehending the behavior of the system. • A careful analysis is needed after measurement. Data analyzing in network also differs from other disciplines. • Developing and evaluating new techniques use measurement or analysis methods including Queuing models and Simulation models. • The different types of queuing systems are analyzed to determine performance measures from the description of the system. Since a queuing model represents a dynamic system, the values of the performance measures vary with time.
  • 6. DELAY (w) • One of the most important performance characteristics of network is delay. • Delay is defined as the time consumed for a bit of data to travel through the network from one node to another. • The end-to-end delay is the average time consumed from sending a packet by the source and receiving it at the destination. • The waiting time is the time that a job spends in a queue waiting to be serviced. • Delay in queuing WQ is given by:
  • 7. UTILIZATION (ρ) • The queuing system is considered in server utilization. • The percentage of time in which the server is busy to process jobs during a simulation is known as server utilization. • If the queuing system consists of a single server, then the utilization is the fraction of the time in which the server is busily occupied. • The server utilization is given by
  • 8. THROUGHPUT (λ) • Network throughput is the average rate of a successful message transfer through a media. • The system throughput is also known as the sum of data rates delivered to all terminals in the network. • The system throughput can be evaluated using mathematical queuing theory. • In this theory, the arrival rate λ indicates the load in packets per time unit and departure rate μ indicates the throughput in packets per time unit. • Since the departure rate is equal to the arrival rate λ for a queuing system in statistical equilibrium. • The throughput is given by:
  • 9. Network load • Network load is principally caused by the bandwidth used, buffer availability and processing time at intermediate nodes. • Indeed, network load is really a key parameter that has a potentially large effect on network methods. • Besides, network load accounts for traffic delay and it is sometimes wrongly identified as congestion.
  • 10. Distribution There are some distributions in this study that will focus on Poisson and Exponential distribution: A Poisson distribution • Poisson distribution in probability theory and statistics can be defined as a discrete probability distribution that shows the probability of a number of events with a known average rate occurring in a fixed period of time and independent of the last event time. • It can be applied to events with other definite intervals such as distance, area or volume. • On the other hand, [13] discussed that Poisson distribution arises when a number of events are counted across time or over an area. • The Poisson distribution is commonly used when the occurrences are independent. • Therefore, when the average frequency of occurrence is known, a sample occurrence cannot change the chance of other occurrences.
  • 11. Distribution B Exponential Distribution • The exponential distribution in probability theory of statistics is a kind of continuous probability distributions. • The time between events in a Poisson process can be defined by exponential distribution. • Furthermore the exponential distribution is really a group of continuous probability distributions. • It describes time between occasions inside a Poisson process, i.e. a procedure by which occasions occur continuously and individually in a constant average rate [6].
  • 12. Network load • Network load is principally caused by the bandwidth used, buffer availability and processing time at intermediate nodes. • Indeed, network load is really a key parameter that has a potentially large effect on network methods. • Besides, network load accounts for traffic delay and it is sometimes wrongly identified as congestion.
  • 13. DESIGN Table I illustrates the parameters used in testing the packet size of Poisson and Exponential which is 320(bits). The type of model is Ethernet station while the type of connection is 100BaseT with its data rate as 100,000,000.
