Introduction - University of Colorado Colorado...

Methodology for Choosing Datacenter for Cloud Providers in West Africa

Region

ByAhmed Alharthi

Bachelor of Science in Computer Science FBSU, Saudi Arabia2009

A ProjectSubmitted to the Graduate School Faculty of Graduate School of the

University of Colorado at Colorado SpringsIn Partial Fulfillment of the Requirements

For the Degree of Master of ScienceDepartment of Computer Science

Spring 2014

This project for the Master of Science degree byAhmed Alharthi

Has been approved for theDepartment of Computer Science

By

________________________________________________________________Advisor: Dr. C. Edward Chow

________________________________________________________________Dr. Jia Rao

________________________________________________________________Dr. Kristen Walcott-Justice

_________________________________________

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Date

List of Content Chapter 1 Introduction................................................................................................... 41.1 Aim and significant of the research...........................................................51.2 Problem description...................................................................................61.3 Targeted Audience…………………………………………………….…..6 1.4 Related Work: ...........................................................................................7 Chapter 2 2.1 Why Africa............................................................................................... 92.2. Growing demand……………………………………………..……….....102.3 African Submarine cables …………………………..………...………….112.4 Geographical Characteristics on the region............................................. 13 2.5 Politic Systems on the region...................................................................16 2.6 Economic status in the region................................................................. 17Chapter 3The proposed methodology......................................................................... 193.1 Decision criteria....................................................................................... 203.2 intensive measurements for the potential location .................................. 233.3 Observations……………………………………………………………...283.4 Estimating the Coast of Space, Cooling and Powering Data center……..29 Chapter 4 DiscussionSelection results …………...........................................................................414.1 Comparing network performance…………………………………...……414.2 Comparing Space, Power and Cooling costs……………..………………45 4.3 Politics System & economy affects cloud................................................464.4 Number of users………………………………………………………......464.5 Submarine cables…………………………………………………………39Chapter 5 Conclusion …………………………………………………………….……48Chapter 6Lessons Learned.......................................................................................... 49Chapter 7Bibliography……………………………………….……………………..…52

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Chapter 1Introduction

1. Introduction

This project report gives a description of the research and process for selecting the best location to build data centers for cloud providers in West Africa. We study and analyze different factors when considering the best location for a cloud provider to choose a data center in a region [1]. This project analyzes countries in West Africa as a potential choice for hosting the data center which serves the region.

There are many studies, reports and expert demonstrations on what are the most important elements in deciding the best location to build a data center for a cloud provider. There are different factors to be considered. Among them, the most important factors are network capability and cost of setting up a data center. They are the most critical business decision for building and maintaining the infrastructure required. IT businesses, small and large are similar in the way they make decisions. In order to make the best decision for their company, researchers and management require sufficient information before arriving at a decision and thus the purpose of this project. In this project, we attempt to go through the decision criteria and options available for IT businesses which are looking to build their data centers in West Africa. This project attempted to produce a detailed list of the most important factors [2, 3, 4]. The decision for include

a. Cost of data center includes Buildings.

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Electricity Landscape ( per square foot)

b. Data center distance from submarine cable c. Network capability includes

Network latency (RTT) Throughput Average Typical Packet Loss

d. Environmental factors e. Economic stability.f. Politics System.g. Ease of can access to the suggested location.

The criteria for selecting a data center for a cloud provider could include choosing a country with lowest cost for setting up a data center, a city with the best network capabilities, a country with stable economic and political system/climate, and so on.

This project discusses the various risk factors that may impact data centers especially the geography in Africa. Similarly we also consider the probabilities of natural disasters such as floods, tornados and hurricanes. For example, many companies choose Colorado for hosting their data centers because the geographic advantage of Colorado with low threat from those natural disaster. There are other issues that should be considered in this project, such as the political situation in these countries. All these factors will be considered during this research.

1.1 Goal and significant of the research

Goal

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To develop a methodology to suggest the best location for setting up data center in West Africa region based network capability and the costs of space, power, and cooling for the datacenter.

Significance of the Research

This research is important in the current age of technological advancement. Currently, IT is a major factor that boosts the economy of most countries. The demand for fast and reliable internet connectivity is growing. This demand helps in connecting various countries and making the world to be a global village.

West Africa is a growing IT hub and it is no exception for it to be connected to the rest of the world. Currently data providers are limited in this region. For this reason, the economy is boosted mostly by agriculture and industries. This therefore creates a good environment for setting up a new data center. New data centers assure a major market share in the region for their users because they improve delivering services for customers. This in turn will help to create more IT related businesses. At the same time the rolling out of new internet connectivity will help dispense information [5].

This will help to boost other related sectors of the economy also. Education will be greatly improved as stakeholders will be able to carry out more innovative ideas. The information and technology sector, mostly media houses, will be able to improve. They will be able to create more digital content and at the same time create more job opportunities [6]

Cloud providers can therefore look at the vast opportunities to invest and expand their presence. At the same time, they will be able to create more revenue and profits. Many businesses depend on data centers. The availability of data centers will surely affect everybody in the region. For this reason West Africa will be the new investment hub that investors won’t ignore [7].

