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==CA - Network design for high availability==
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{{Sidebar}}
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[[:File:Network_design_for_high_availability-CA_description.pdf]]
 
  
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[[:File:Network_design_for_high_availability-PacketTracerFile.zip]]
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<br />
 
<br />
−
===Group justification report===
+
==Projects portfolio==
−
[[:File:Network_design_for_high_availability-GroupJustificationReport.pdf]]
 
  
  
−
To make the decision of the more suitable network design for the new data center of Dublin Computer School (DCS), we consider the following specification provided in the description of the project:
+
<br />
 +
==Data Analytics courses==
 +
 
 +
 
 +
<br />
 +
==Possible sources of data==
 +
 
 +
 
 +
<br />
 +
==What is data==
 +
 
 +
 
 +
<br />
 +
===Qualitative vs quantitative data===
 +
 
 +
 
 +
<br />
 +
====Discrete and continuous data====
 +
 
 +
 
 +
<br />
 +
===Structured vs Unstructured data===
 +
 
 +
 
 +
<br />
 +
===Data Levels and Measurement===
 +
 
 +
 
 +
<br />
 +
===What is an example===
 +
 
 +
 
 +
<br />
 +
===What is a dataset===
 +
 
 +
 
 +
<br />
 +
===What is Metadata===
 +
 
 +
 
 +
<br />
 +
==What is Data Science==
 +
 
 +
 
 +
<br />
 +
===Supervised Learning===
 +
 
 +
 
 +
 
 +
<br />
 +
===Unsupervised Learning===
 +
 
 +
 
 +
<br />
 +
===Reinforcement Learning===
 +
 
 +
 
 +
<br />
 +
==Some real-world examples of big data analysis==
 +
 
 +
 
 +
<br />
 +
==Statistic==
 +
 
 +
 
 +
<br />
 +
==Descriptive Data Analysis==
 +
 
  
−
*The fact that the business is home grown in Dublin and the organization is expanding rapidly both in Dublin and in many sites around Ireland,
+
<br />
−
*The growth expected by the Infrastructure Manager,
+
===Central tendency===
−
*The need of a new Moodle system and a CRM system for the Marketing department,
 
  
  
−
We can see that Dublin Computer School (DCS) is, without any doubt, expecting a significant growth for the next years. Therefore, based on this fact, and after evaluating the budget, we decided to go for an ambitious design that ensure not only availability and reliability but also scalability of the network. We have to take into consideration that this data center is going to be used for all the sites around Ireland, where the company is also expecting growing.
+
<br />
 +
====Mean====
  
  
 
<br />
 
<br />
−
====Dublin====
+
=====When not to use the mean=====
−
As we have already mentioned, in the Dublin LAN we are going to place the new data center; but also, this network have to be designed to provided end user devices communication (Wired and Wireless).
 
  
  
−
In general, our design is based on the concepts described in the «Campus LAN and Wireless LAN Design Guide» of Cisco [\cite]. We built a hierarchical Three-Tier Design: Core, Distribution and Access layers.
+
<br />
 +
====Median====
  
−
At the beginning of the project, we though a Two-Tier Design was the most suitable option, but after consider many factors, the expected growing of the network tipped the scale in favor of the Three-Tier Design (See Figure \ref).
 
  
 +
<br />
 +
====Mode====
  
−
In Figure see we show the design for the Dublin network. Our design is composed by:
 
  
−
*A layer 3 switch in the core.
+
<br />
−
*Two layer 3 distribution switches.
+
====Skewed Distributions and the Mean and Median====
−
*Four access switches.
 
