Network Redesign Project: Objectives, Scope And Hardware Selection

Executive Summary

An Australian based data analytics company, XYZ, opening new branch offices on Osaka and San Francisco. These offices require new equipment and thus there is need to redesign the existing network so as to meet the new requirements. The project is aimed at ensuring that all the branches are able to access real time data without delay. As such network redesign is necessary in order to ensure that every user is able to work independently on the shared network. The new network will ensure reliability, efficiency, security of the data and information, and scalability (Kuipers, 2012, p. 18). This are the major aspects that every network should meet. XYZ is a company that deals with data analytics and data is a very fragile and critical resource, therefore, security, privacy, and confidentiality is key.

The network will be able to meet these aspects. The main objective of the project is to assess and redesign the existing network so as to meet the new requirements as the company grows, that is, allow the new offices to access the network resources efficiently and effectively. Secondly, the network should ensure that communication is effective and reliable. Thirdly, sharing of resources (Mbale & Mufeti, 2011, p. 1). Fourthly, is load balancing to provide a way to increase the overall system performance, and finally to allow only authorized users to access the network.

PART 3 – Subnet the network using VLSM, and assign IP addresses

Table 1. Subnets

Subnet name	Subnet address	Subnet mask	First useable address	Last useable address	Broadcast address	Static address range	DHCP address range
Seattle	172.10.0.0	/20	172.10.0.1	172.10.24.254	172.10.24.255	172.10.0.1-172.10.0.2	172.10.0.3 – 172.10.24.254
Hong Kong	172.10.15.0	/21	172.10.15.1	172.10.33.254	172.10.33.255	172.10.15.1	172.10.15.2 – 172.10.33.254
Osaka	172.10.95.0	/22	172.10.95.1	172.10.37.254	172.10.37.255	172.10.95.1	172.10.95.2- 172.10.37.254
Seoul	172.10.38.0	/22	172.10.38.1	172.10.21.254	172.10.21.255	172.10.38.1	172.10.38.2- 172.10.21.254
Vancouver	172.10.22.0	/23	172.10.22.1	172.10.23.254	172.10.23.255	172.10.22.1	172.10.22.2 – 172.10.23.254
San Francisco	172.10.98.0	/25	172.10.98.1	172.10.98.126	172.10.98.127	172.10.98.1	172.10.98.1 – 172.10.98.126
Seoul_Server	172.10.98.128	/29	172.10.98.129	172.10.98.134	172.10.98.135	172.10.98.129-172.10.98.131	172.10.98.132-172.10.98.134
Osaka_Server	172.10.98.136	/29	172.10.98.137	172.10.98.142	172.10.98.143	172.10.98.137-172.10.98.139	172.10.98.140-172.10.98.142
WAN SEO-Hong Kong	172.10.98.144	/30	172.10.98.145	172.10.98.146		172.10.98.145-172.10.98.146
WAN SEO-Seattle	172.10.98.148	/30	172.10.98.149	172.10.98.150		172.10.98.149-172.10.98.150
WAN SEO-Osaka	172.10.98.152	/30	172.10.98.153	172.10.98.154		172.10.98.153-172.10.98.154
WAN SEA-Vancouver	172.10.98.160	/30	172.10.98.161	172.10.98.162		172.10.98.161-172.10.98.162
WAN SEA-San Francisco	172.10.98.164	/30	172.10.98.165	172.10.98.166		172.10.98.165-172.10.98.166
WAN SAN-Vancouver	172.10.98.168	/30	172.10.98.169	172.10.98.170		172.10.98.169-172.10.98.170