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1.2 Problem description There is lack of sufficient research and information about Africa regarding possible best locations in which data centers can be set up to aid in the globalization movement leading to ease of doing business and world trade. For this reason, this research paper will focus on West Africa region by analyzing and evaluating one of the following countries with an aim of proposing the best location for any company to base its data center within these geographical regions:

Senegal, Guinea, Liberia, Ghana, Togo, Benin, Sierra Leone, Nigeria, Cameron, Cote d'ivoire, Congo, Angola, Democratic Republic of the Congo and Namibia.These countries have been chosen since they lie next to the large water bodies and these oceans and seas are serviced by marine cables such as fiber optic which are part of a major internet and communication infrastructure [8]. Furthermore, cloud providers are looking an in-depth evaluation of these countries since they plans on having a data center in these parts of the world to help the company with its service delivery and expansion plans into new markets and regions.

1.3 The targeted audienceThe targeted audience for this research is any one conducting research

in cloud computing and the related facility location problem, in particular to those cloud providers interested in setting up data centers in West Africa. For example, Amazon EC2 is planning to expand their operations into new regions and based on their current market presence as shown in Figure 1 Amazon EC2’s global infrastructure.

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Figure 1. Amazon EC2 global infrastructures [1]Amazon EC2 currently does not have data centers in Africa. Any one

intends to do that needs a critical evaluation of geographical, risk mitigation, natural disaster factors and man-made situation factors within the listed countries in order to determine the best location for their new data center. The Amazon EC2 currently does not have data centers in Africa. Any one intends to do that needs a critical evaluation of geographical, risk mitigation, natural disaster factors and man-made situation factors within the listed countries in order to determine the best location for their new data center. Although the paper will give a clear and precise location of the most suitable location based on the proposed metrics, the final decision on where the data center will be located will depend on other factors such as demands and financial situations.

1.4 Related workIn this section we will analyze any previous work concerning the suitability of locating data centers. For example, John Rath has presented a work that attempt to breaks down the decision criteria and options available to companies who are looking to build data center. His work presents an

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exhaustive list of potential factors to be weighed when doing site selection analysis [2].

IBM has well explained documents in data center strategies. For example, how to meet a growing need for more data center capacity, the IBM Canada Leadership Data Centre leverages best practices developed in designing, building and operating data centers around the world [9].

Federico Larumbe and Brunilde Sans`o in their work on Cloud Location and Routing problem (CLRP) [10] formulated a mathematical problem that is looking to compute geographical distribution on multiple regions, the location of data center, servers and software components. Their results underline the importance of location and routing showing that the budget and number of datacenters have critical impact on overall network performance.

Khosrow Ebrahimi, Gerard F. Jones, and Amy S. Fleischer presented a comprehensive review of data center cooling technology, operations conditions and corresponding availability of data center waste heat sources and stream[11].

In terms of Africa’s network connectivity, M. Zennaro, E. Canessa, K.R. Sreenivasan , A.A. Rehmatullah and R.L. Cottrell used Ping methodology and obtained data to quantify the difference in performance between developed and developing countries [12].

Linda K. Nozick, Mark A. Turnquist presented a model which forces to answer these questions: how many data center should be in a place? Where should be located? Also, the p-dispersion problem is closely related to this proposal. The p-dispersion problem seeks to locate p facilities in an area or a graph such that the minimal distance between two facilities is maximized [13].

There are other researches on the strategies of picking up data center

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locations for Africa. In same time, there are reports about the most important factors for choosing data center locations in United States for army and enterprise [2, 4]. Also, there are different journals on different factors for preventing specific locations [3].

Chapter 2Why Africa

2.1 Why AfricaData center in Africa is expanding fast. In 2011 alone, there were more than 20 date center facilities began construction in several regions such as, Uganda, Reunion, Ghana, Benin, Zimbabwe, Cameron, Angola, Burkina Faso and Kenya. Telecoms operators are jumping on the bandwagon, trying to capitalize on the opportunity to lock in their corporate customers further, without always recognizing the difficult road ahead [14]. Few years ago, the world believed that all the hype and buzz that surrounding cloud computing and data center had skipped Africa. However, developments indicate that the Dark Continent is facing a huge demand in term of data services, but still not enough comparing to other continents. For example, According to data center map, Africa has 32 datacenters and 18 of which are located in South Africa. On the other hand, country like United States has 1242 data center alone. Figure 2 and 3 show the number

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of data center in both Africa and countries around the world [15]

Figure 2. Number of data centers in African [15].

Figure 3. Number of data center around the world [15].

Figure 2 show that data center is growing business in Africa. Internet penetration continues to increase across the continent fuelling data services demand and lowering latency. Corporation and cloud providers are becoming aware of the advantages of having data centers in Africa. Outsourcing provides them to focus on their core activities, while worries about unreliable power and poor security can be assigned to a committed

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third party that will assure the required uptime and data integrity. Finally, data centre outsourcing gives cost savings, and a capacity to rapidly react to changing market needs.