  
  
 
<br />
 
<br />
−
=====VLANs=====
+
====Summary of when to use the mean, median and mode====
−
We created 4 VLANs:
+
measures-central-tendency-mean-mode-median-faqs.php
  
−
*VLAN10 (Student)
 
−
*VLAN20 (Marketing)
 
−
*VLAN30 (HR)
 
−
*VLAN40 (Finance)
 
−
*VLAN99 (Management)
 
  
 +
<br />
 +
===Measures of Variation===
  
−
We perform the following settings:
 
  
−
*We configure the management interface (VLAN99) in every switch with an IP address
+
<br />
−
*802.1Q Trunk Between the Switches (Manually configuration)
+
====Range====
−
*In the access switches, we configured access ports for the end user devices and server and assigned VLANs to the correct switch interfaces (See Figure XX). The servers interfaces were assigned to the Management VLAN99.
 
  
  
 
<br />
 
<br />
−
=====Rapid spanning tree between switches=====
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====Quartile====
−
In our implementation, we made sure root bridge is in a suitable position. To do so, we manually configuring priority to influence the root election:
 
  
−
*We placed the root bridge in to core of our design for all VLANs
 
−
*We placed the root secondary in the distribution level of the network and configure Load Balancing sharing the root secondary between the 2 distribution switches.
 
  
−
<syntaxhighlight>
+
<br />
−
MS1(config)#spanning-tree vlan 1,10,20,30,40,99 root primary
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====Box Plots====
  
−
MS2(config)#spanning-tree vlan 1,10,20 root secondary
 
−
MS3(config)#spanning-tree vlan 30,40,99 root secondary
 
−
</syntaxhighlight>
 
−
 
−
With this configuration, RSTP is avoiding redundant by blocking port mostly in the access layer.
 
  
−
Because we did load balancing sharing the root secondary between the 2 distribution switches, and because we are doing «Per-Vlan rapid spanning tree mode», the port blocked would depend on the VLAN. For example, if we consider '''S4'''. The rapid spanning tree protocol is blocking the '''F0/18''' port for the VLANs where the '''root secondary''' is '''MS2'''. However, for the VLANs where the '''root secondary''' is '''MS3''', rapid spanning is blocking the '''Fa0/14''' port. That is why all the ports are shown in green in our network (none of the port in blocked for all VLANs) (See Figure XX).
+
 
 +
<br />
 +
====Variance====
 +
 
 +
 
 +
<br />
 +
====Standard Deviation====
  
  
 
<br />
 
<br />
−
=====Configuring 802 1Q trunk-based inter-VLAN routing=====
+
==== Z Score ====
−
No key decisions had to be taken in this part, we just configure 802 1Q trunk-based inter-VLAN routing to provide routing for our multiple VLANs. You can verified all IP addresses and interfaces configured in the Addressing table.
 
  
  
 
<br />
 
<br />
−
=====Wireless access for a GUEST wifi network=====
+
===Shape of Distribution===
−
The GUEST wifi network was configured using a wireless rourters attached to one of the access switches. In Figure XX we show the configuration performed. We attached the wireless router to VLAN10 and created a new wifi network. A DHCP server was also enable in the wireless router so the devices were are able to request an IP via DHCP (Figure xx)
+
 
  
−
Some security configurations were also performed:
+
<br />
−
* We configured a passphrase for the GUEST network: duboffice2019
+
====Probability distribution====
−
* Enable encryption.
 
  
  
 
<br />
 
<br />
−
====WAN====
+
=====The Normal Distribution=====
−
We created a WAN network connecting a total of 5 sites: Dublin, Galway, Limerick, Cork and Sligo. You can see the IP addresses in the Addressing table. They corespondent to the 10.0.0.0 network.
 
  
  
−
We make sure to include redundant paths between Dublin and Galway, which is the main concern of our WAN.
+
<br />
 +
====Histograms====
  
  
−
We configured OSPF Routing Protocol. OSPF is a widely used protocols with one of the lower Administrative Distance (110). That is why, in case of multiple routing protocols configured in a router (such as RIP or IS-IS), OSPF would be the defauld one and used to route packets. OSPF is able to determine the shortest path to a destination by adding the costs of each path to reach a destination.
+
<br />
 +
====Skewness====
  
  
 
<br />
 
<br />
−
====Addressing table====
+
====Kurtosis====
−
{| class="wikitable" style="margin: 0 auto;"
 
−
|+
 
−
!
 