Table 1: Subnet Table

Table 2. Router Interfaces

Location	Interface	IP address	Subnet mask
Seoul	S0/0	104.200.16.26	/30
S0/1	172.10.98.153	/30
S0/2	172.10.98.149	/30
S0/3	172.10.98.145	/30
Fa 0/0	172.10.38.1	/22
Fa 0/1	172.10.96.129	/29
Hong Kong	S0/0	172.10.98.146	/30
Fa 0/0	172.10.15.1	/21
Seattle	S0/0	172.10.98.150	/30
S0/1	172.10.98.161	/30
S0/2	172.10.98.165	/30
Fa 0/0	172.10.0.1	/20
Vancouver	S0/0	172.10.98.162	/30
S0/1	172.10.98.170	/30
Fa 0/0	172.10.22.1	/23
San Francisco	S0/0	172.10.98.166	/30
S0/1	172.10.98.169	/30
Fa 0/0	172.10.98.1	/25
Osaka	S0/0	172.10.98.154	/30
S0/1	172.10.98.157	/30
Fa 0/0	172.10.95.1	/22
Fa 0/1	172.10.96.137	/29

Table 2: Router Interfaces

Table 3. Servers

Location	Server name	IP address	Subnet mask
Osaka	DB	172.10.96.138	/29
USER	172.10.96.139	/29
Seoul	WEB	172.10.96.130	/29
MAIL	172.10.96.131	/29

Table 3: Servers

PART 4 – Research and source appropriate devices justifying choices

The devices being planned to be procured include two routers for Tokyo and Osaka, switches for every LAN and one for the servers located in Osaka, two servers (User and DB located in Osaka), and 200 PCs (150 for San Francisco and 50 for Osaka). The most critical devices amongst are the servers because of the critical and vulnerable resource, data for XYZ data analytics company which hold a lot of data for clients (Di Corato, 2013). Data analysis is a very essential process and requires a computer with relatively high performance especially the processor, hard disk, and RAM. Finally, it is important to consider affordable but reliable devices.eighted Decision Matrix – Hardware resource requirements analysis.

The weights below apply for all the devices.

0	The scenario does not require the requirements
1	The requirements are not very essential
3	The requirements should be met
5	The requirements are very critical

0	The requirements have not been met totally
2	The requirements have been met partially
4	All the requirements have been met
6	The requirements have been met and surpassed

Requirements

Requirement	Description	Weight
Speed	1000MBPS transfer speed	5
Price	Not more than $400	1
Firewall	Should be available	3
Ports	Minimum of 2 ports and 4 ports respectively	3

Details

Model	Ports	Speed (Mbps)	Cost ($)	Firewall
Cisco RV325	16	1000	545	yes
Net Gear FVS336G	2	1000	350	Yes
Cisco RV042-AU	4	100	185	no
Linksys LRT214-AU	6	1000	215	Yes
Cisco RV042G-K9-AU	4	1000	300	Yes

Weight Score

Requirement	No. Ports	Speed (Mbps)	Cost ($)	Firewall	Total weight score
Description	>=4	>=1000	<400	Yes
Weight	3	5	1	3

Cisco RV325	6	4	0	4	50
Net Gear FVS336G	0	4	4	4	36
Cisco RV042-AU	4	0	4	0	16
Linksys LRT214-AU	6	4	6	4	56
Cisco RV042G-K9-AU	4	4	4	4	48

Requirement

Requirement	Description	Weight
Cost	<4500 for all	3
Speed	100M	3
Port number	>=16	5
Manageable	Yes	1

Details

Model	No. Ports	Speed (Mbps)	Cost ($)/per device	Management
Cisco SF 200E-24P	24	10/100/1000	387	Yes
HP J9981A 1820-48G	48	10/100	540	Yes
Cisco SF 100-24	24	10/100	105	Yes
Cisco SF 100D-16	16	10/100	80	No
D-Link DGS-1100-24	24	10/100/1000	200	Yes

Need number = 150/port number + 80/port number + 1 (for the main switch)

Weight Score:

Requirement	No. Ports	Speed (Mbps)	Cost ($)	Management	Total weight score
Description	>=16	>=100	<4500	Yes
Weight	5	3	3	1

Cisco SF 200E-24P	4	4	0	4	36
HP J9981A 1820-48G	6	6	2	4	52
Cisco SF 100-24	4	4	4	4	50
Cisco SF 100D-16	4	4	6	0	50
D-Link DGS-1100-24	4	6	4	4	54

Requirement

Requirement	Description	Weight
Cost	< 3500 $	1
CPU	>= quad core	5
RAM	>=16GB	5
Network Controller	1000Mbps	3