2.2 Growing demand

TeleGeography’s Global Bandwidth Forecast Services has reported that Africa is expected to drive the world in international bandwidth demand growth, in the few coming years. Africa’s demand is expected to grow to an annual rate of 51% between 2012 -2019. That is a huge demand comparing to Latin America and Middle East which are expecting to grow 37% annually. Angola is expected to grow 71% annually over the next seven years, Tanzania 68% and Gabon which is expected to grow 67%.Africa still has lower bandwidth comparing to the expected annually grow percentage comparing with rest of the world. Africa as a continent expected to reach 17.2 Tbps in 2019. That is only 1/4 of the expected demand of South America and less than that of Canada. Figure 4 demonstrates the growing demand in the world [26].

Figure 4. The international demand for bandwidth [16].

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2.3 Africa Submarine cables

“Africa is surrounded by networks of submarine communications cable systems that will play a major part in supporting the continent's future growth” [16].

For years specially equipped ships tasked with distributing submarine communication cables for the last five years. These ships have been navigating new routes off the both east and west of the Dark Continent. These submarine cables provide high–speed, high capacity fiber optic to play an integral part in promoting digital links that will be the backbone of Africa’s information infrastructure for the future. Figures 5 and 6 show the submarine cables have increased in number between 2010 -2012.

Figure 5. Submarine cables around Africa in 2010.

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Figure 6. Submarine cables around Africa in 2012 [16].The previous two figures show how fast counties and corporation in Africa to connect to the global network. In two years, the western coast of Africa jumps from having two submarine cables to five cables with a capacity from 340 gigabits to 5120 gigabits. By the end of 2014, combined service and cables capacities will increase to 67Tb/s on the west coast and 22Tb/s on the east. These submarine cables reach different landing points, starting from Cape Town in South Africa and crossing the west coast of Africa to UK. Although these landing points countries have shore sites are located to gain most from begin physically linked. The goal of these sites is to provide feed through and landlocked countries to enable these countries to build their own networks and connect to the world [16].

2.4 Geographical & Climate Characteristics of the regionGeographical factors are mostly overlooked in the process of deciding where the best location to build data center is. Bad climate, tsunami,

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hurricane, and earthquake could be damaging to the data centers or their operation. Site selection is the key for most companies, not only because the companies will save critical systems and platforms but also to keep their operations expenses on average. However, West Africa region has a combination of different geographical and climate patterns. This project goes through these pattern's characteristics because it has a strong relationship with the cost of operating data center. Hotter weather means increasing in the cost of cooling data center. The following sections will provide an overview of these factors:

Climate & topography of the continent In general, West Africa has four climate zones, the Tropical, the Savanna, the Sahara and the Sahel. That makes the weather ranges from lush tropical rainforest to arid desert, while, in the center and few southern areas, there is a combination of savanna and rainforest climate. In fact, Africa considers being the hottest continent on earth [17]. Figure 7 shows the different patterns in weather where it is wetter in the south and drier at north.

Figure 7. Weather patterns on Africa [17].

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Tornados & Hurricanes:Hurricanes and tornadoes are both stormy atmospheric systems that have the potential of causing destruction. They are caused due to instability in atmospheric conditions. According to the region and severity of stormy conditions, these storms may be referred to as typhoons or tropical cyclones [27]. Tornado and Hurricanes can create stress and dramatic issue for data center. For example, what happened in hurricane Katrina 2005 made a huge impact on datacenters that are located in New Orleans. Prior to hurricane Katrina, the department of Agriculture moved its data center from New Orleans to Kansa City, Missouri [18]. Tornado does accrue where a thunderstorm is possible, and there is history of places have been struck with tornados and Hurricanes [19]. Still hurricanes and tornados are rare because they typically get weak at the cost line of West Africa and travel west of the Atlantic Ocean. That makes West Africa safer location for this kind of facilities.

EarthquakeEarthquakes occur every day around the world, but large ones and potential for future large quakes. These large earthquakes need to be taken into data center site location consideration. The west coast of Africa considers being safer form these quakes comparing to other continents. There are countries in the west coast of Africa got hit by earthquakes, such as, the Democratic Republic of the Congo and South Africa. In general, Africa as a continent has the lower number of earthquakes comparing to other continents, South Africa is the spotlight of earthquakes in the continent follow by Egypt [19]. Figure 18 shows the major earthquakes in the world and clearly showing that Africa is totally out of the spot.

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Figure 8. Major earthquakes in the world since 1900 [20].

Tsunami Since this project is targeting the west coast line of Africa, which facing the Atlantic Ocean, Tsunami has to be consider as a major threat to data center location. As 2004 tsunami that wreaked death and destruction in Indonesia, it had reached the east coast of Africa and struck Somalia, Kenya and reached South Africa [21]. East coast of Africa is out the scope of this study. We focus on the west side of the continent. Since tsunami waves has relationship with earthquakes the chance of a tsunami in the west coast are lower than the east coast.