−
!Device
 
−
!Interface
 
−
!IP Address
 
−
!Subnet Mask
 
−
!Default Gateway
 
−
!Comments
 
−
|-
 
−
| rowspan="28" |'''Dublin'''
 
−
| rowspan="9" |'''R1'''
 
−
|G0/1.1
 
−
|172.16.1.1
 
−
|255.255.255.0
 
−
|
 
−
|
 
−
|-
 
−
|G0/1.10
 
−
|172.16.10.1
 
−
|255.255.255.0
 
−
|
 
−
|
 
−
|-
 
−
|G0/1.20
 
−
|172.16.20.1
 
−
|255.255.255.0
 
−
|
 
−
|
 
−
|-
 
−
|G0/1.30
 
−
|172.16.30.1
 
−
|255.255.255.0
 
−
|
 
−
|
 
−
|-
 
−
|G0/1.40
 
−
|172.16.40.1
 
−
|255.255.255.0
 
−
|
 
−
|
 
−
|-
 
−
|G0/.1.99
 
−
|172.16.99.1
 
−
|255.255.255.0
 
−
|
 
−
|
 
−
|-
 
−
|S0/0/0
 
−
DCE
 
−
|10.16.1.1
 
−
|255.255.255.252
 
−
|
 
−
|
 
−
|-
 
−
|S0/0/1
 
−
|10.16.2.1
 
−
|255.255.255.252
 
−
|
 
−
|
 
−
|-
 
−
|S0/1/0
 
−
DCE
 
−
|10.16.3.1
 
−
|255.255.255.252
 
−
|
 
−
|
 
−
|-
 
−
|'''MS1'''
 
−
|VLAN 99
 
−
|172.16.99.11
 
−
|255.255.255.0
 
−
|
 
−
|Root primary for all VLANs
 
−
|-
 
−
|'''MS2'''
 
−
|VLAN 99
 
−
|172.16.99.12
 
−
|255.255.255.0
 
−
|
 
−
|Root secondary for VLAN  1, 10, 20
 
−
|-
 
−
|'''MS3'''
 
−
|VLAN 99
 
−
|172.16.99.13
 
−
|255.255.255.0
 
−
|
 
−
|Root secondary for VLAN 30, 40, 99
 
−
|-
 
−
|'''S1'''
 
−
|VLAN 99
 
−
|172.16.99.21
 
−
|255.255.255.0
 
−
|
 
−
|
 
−
|-
 
−
|'''S2'''
 
−
|VLAN 99
 
−
|172.16.99.22
 
−
|255.255.255.0
 
−
|
 
−
|
 
−
|-
 
−
|'''S3'''
 
−
|VLAN 99
 
−
|172.16.99.23
 
−
|255.255.255.0
 
−
|
 
−
|
 
−
|-
 
−
|'''S4'''
 
−
|VLAN 99
 
−
|172.16.99.24
 
−
|255.255.255.0
 
−
|
 
−
|
 
−
|-
 
−
|'''Server1'''
 
−
|G0
 
  
−
(vlan99)
 
−
|172.16.99.80
 
−
|255.255.255.0
 
−
|172.16.99.1
 
−
|
 
−
|-
 
−
|
 
−
|G1
 
  
−
(vlan99)
+
<br />
−
|
+
====Visualization of measure of variations on a Normal distribution====
−
|
+
 
−
|
+
 
−
|
+
<br />
−
|-
+
==Simple and Multiple regression==
−
|'''Server2'''
+
 
−
|G0
+
 
−
(vlan99)
+
<br />
−
|172.16.99.82
+
===Correlation===
−
|255.255.255.0
+
 
−
|172.16.99.1
+
 
−
|
+
<br />
−
|-
+
====Measuring Correlation====
−
|
+
 
−
|G1
+
 
−
(vlan99)
+
<br />
−
|
+
=====Pearson correlation coefficient - Pearson s r=====
−
|
+
 
−
|
+
 
−
|
+
<br />
−
|-
+
=====The coefficient of determination <math>R^2</math>=====
−
|'''PC1'''
+
 