  • 14. RESULTS This phase compares and analyzes the two scenarios together which are Scenario 1 (Poisson) and Scenario 2 (Exponential). The mentioned five parameters are analyzed in the following section. A. Delay B. Load C. Traffic Received D. Utilization E. Throughput
  • 15. A. Delay • Figure 1 shows the comparison results between two scenarios in the field of delay. Scenario 1 is on Poisson distribution while scenario 2 is Exponential. • Maximum delay recorded in scenario 1 is at 15 minutes that is 0.0001075, and for scenario 2 the maximum delay stated is 6.5 minutes that is 0.0001045. • Figure 1 shows that the delay in Poisson distribution is higher than that in exponential distribution. Exponential Poisson Time Fig 1 Delay Delay / Sec
  • 16. B. Load • Figure 2 shows the load arameter for the scenarios. As can be seen in the figure, the maximum load recorded for scenario 2 is 280 bits/second while the minimum is 99 bits/second. • This is different for scenario 1 where the maximum load is only 99 bit/second while the minimum is 30 bits/second. • According to Figure 2, Exponential had a higher load compared to that of Poisson Exponential Poisson Time Fig 2 Load Bits / Sec
  • 17. C. Traffic Received • The following Figure 3 illustrates that traffic received for Exponential is higher compared to Poisson. • The maximum traffic received for scenario 2 (Exponential) is 362 bits/second compared to 265 bits/second for Poisson (scenario 1). • But in the 5th minute, the same volumes of traffic are received for both. Exponential Poisson Time Fig 3 Traffic Recieved Bits/ Sec
  • 18. D. Utilization • Figure 4 shows the utilization for the scenarios. As can be seen, the maximum utilization for scenario 2 is 0.00140 while the minimum is 0.00030. • This is different for scenario 1 where the maximum utilization is only 0.00080 while the minimum is 0.00030. • According to Figure 4,Exponential had a higher utilization compared to Poisson. Exponential Poisson Time Fig 4 Utilization Bits / Sec
  • 19. E. Throughput • Figure 5 shows the throughput for the scenarios. • As can be seen, the maximum throughput for scenario 2 is 0.5 (packet/sec) while the minimum is 0.12 (packet/sec). • This is different for scenario 1 where the maximum throughput is only 0.31 (packet/sec) while the minimum is 0.12 (packet/sec). • According to Figure 5, Exponential had a higher throughput compared to Poisson. Exponential Poisson Time Fig 5 Throughput Bits / Sec
  • 20. SUGGESTIONS AND FUTURE WORK There are many distributions that have not yet been studied, such as Bernoulli distribution, binomial distribution, geometric distribution, negative binomial distribution and discrete uniform distribution. In addition, other parameters can also be applied such as traffic send, data loss and jitter.
  • 21. REFERENCES [1] Periklis C., A. C. B. (2007). Performance Analysis of IEEE 802.11 DCF in Presence of Transmission Errors. IEEE Communications Society. [2] Wu Bin, Z. H., Sun He. (2008). Research on Multilevel Control Model of Network Performance Based on Multi-Agent. International Conference on Advanced Computer Control/IEEE. [3] Shu-Gang L., H. F., Jin-Jiang L. (2003). Application of Event Graph in Network Performance Analysis. Proceedings of the Second International Conference on Machine Learning and Cybernedcs. [4] Bradley W. S, S. W. H., Jeffery A. K, and Stephen W. P. (2011). Diagnosing Anomalous Network Performance with Confidence. International Symposium on Cluster, Cloud and Grid Computing/IEEE.
  • 22. REFERENCES [5] Gunter B, S. G., Hermam D. M., Krisher S. T. (2006). Queuing networks and Markov chains: modeling and performance evaluation. [6] Daniel F., Schmidt E. M. (2009). Universal Models for the Exponential Distribution. Monash University Clayton School of Information Technology/IEEE. [7] Merike K., (2003). Designing Network Security, 2nd Edition. Cisco Press [8] William S., (2000). Data and Computer Communications, 5th Ed. Universidade do Minho – Dep. Informática - Campus de Gualtar. [9] Lambert M. S, Miriam T. T, Susan F. M. (2010). Queuing Theory (First ed.): Betascript Publishing.
  • 23. REFERENCES [10] Rajender K. Brahmjit S (2010). EVM as Generic Qos Trigger for Heterogeneous Wireless Overlay Networks. International Journal of Wireless & Mobile Networks (IJWMN). DOI: 10.5121/ijwmn.2010.2315 [11] Ivo A., Jacques R. (2002). Queuing Theory. Eindhoven University of Technology. [12] Adriano G., Chris G. (2009). A New Metric for Network Load and Equation of State, In Proceedings of IEEE Computer Society (ICNS09), Valencia, Spain [13] Bernard A. R. (2006), Fundamentals of Biostatistics (sixth edition). Cengage Learning, THOMSON. [14] Fei T., Gang Z. (2009), the Research of an Approach to Design Local Area Network Topology Based on Genetic Algorithm. Second International Symposium on Computational Intelligence and Design. ISBN: 978-0-7695-3865-5
  • 24. REFERENCES [15] Munir B. S., Abhik C., S. L. Nalbalwar, K. T. Subramanian (2010). Novel Approach to Improve QoS of a Multiple Server Queue. Int. J. Communications, Network and System Sciences. doi:10.4236/ijcns.2010.31012 [16] Geoff H., Telstra (2014). Measuring IP Network Performance, Volume 6, Number 1. Access date 2/12/2014. Retrieved at: http://www.cisco.com/web/about/ac123/ac147/archived_issues/ipj_6- 1/measuring_ip.html