Details

Model	Cost($)	Processor	Memory	Network Controller
Lenovo 5457C5M x3100 M5	1800	Intel quad Core-3.4 GHz	8GB	Gigabit Ethernet
HP ProLiant ML10	1599	Intel Xeon-3.1GHz	8GB	HP Ethernet 1Gb 2-port 332i Adapter
LENOVO x3100	1750	Intel Xeon quad Core-3.1 GHz	16GB	Gigabit Ethernet
Cisco UCS C220 M3 SFF	899	Intel E5-2620v2(6 Core) -2.10 GHz	32GB	Ethernet 1Gb
HP 778641-B21 DL80 Gen9	3099	Intel Xeon 6 core-1.9 GHz	16GB	Ethernet Adapter -1Gb 361i

Weight Score

Requirement	Cost($)	Processor	Memory	Network Controller	Total weight score
Description	<=3500	>= quad Core	>=16G	>=1000Mbps
Weight	1	5	5	3

Lenovo 5457C5M	6	4	0	4	38
Cisco UCS C220 M3	4	6	6	4	76
HP 778641-B21	4	6	4	4	66
HP ProLiant ML10	6	0	0	4	18
HP J9981A 1820-48G	6	4	4	4	58

Requirement

Requirement	Description	Weight
Cost	<= 1500 $	5
CPU	>= core i7	5
RAM	>=8GB	3
ROM	>=500GB	3

Details

Model	Cost($)	Processor	Memory	Storage
Intel core i7 6600 Turbo Pack	750	Core i7 quad core 3.3GHz	8GB	1TB
Umart ASUS Vivo	659	Asus VM62-G039M	8GB	500GB
Intel i7 6700 Extreme Pack	1350	Intel Core i7 6700 Quad Core 3.4GHz	16GB	2TB
Intel XEON workstation	3599	Intel Xeon E5-2630v3 CPU – 2.4Ghz	16GB	2 x Hitachi 1TB
Intel Core i3 4170 Value Pack	599	Intel Core i3 4170 3.6Ghz	4GB	500GB

Weight Score:

Requirement	Cost($)	Processor	Memory	Storage	Total weight score
Description	<=1500	>=i7	>=8GB	>=500G
Weight	5	5	3	3

Intel i7 6600 Turbo Pack	4	4	6	6	76
Umart ASUS Vivo PC Pack	6	4	4	4	74
Intel i7 6700 Extreme Pack	4	6	6	6	86
Intel XEON CAD Workstation	0	6	6	6	66
Intel Core i3 4170 Value Pack	6	0	4	4	54

Item No	Item	Quantity	Price ($)	Total Cost ($)
1	Router-Linksys LRT214-AU	2	215	430

2	Switch-D-Link DGS-1100-24	6	200	1200

3	Sever- Cisco UCS C220 M3 SFF 2xE5-2620v2 2x	2	899	1798
4	PCs- Intel i7 6700 Extreme Pack	10	1350	13500
TOTAL BUDGET	16,928

The budget set out for this project was $10,000 but based on the requirements and the needs of the new offices the budget have exceeded by $6,928. However, this cost is worth it because affordable, more reliable, and secured devices have been selected. Thus, over time there is minimal maintenance required on these devices.

PART 5 – Cloud computing proposal

Currently, there are several cloud service providers around the world like Amazon, Alibaba, Microsoft Azure, Google, and many others can choose from. Cloud computing offer centralized access to resources and the company will not have to worry about the management of resources (Hoque & Gupta, 2012, p. 29). Replacing all workstations with thin clients connect to the cloud reduces operational costs by a big margin. Thin clients are computers that connected to a network with no local storage.