−
|NIC
+
 
−
(vlan10)
+
<br />
−
|172.16.10.51
+
====Correlation <math>\neq</math> Causation====
−
|255.255.255.0
+
 
−
|172.16.10.1
+
 
−
|
+
<br />
−
|-
+
====Testing the "generalizability" of the correlation ====
−
|'''PC2'''
+
 
−
|NIC
+
 
−
(vlan20)
+
<br />
−
|172.16.20.52
+
===Simple Linear Regression===
−
|255.255.255.0
+
 
−
|172.16.20.1
+
 
−
|
+
<br />
−
|-
+
===Multiple Linear Regression===
−
|'''PC3'''
+
 
−
|NIC
+
 
−
(vlan30)
+
<br />
−
|172.16.30.53
+
===RapidMiner Linear Regression examples===
−
|255.255.255.0
+
 
−
|172.16.30.1
+
 
−
|
+
<br />
−
|-
+
==K-Nearest Neighbour==
−
|'''PC4'''
+
 
−
|NIC
+
 
−
(vlan40)
+
<br />
−
|172.16.40.54
+
==Decision Trees==
−
|255.255.255.0
+
 
−
|172.16.40.1
+
 
−
|
+
<br />
−
|-
+
===The algorithm===
−
| rowspan="2" |'''Wireless router0'''
+
 
−
|Internet setup
+
 
−
|172.16.10.101
+
<br />
−
|255.255.255.0
+
====Basic explanation of the algorithm====
−
|172.16.10.1
+
 
−
|
+
 
−
|-
+
<br />
−
|Network setup
+
====Algorithms addressed in Noel s Lecture====
−
|172.16.50.1
+
 
−
|255.255.255.0
+
 
−
|
+
<br />
−
|
+
=====The ID3 algorithm=====
−
|-
+
 
−
|'''Laptop1'''
+
 
−
|
+
<br />
−
|
+
=====The C5.0 algorithm=====
−
|
+
 
−
|
+
 
−
|
+
<br />
−
|-
+
===Example in RapidMiner===
−
|'''Laptop2'''
+
 
−
|
+
 
−
|
+
<br />
−
|
+
==Random Forests==
−
|
+
https://www.youtube.com/watch?v=J4Wdy0Wc_xQ&t=4s
−
|
+
 
−
|-
+
 
−
| colspan="7" | -
+
<br />
−
|-
+
==Naive Bayes==
−
| rowspan="4" |'''Limerik'''
+
 
−
| rowspan="3" |'''R2'''
+
 
−
|S/0/0/0
+
<br />
−
|10.16.1.2
+
===Probability===
−
|255.255.255.252
+
 
−
|
+
 
−
|
+
<br />
−
|-
+
===Independent and dependent events===
−
|S/0/0/1
+
 
−
DCE
+
 
−
|10.16.4.1
+
<br />
−
|255.255.255.252
+
===Mutually exclusive and collectively exhaustive===
−
|
+
 
−
|
+
 
−
|-
+
<br />
−
|G0/0
+
===Marginal probability===
−
|172.18.1.1
+
The marginal probability is the probability of a single event occurring, independent of other events. A conditional probability, on the other hand, is the probability that an event occurs given that another specific event has already occurred. https://en.wikipedia.org/wiki/Marginal_distribution
−
|255.255.255.0
+
 
−
|
+
 
−
|
+
<br >
−
|-
+
===Joint Probability===
−
|'''PC7'''
+
 
−
|NIC
+
 
−
|172.18.1.57
+
<br />
−
|255.255.255.0
+
===Conditional probability===
−
|172.18.1.1
+
 
−
|
+
 
−
|-
+
<br />
−
| colspan="7" | -
+
====Kolmogorov definition of Conditional probability====
−
|-
+
 
−
| rowspan="9" |'''Galway'''
+
 
−
| rowspan="3" |'''R3'''
+
<br />
−
|S0/0/1
+
====Bayes s theorem====
−
|10.16.4.2
+
 