2.5 Politic Systems on the regionThere is a variety of politics systems in West Africa. These politics system have copied itself from Europe as a result of been colonist. The majority of these systems are using Republics with a semi-presidential system of government as Ghana, Cape Verde, the Democratic Republic of the Congo,

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Guinea, Mauritania and Senegal. Other politics systems in West Africa have Federal republics systems like what is rolling Nigeria. Another example, South Africa, uses democratic republic systems where the president is elected to serves both as head of state and as head of government. Figure 9 shows the diversity of politics systems in Africa and the world

Figure 9. Politic systems around the world [36].

Why we care about politic systems in deciding the location of datacenter

In recent decades politics systems have collapsed and other begin taking over the rules. Changing politics systems could change different policies, which might affect somehow datacenter operations. It could affect the cost of powering these datacenters or may affect who can use them. Also, it may affect the privacy of these datacenters like what happened in Saudi Arabia 2010. In 2010, Saudi Arabia government forced RIM (Research In Motion), Blackberry service provider, to place their servers within the country, putting them under the jurisdiction of local security force. RIM claims it

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does not provide any private data to the government and their cryptographic keys are strong enough. These claims probably true, but if a server within a country region that’s mean it will be subject for local laws.Another example, what called these days “Arabic Spring” which has expanded to reach other countries in different continent, such as, Venezuela in South America. However, customers won’t host their own sensitive data in a country has unstable systems. These politic systems add number of risk factors have to be weighed when considering the next location for building data center facilities.

2.5 Economic status in the region

According to the Economic Community of West African States (ECOWAS), the economy of West Africa region experiencing growth rate of seven per cent in 2013 compared to 6.6 per cent in 2012 [22]. Also, this rate expected to continue its rapid growth rates to 7.4% in 2014. The economy on the west coast of Africa is driven by oil industry, mineral sector, agriculture and services. In the same time, the economy of southern Africa grows around 4% in 2013 and it continues accelerating to reach 4.6% in 2014. Few countries are still struggling to improve their GDP such as Angola. Meanwhile, South Africa is taking over the southern region and keeps growing but not as much as Nigeria in the west side.On May 7th, 2014, the world proved that Africa’s remarkable growth is projected to stay above 5 per cents in 2014 with West Africa, the fastest growing region in the continent [22]. Nigeria now is taking over South Africa and listed as the biggest economy in the continent. That will make companies look with real interest to the west side of Africa considering the huge information technology market that need to grow side by side with the economy. The following chart shows GDP of Nigeria taking it above South Africa in the ranking of the continent’s economies by size.

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Figure 10. GDP Africa countries [22].

Why we care about economy in data center site location

Growing economy for data center means availability of resources. Data center uses a large amount of electricity to run equipment. That is why selecting location with strong and stable economy reflects the stability of the provided services to the datacenter. Another reason is labor availability. Strong economy means the chances of attracting more labor to the area is bigger. The quality of these labors also might increase as a reflection to the growth of the region. Moreover, any time a company considers a capital investment projects in growing country, the country mostly try to reduce the costs of these projects that have an advantage when it comes to big projects such as building data centers.

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Chapter 3The proposed MethodologyThe methodology of this project is different and has the combination of different factors form different datacenter’s owners around the world. For example, Intel has a range of factors to consider when choosing data center site [24]. Their factors are related to the physical capacity of the data center, such as, WAN infrastructure and availability of water for cooling the facility. Also, they consider other factors related to business operations of data center, such as, costs that are related to workforce, water, power or landscape.HP is another example of data center owner [25]. HP has its own approach when it comes to choosing data center site location. For example, HP considers the size, price and geographical quality of the land as the first subject, the availability of power and communication network in the region. Also, the cost of these operations is matter such as, electricity, water, networking access costs and the amount of the provided power and the chances to increase it. Also, HP considers free cooling is considerable factor that will help to reduce the cost of operations.

Some methodologies have been published about collecting data from different geographical locations. For example, Sanjay M. Parekh, Robert B. Friedman, Neal K. Tibrewala, and Benjamin Lutch have Google patent about using systems and methods for determining collection and using geographic locations of internet users [28].M. Zennaro, E. Canessa, K.R. Sreenivasan , A.A. Rehmatullah, and R.L. Cottrell used Ping methodology and obtained data to quantify the difference in performance between developed and developing countries. They used

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Ping and Trace route as measurement tools targeting educational web services to obtain data [12].However, Data center has to be in a safe location out of any natural disaster or any threats. What happened in Louisiana’s datacenter was a shock for information technology community. In the same way, customers want their data to be in a safe place and out of any privacy obligations as what happening in Saudi Arabia. Also, end user is looking to interact faster with low latency with instance. However, since this project is dealing with continent, which has lack information about technology and communications, this project has to come up with a methodology to choose a new location for data center using the experiences of other datacenter‘s owners.

3.1 Decision criteria of this projectIn this project, we analyzed different factors and looked for initial locations that might fit most of these factors. We basically looked for the following:

- Environmental conditions: Is the selected location’s environment conditions has the minimum requirements for hosting a data center as section 2.3 explained.