Thin clients run virtual desktops using virtualization technologies from Citrix, Microsoft, and VMware in remote servers, either in-house or outsourced (Karyakina & Melnikov 2017, p. 253). Shifting to cloud computing using thin clients reduces the costs, enhance data security, improve overall performance, reduces energy consumption and cuts power costs by up to 80%, and offer efficient and effective remote management (Wickboldt et al., 2015, p. 281). The table below give a comparison of the three scenarios in order to make a recommendation:

Comparison Table

Technology	Operational Cost	Energy Consumption	security	Flexibility and Scalability
Cloud Serviced provider accessed through thin clients	No initial cost, monthly or annual subscription. Management of infrastructure is done by the cloud service provider (Wolf, 2010, p. 9). Cost of subscription compared for three providers: -Amazon $320 per thin client per year -Microsoft Azure $400 per thin client per year -Alibaba $270 per thin client per year	Power cost can be reduce by up to 80% because thin client are endorsed by green energy thus consumes and emits small amount of energy (Wang, 2014, p. 172).	Less secure. Data security is managed by the cloud provider and there is no assurance that no third parties will access the data (Movassaghi, Abolhasan, & Lipman, 2011).	Can be modified easily without affecting the services. Easily scalable
In-house cloud infrastructure accessed through	High initial cost of about $428000 to set up an in-house cloud infrastructure in Saul. However, other subsequent costs are reduced.	Energy consumption reduced by up to 60%. Workstations that consume a lot of energy is done away with because access will be done through thin clients that consumes less energy (Wang, 2014, p. 172).	The company is responsible for its data security. Accessed to data centres are controlled and thus makes it easier to protect and monitor access to critical data.	Its flexible but requires redesigning for it to be scaled.
Physical workstations	It is expensive compared to the two alternatives. The company currently pays $850 per workstation to the contractor.	Consumes a lot of energy because all the workstations need power for them to be powered. Each work station consumes 230kWh per year.	Relatively secured, however because of lack of centralize data management it might be a challenge to detect a bridge.	Not easily scalable, require procurement of new equipment and maybe it may need the whole network to be redesigned to meet the new requirements.

Recommendation

The company has 765 computers meaning that there are 765 employees that should have access to the data. The table below gives a breakdown of the cost of each choices

Technology	Operational Cost
Cloud Serviced provider accessed through thin clients	-Amazon $320 x 765= $244,800 -Microsoft Azure $400 x 765= $306,000 -Alibaba $270 x 765= $206,550
In-house cloud infrastructure accessed through	High initial cost of about $248,000
Physical workstations	$850 x 765= 650, 000

From the above statistics and the analysis done considering other factors such as security, energy consumption, and scalability, it is recommended that the company go for in-house cloud infrastructure accessed through thin clients. Initial cost may be high but subsequent costs will be greatly reduced. Furthermore, energy consumption has been reduced by up to 60% making it environment friendly. Also, XYZ is a data analytics company meaning that data security is their major concern and this option offers better security compare to the other two.

Reference List

Di Corato, G. (2013). The Internet Service Provider Liability in Online Trademarks Infringement. SSRN Electronic Journal.

Hoque, I. and Gupta, I. (2012). Disk Layout Techniques for Online Social Network Data. IEEE Internet Computing, 16(3), pp.24-36.

Karyakina and Melnikov (2017). Comparison of methods for predicting the customer churn in Internet service provider companies. Machine Learning and Data Analysis, 3(4), pp.250-256.

Kuipers, F. (2012). An Overview of Algorithms for Network Survivability. ISRN Communications and Networking, 2012, pp.1-19.

Mbale, J. and Mufeti, K. (2011). Phase teaching model for subnetting IPv4. International Journal of Internet Technology and Secured Transactions, 3(1), p.1.

Movassaghi, S., Abolhasan, M. and Lipman, J. (2011). Addressing Schemes for Body Area Networks. IEEE Communications Letters, 15(12), pp.1310-1313.

Wang, J. and Wang, Q. (2014). Analyzing and predicting software integration bugs using network analysis on requirements dependency network. Requirements Engineering, 21(2), pp.161-184.

Wickboldt, J., De Jesus, W., Isolani, P., Both, C., Rochol, J. and Granville, L. (2015). Software-defined networking: management requirements and challenges. IEEE Communications Magazine, 53(1), pp.278-285.

Wolf, T. (2010). In-network services for customization in next-generation networks. IEEE Network, 24(4), pp.6-12.

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