−
|255.255.255.252
+
 
−
|
+
<br />
−
| rowspan="3" |'''Standby router in HSRP'''
+
=====Likelihood and Marginal Likelihood=====
−
Slow path
+
 
−
|-
+
 
−
|S0/1/1
+
<br />
−
|10.16.5.1
+
=====Prior Probability=====
−
|255.255.255.252
+
 
−
|
+
 
−
|-
+
<br />
−
|G0/1
+
=====Posterior Probability=====
−
|172.17.1.1
+
 
−
|255.255.255.0
+
 
−
|
+
<br />
−
|-
+
===Applying Bayes' Theorem===
−
| rowspan="2" |'''R4'''
+
 
−
|S0/0/0
+
 
−
DCE
+
<br />
−
|10.16.6.1
+
====Scenario 1 - A single feature====
−
|255.255.255.252
+
 
−
|
+
 
−
| rowspan="2" |'''Active router in HSRP'''
+
<br />
−
(Because the other path is slow)
+
====Scenario 2 - Class-conditional independence====
−
|-
+
 
−
|G0/0
+
 
−
|172.17.1.2
+
<br />
−
|255.255.255.0
+
====Scenario 3 - Laplace Estimator====
−
|
+
 
−
|-
+
 
−
|'''Switch0'''
+
<br />
−
|VLAN 1
+
===Naïve Bayes - Numeric Features===
−
|172.17.1.6
+
 
−
|255.255.255.0
+
 
−
|<code>172.17.1.254</code> (virtual IP for <code>HSRP</code>) <s>172.17.1.1</s>
+
<br />
−
|
+
===RapidMiner Examples===
−
|-
+
 
−
|'''Switch1'''
+
 
−
|VLAN 1
+
<br />
−
|172.17.1.7
+
==Perceptrons - Neural Networks and Support Vector Machines==
−
|255.255.255.0
+
 
−
|<code>172.17.1.254</code> (virtual IP for <code>HSRP</code>)  <s>172.17.1.2</s>
+
 
−
|
+
<br />
−
|-
+
==Boosting==
−
|'''PC5'''
+
 
−
|NIC
+
 
−
|172.17.1.55
+
<br />
−
|255.255.255.0
+
===Gradient boosting===
−
|<code>172.17.1.254</code> (virtual IP for <code>HSRP</code>)  <s>172.17.1.1</s>
+
 
−
|
+
 
−
|-
+
<br />
−
|'''PC6'''
+
==K Means Clustering==
−
|NIC
+
 
−
|172.17.1.56
+
 
−
|255.255.255.0
+
<br />
−
|<code>172.17.1.254</code> (virtual IP for <code>HSRP</code>)  <s>172.17.1.2</s>
+
===Clustering class of the Noel course===
−
|
+
 
−
|-
+
 
−
| colspan="7" | -
+
<br />
−
|-
+
====RapidMiner example 1====
−
| rowspan="4" |'''Cork'''
+
 
−
| rowspan="3" |'''R5'''
+
 
−
|S0/0/0
+
<br />
−
|10.16.6.2
+
==Principal Component Analysis PCA==
−
|255.255.255.252
+
 
−
|
+
 
−
|
+
<br />
−
|-
+
==Association Rules - Market Basket Analysis==
−
|S0/0/1
+
 
−
DCE
+
 
−
|10.16.2.2
+
<br />
−
|255.255.255.252
+
===Association Rules example in RapidMiner===
−
|
+
 
−
|
+
 
−
|-
+
<br />
−
|G0/0
+
==Time Series Analysis==
−
|172.19.1.1
+
 
−
|255.255.255.0
+
 
−
|
+
<br />
−
|
+
==[[Text Analytics|Text Analytics / Mining]]==
−
|-
+
 
−
|'''PC8'''
+
 
−
|NIC
+
<br />
−
|172.19.1.58
+
==Model Evaluation==
−
|255.255.255.0
+
 
−
|172.19.1.1
+
 
−
|
+
<br />
−
|-
+
===Why evaluate models===
−
| colspan="7" | -
+
 
−
|-
+
 
−
| rowspan="4" |'''Sligo'''
+
<br />
−
| rowspan="3" |'''R6'''
+
===Evaluation of regression models===
−
|S0/1/0
+
 