- Submarine cables: Does the selected location have access to three or more fiber optical submarine cables as section 2.3 shows.

- Power: Does the selected locations have enough and stable power to satisfy and feed the requirements of data center. Is the cost of power affordable?

- Politically stable: Does the selected location have stable political atmosphere? Section 2.5.

- Network capabilities: Does the selected locations have reasonable network infrastructure? For example, Does it have good average latency?

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- Economy: Does the selected location have a stable economy? Is the economy ready to host capital investment project such as data center? Section 2.6.

All the previously mentioned factors are important in site location process. To find out which one of West Africa countries meets the minimum requirements of these factors, we used the decision process specified in Figure 11 to eliminate undesired locations and narrow down the possible sites. These potential locations are located in the West coast of Africa and they are Senegal, Guinea, Liberia, Ghana, Togo, Benin, Sierra Leone, Nigeria, Cameron, Cote d'ivoire, Congo, Gabon, Angola, the Democratic Republic of the Congo, Namibia and South Africa.

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Figure 11. Decision Process.

As a result for decision process, there were counties that were removed from the original set of good locations for the potential data centers of cloud providers. Table 1 shows the results of decision criteria

Table1. Results of data center decision process.

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Totally meets the requirements.

Partially meet the requirements.

It does not meet the requirements.

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From Table 1, there were countries eliminated because they does not meet the basic requirements of hosting a data center. For example, Angola, Gabon and Cameron have been eliminated immediately form the scope of this project because the politics status of these countries are not recommended to host significant capital projects such as data center.

Also, other countries have been removed from the scope of this project because they did not meet critical condition to host and build data center. To build a data center in any location, the location has to have an average of 3 or more submarine cables to operate normally and prevent begin disconnected for any reason. So if cable disconnected, other cables should operate normally that provide stability for the provided services. In the early 1990s, an accident occurred at the rail yard just north of downtown Sacramento, California. As a result, customers experienced network outage because their network provider has fiber underground and was installed along with the railroad path. The other fiber service provider was in the same trench, as result customers were also impact [24]. That shows the importance of having more than one reliable fiber optical service in the selected location to grantee traffic will continue flow no matter what happen.

The results from Figures 11 and Table 1 are the potential site locations to building data center for cloud provider. The potential locations are; Senegal, Ghana, Nigeria and South Africa.

3.2 Intensive Network measurement for potential location

As a part of this project we have to find one possible location to build datacenter for cloud services in the West coast of Africa. The decision process in Section 3.1 has left the following countries, Senegal, Ghana, Nigeria and South Africa. However, to find out this location, project will

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focus more on both of network capabilities and the cost of space, power, and cooling in these countries.

Tools used for analyzing the networks

1- Znmap: Nmap (Network Mapper) is a security scanner originally written by Gordon Lyon. This tool used to discover hosts and services on a computer network, thus creating a "map" of the network. To accomplish its goal, Nmap sends specially crafted packets to the target host and then analyzes the responses. [29]

2- Visual route: It is a graphical tool that scans networks and provides a graphical view of connection performance. The report that shows packet loss and latency for each network hops.

Amazon Linux AMI 2014.03.1 machines in Singapore, Sydney and Sao Paulo are used to measure the network performance. They all run Linux Fedora 20 OS.

3.2.1 Network measurements The goal of network measurements is to find the performance of the four countries and analyze how they will interact with global communication through fiber optical cables. Every continent has it is own performance while they communicate with the rest of the world. In this project we measured web hosts of Senegal, Ghana, Nigeria and South Africa from Linux machines that are located in Colorado United states at North America, Singapore East Asia, Sydney, and Sao Paulo Brazil. We ran the experiments four times a day for 7 days. We choose these locations to understand packets movements through different submarine cables. Also, these locations will help to get latency, packet loss percentage and round trip time.

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- Latency (delay) is an expression of how much time it takes for a packet of data to get from point to another.

- Round trip time (RTT): is an expression that measure the time that take for single packet to travel from one host to other and come back again to the sender. Also called round trip delay.

- Packet loss: is a good measure of the quality of the link for many TCP based application. It is mainly cause by congestion which in turn cause queues in routers to fill and packet to be dropped. Also it may cause by bit error in the link or in network devices.

All the previous tools are helpful when it comes to measure the performance between to hosts in a network. They help to understand where the problems are in a network and for getting details sense about the internet between hosts.

Two main utilities the project will use to measure network latency, packet loss and round trip time for the mentioned countries – the Ping and traceroute utility

- Traceroute: is a utility that record the route that packet takes through the internet between one host to other. It is also consider as debugging tool that attempt to trace the path of packet through the network.

- Ping: it is a tool to determine if a host is operating normally and its network connection are intact. It places a timestamp in packet which will send through network to practical IP address. The sender will be waiting (listing) for the return packet.