−
|10.16.3.2
+
 
−
|255.255.255.252
+
<br />
−
|
+
===Evaluation of classification models===
−
|
+
 
−
|-
+
 
−
|S0/1/1
+
<br />
−
DCE
+
===References===
−
|10.16.5.2
+
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===[[NumPy and Pandas]]===
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===[[Data Visualization with Python]]===
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===[[Text Analytics in Python]]===
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===[[Dash - Plotly]]===
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===[[Scrapy]]===
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==[[R]]==
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===[[R tutorial]]===
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==[[RapidMiner]]==
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==Assessments==
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===Diploma in Predictive Data Analytics assessment===
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==Notas==
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==References==
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Latest revision as of 21:50, 10 March 2021



aver


Contents

Projects portfolio


Data Analytics courses


Possible sources of data


What is data


Qualitative vs quantitative data


Discrete and continuous data


Structured vs Unstructured data


Data Levels and Measurement


What is an example


What is a dataset


What is Metadata


What is Data Science


Supervised Learning


Unsupervised Learning


Reinforcement Learning


Some real-world examples of big data analysis


Statistic


Descriptive Data Analysis


Central tendency


Mean


When not to use the mean


Median


Mode


Skewed Distributions and the Mean and Median


Summary of when to use the mean, median and mode

measures-central-tendency-mean-mode-median-faqs.php



Measures of Variation


Range


Quartile


Box Plots


Variance


Standard Deviation


Z Score


Shape of Distribution


Probability distribution


The Normal Distribution


Histograms


Skewness


Kurtosis


Visualization of measure of variations on a Normal distribution


Simple and Multiple regression


Correlation


Measuring Correlation


Pearson correlation coefficient - Pearson s r


The coefficient of determination


Correlation Causation


Testing the "generalizability" of the correlation


Simple Linear Regression


Multiple Linear Regression


RapidMiner Linear Regression examples


K-Nearest Neighbour


Decision Trees


The algorithm


Basic explanation of the algorithm


Algorithms addressed in Noel s Lecture


The ID3 algorithm


The C5.0 algorithm


Example in RapidMiner


Random Forests

https://www.youtube.com/watch?v=J4Wdy0Wc_xQ&t=4s



Naive Bayes


Probability


Independent and dependent events


Mutually exclusive and collectively exhaustive


Marginal probability

The marginal probability is the probability of a single event occurring, independent of other events. A conditional probability, on the other hand, is the probability that an event occurs given that another specific event has already occurred. https://en.wikipedia.org/wiki/Marginal_distribution



Joint Probability


Conditional probability


Kolmogorov definition of Conditional probability


Bayes s theorem


Likelihood and Marginal Likelihood


Prior Probability


Posterior Probability


Applying Bayes' Theorem


Scenario 1 - A single feature


Scenario 2 - Class-conditional independence


Scenario 3 - Laplace Estimator


Naïve Bayes - Numeric Features


RapidMiner Examples


Perceptrons - Neural Networks and Support Vector Machines


Boosting


Gradient boosting


K Means Clustering


Clustering class of the Noel course


RapidMiner example 1


Principal Component Analysis PCA


Association Rules - Market Basket Analysis


Association Rules example in RapidMiner


Time Series Analysis


Text Analytics / Mining


Model Evaluation


Why evaluate models


Evaluation of regression models


Evaluation of classification models


References

Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977 Mar;33(1):159-174. DOI: 10.2307/2529310.



Python for Data Science


NumPy and Pandas


Data Visualization with Python


Text Analytics in Python


Dash - Plotly


Scrapy


R


R tutorial


RapidMiner


Assessments


Diploma in Predictive Data Analytics assessment


Notas


References


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