Network measurements from North AmericaThe following command illustrates how to send 10 pings, each 1,500 bytes in size, to IP address in every country listed in the table

IP address is 128.198.49.171

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[aalharth@walrus ~]$ ping -c 10 -s 1500 IP address

[aalharth@walrus ~]$ traceroute IP address

Table 2 shows the result of Ping IP addresses in the countries belowCountry Latency Round trip time Packet loss Number of HopsGhana 197.253.65.36 23 ms 220 ms 10 % 17South Africa 41.74.178.82 27 ms 265 ms 0% 20Senegal 41.83.255.255 30 ms 182 ms 0% 17Nigeria 41.222.211.58 27 ms 182 ms 0% 18

Ghana South Africa Senegal Nigeria 0

50

100

150

200

250

300

23 27 30 27

220

265

182 182

Latecny RTT

Figure 12. RTT range between the 4 countries

Network measurements from East Asia (Singapore) IP address is 54.255.149.162

Table 3 shows the result of Ping IP addresses in the countries belowCountry Latency Round trip

timePacket loss Number of

HopsGhana 197.253.65.36 28 ms 270 ms 0% 12South Africa 41.74.178.82 48 ms 476 ms 0% 20Senegal 41.83.255.255 24 ms 236 ms 0% 13

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[root@ip-172-31-10-140 //] ping -c 10 -s 1500 IP address

[root@ip-172-31-26-86 ec2-user]# traceroute IP address

Nigeria 41.222.211.58 27 ms 274 ms 0% 19


50

100

150

200

250

300

350

400

450

500

2848

24 27

270

476

236274

RTT Latency

Figure 13. RTT range between the 4 countries.

Network measurements from East Asia (Sydney) IP address is 54.206.48.231

Table 4 shows the result of Ping IP addresses in the countries belowCountry Latency Round trip time Packet loss Number of

HopsGhana 197.253.65.36 44 ms 440 ms 0% 17South Africa 41.74.178.82 44 ms 493 ms 0% 25Senegal 41.83.255.255 42 ms 428 ms 0% 17

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[root@ip-172-31-10-140 ec2-user]# ping -c 10 -s 1500 IP address

[root@ip-172-31-10-140 ec2-user]# traceroute IP address

Nigeria 41.222.211.5 42 ms 448 ms 0% 23


100

200

300

400

500

600

44 44 42 42

440

493

428448

Latency RTT


Network measurements from South America (Brazil) IP address is 54.207.65.201

Table 5 shows the result of Ping IP addresses in the countries belowCountry Latency Round trip time Packet loss Number of HopsGhana 197.253.65.36 30 ms 312 ms 0% 14

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[root@ip-172-31-31-251 ~]# ping -c 10 -s 1500 IP address

[root@ip-172-31-31-251 ~]# traceroute IP address

South Africa 41.74.178.82 36 ms 343 ms 0% 16Senegal 41.83.255.255 31 ms 281 ms 0% 14Nigeria 41.222.211.58 32 ms 297 ms 0% 12

Ghana South Africa 4 Senegal Nigeria 0

50

100

150

200

250

300

350

400

30 36 31 32

312

343

281297

Latency RTT


3.3 Observations:Most of the time, Ghana did not interact smoothly with traffic coming from different zones as other country does. Sometime packets round trip time travel form source to the destinations in Ghana mostly higher than other. Using IP address in Ghana show same results. Also Ghana shows average of 10% loss packets multiple times.

Table 6. Packet loss and high round trip time in Ghana

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1. [aalharth@walrus cs701]$ ping -c 10 197.253.65.36

PING 197.253.65.36 (197.253.65.36) 56(84) bytes of data.

64 bytes from 197.253.65.36: icmp_seq=1 ttl=43 time=981 ms









--- 197.253.65.36 ping statistics ---

10 packets transmitted, 9 received, 10% packet loss, time 8997ms

rtt min/avg/max/mdev = 960.481/975.940/1003.880/11.900 ms, pipe 2

This performance did not occur all the time, but comparing to others countries these results were unusual. To understand more about tracking down network problems we used traceroute utility to debug these paths. We found that there are hosts that cause this delay in London before sending these packets to the final destination in Ghana.

Table 7. The results of Ping IP addresses in the chosen countries.

17 if-1-0-0.core4.LDN-London.as6453.net (80.231.76.85) 212.707 ms if-4-2.tcore1.L78-London.as6453.net (80.231.130.33) 203.297 ms if-7-2.tcore1.L78-London.as6453.net (66.198.70.26) 203.457 ms

18 ix-3-2-7.core4.LDN-London.as6453.net (80.231.76.34) 298.447 ms 298.180 ms if-17-2.tcore1.LDN-London.as6453.net (80.231.130.130) 205.665 ms

19 if-1-0-0.core4.LDN-London.as6453.net (80.231.76.85) 198.350 ms * 202.336 ms

20 197.253.65.36 (197.253.65.36) 291.164 ms 288.979 ms ix-3-2-7.core4.LDN-London.as6453.net (80.231.76.34) 298.457 ms

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To understand more which submarine cables the packets are taking from London to Ghana we reviewed submarine cables map and found out that there are two submarine cables that are landing in Ghana from London GLO-1 and WACS. GLO-1 lands in most of West Africa countries including South Africa.

Table 8. The results of Ping IP addresses in the chosen countries17 if-1-0-0.core4.LDN-London.as6453.net (80.231.76.85) 212.707 ms if-4-2.tcore1.L78-London.as6453.net

(80.231.130.33) 203.297 ms if-7-2.tcore1.L78-London.as6453.net (66.198.70.26) 203.457 ms

18 ix-3-2-7.core4.LDN-London.as6453.net (80.231.76.34) 298.447 ms 298.180 ms if-17-2.tcore1.LDN-London.as6453.net (80.231.130.130) 205.665 ms

19 if-1-0-0.core4.LDN-London.as6453.net (80.231.76.85) 198.350 ms * 202.336 ms

20 197.253.65.36 (197.253.65.36) 291.164 ms 288.979 ms ix-3-2-7.core4.LDN-London.as6453.net (80.231.76.34) 298.457 ms

Form the table above, we can see last hop handles the packets to Ghana is core4.LDN-London.as6453.net. We found out that this hope owned by TATA communication which partially owns and operates WACS (West Africa Cable System) [30]. WACS submarine cable lands at 14 countries with design capacity of 5.2 Tbps, but currently runs 500 Gbps which kind of not enough to satisfying the needs of 14 countries.

Assumption: Ghana has showed huge latency and average packet loss many times. From the previously mentioned results the project assumed that this latency is a result of huge traffic that WACS face.

On the other hand, Nigeria & Senegal: These two counties have a good average of round trip rates from different destinations comparing to other countries. The best respond for Nigeria was with packets recorded from Brazil with less number of hops observed. The unique about Nigeria is receiving packets from different zones through different paths not like what happening with Ghana. Most of the time, Ghana receives packets from same hops that might cause the delay.

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3.4 Estimating the Coast of Space, Cooling and Powering Data center With the uptake of such technology, the need for efficient and cheap

data centers is inherent. It is therefore of great importance to conduct a cost analysis before setting up a data center for cloud provider in such a continent. The fundamental costs associated with a data center are the cost of electricity (power), networking infrastructure and cooling power. However, these are not the only cost associated with a complete data center, other cost such as operational maintenance, licenses, depreciation and cost of expertise are also important. HP has technical reports that discuss the best mathematical equations to estimate the real coast of data centers [31]. For the purposes of being discrete, this project will focus only on three different costs: cost of power, cooling and space. These are the elementary cost which are estimable and can be calculated. The general model for model for the cost of a data center can be modeled using the below equation.

cost total=cost power+cost space+costcooling+costoperation E.q 1

3.4.1 NomenclatureA area of the computer room (data center) or full property Cap capitalization rate, ratio of the net operating income (NOI) to the real estate property value. Lower cap rates reflect an appreciating real estate market.J1 capacity utilization factor, i.e. ratio of actual data center power consumption to the maximum design (rated) power consumption K1 burdened power delivery factor, i.e. ratio of amortization and maintenance costs of the power delivery systems to the cost of grid power K2 burdened cooling cost factor, i.e. ratio of amortization and maintenance costs of the cooling equipment to the cost of grid power

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L1 cooling load factor, i.e. ratio of power consumed by cooling equipment to the power consumed by compute, storage and networking hardwareNOI Net Operating Income (in $), calculated as the difference between gross operating Income and actual operating costs (not including amortization and maintenance)P power consumed by the hardware, networking or cooling equipment (Watts)R Number of racks utilized in a data centerU$,grid cost of grid power, in $/KWh or $/W per monthU$,A&M amortization and maintenance (depreciation) costs of either the power delivery or cooling equipment, in $/KWh or $/W per monthThe Cost model is the aggregate of Cost of Space, Power hardware, Cooling and operations. It can be represented as follows:

cost total=cost power+cost space+costcooling+costoperationE.q 1

Estimate the total coast, we will assume that our facility is 30000 sq ft and occupied with 50%

3.4.2 Cost of SpaceThe value of real estate is estimated as the ratio of Net Operating Income (Excess of Gross Operating Income over Operating Expenses) and the Capitalization rate (Like P/E rate). The capitalization rate is a representation of the ratio of current valuation to expected return.The real estate value of commercial property is given by:-

Pricecommercial property=NOI

Cap rate E.q 2

NOI = Net Operating Income

For a data center, however, a distinction is made between the total space, active space, and leased space However with the rate of occupancy of data center space, the formula becomes:

36

Cost space=( NOI

ft2 )(Acenter )(%occupancy )

Cap RateE.q 3

The average rate renting spaces are shown in Table 9 below.

Table 9. Typical leasing rates for different markets

Country Average rent (per sq ft)Lagos, Nigeria $6.48 – 8.00 Accra, Ghana $5.7-10.5Dakar, Senegal $ 5.5 – 9.5Cape Town, South Africa $ 10 - 14

Average rent is

Cost space=( average cost

ft2 ) ( Acenter ) (%occupancy )

Cap Rate E.q 4

Assuming Cap rate as 10% and Operating Income as NIL, Average Rent = NOI

Table 10. Net Operating Income avaege

Country Average rent (/ sq ft)

NOI

Lagos, Nigeria $6.48 – 8.00 (6.48 + 8) / 2 = $7.24

Accra, Ghana $5.7 – 10.5 (5.7+10.5) / 2 = $8.1

Dakar, Senegal $ 5.5 – 9.5 (5.5 + 9.5) / 2 = $7.5

Cape Town, South Africa $ 10 - 14 (10 + 14) / 2 = $12

Table 11. Space cost

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Country Cost Space - (A) Rate /sqftLagos, Nigeria (7.24 x 30000 sqft x 50%) / 0.10 = $

1,086,000(A)/30000= $36.2/ft

Accra, Ghana (8.1x 30000 sqft x 50%) / 0.10 = $ 121,5000

(A)/30000= $40.5/ft

Dakar, Senegal (7.5 x 30000 sqft x 50%) / 0.10 = $ 112,5000

(A)/30000= $37.5/ft

Cape Town, SA (12 x 30000 sqft x 50%) / 0.10 = $ 1,800,000

(A)/30000= $60/ft

Lagos, Nigeria Accra, Ghana Dakar, Senegal Cape Town, SA

$36 $41

$38

$60

Space Cost / sqft

Figure 16. Cost of Space per sq ft.

Form the Table 11 and Figure 16 above; it is evident that the cost of power is highest in Cape Town followed by Accra, Ghana and Dakar, Senegal in that order. If the cost of space was the only parameter in the evaluation of the cost of a data center, then the cheapest option in this case is Lagos, Nigeria. The cost of the a similar size data center cost twice as much in Cape Town, south Africa. Thus in this situation Cape Town in South Africa is the least suitable location for a data center facility.

3.4.3 Cost of Power Delivery

Electrical power is the heart of any data center; systems hosted by the data center require uniquely conditioned power specifications for the m to

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work properly. Beyond the power supplied by the national grid, data centers are equipped with online generators and battery back-ups. A data center cannot afford to go offline and hence a crucial design consideration is the redundancy of power transmission. To model the cost of power, elements such as maintenance cost and depreciation of infrastructure responsible for power transmission and back should be factored in.

The cost of power is therefore modeled as,

Cost power=U $ . grid PConsumed+K1U $ . grid PConsumed Eqn. 5

whereU $ . grid−cost of power formthe grid

PConsumed− power consumed

In addition to the Electricity cost of power from grid, is summed up with the increased cost of depreciation and amortization to call Cost of Power Delivery as Burdened cost of power delivery. K1 is the measure of the increased effective cost of power delivery due to amortization and maintenance costs associated with the power delivery system.

K 1= J 1 U $ , A∧M PowerU $ , grid ! E.q 6

The J1 is the capacity utilization factor is calculated as:

J 1= Installed(Max )Capacity(Watts)Utilized Capacity (Watts)

= PratedP consumed hardware E.q 7

K1 is the ratio of the realized (weighed) amortization and maintenance costs per Watt of power delivered to the direct cost of electricity (per Watt) drawn from the grid. This can be calculated on a monthly basis. J1 weighs the amortization costs by measuring the level of utilization of the conditioned space in the data center.

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Combining A, B & C, the formula becomes:

Cost power=U $ , grid P consumed hardware+U $ , A∧M Power P rated E.q 7

Table 12. Electricity Rates.Country Power cost KWHLagos, Nigeria $0.20Accra, Ghana $0.31Dakar, Senegal $0.42Cape Town, South Africa $0.48

Holding all factors constant, the cost of power is highest in Accra, Ghana followed by Dakar, Senegal, then Lagos, Nigeria and cheapest in Cape Town, South Africa. In terms the cost of power, the most conducive city is Cape Town in South Africa while the most expensive is Accra in Ghana. Lagos, Nigeria is not also expensive. Considering this Africa, factor such as frequency of power outages and power surges that may lead to equipment failure should also be factored in. Power surge inflate infrastructure cost due to the need of voltage protectors on virtually all equipment.

Assuming 10 MW data centre and U $ , A∧M , power U $ , gridU $ , A∧M , power=U $, grid

Table 13. Cost of Power delivery for 10 MWCountry Power cost

KWHWorking - U$,A&M, power = U$,grid =

Lagos, Nigeria

$0.20 ($0.20 x 24 hr x 30 days) / 103 = 0.144 / watt / month

Accra, Ghana $0.31 ($0. 31 x 24 hr x 30 days) / 103 = 0.223 / watt / monthDakar, Senegal


Cape Town, SA


J 1= P ratedP consumed hardware

J 1=105

=2

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K 1=J 1U $ , A∧M Power /U $ ,grid=2

Cost power=U $ . grid PConsumed+K 1U $ . grid PConsumed E.q 8

Table 14. Cost of power per Unit sq ftCountry U$,grid Cost Power - (A) Rate /sqft

Lagos, Nigeria 0.144 / watt / month

[0.144 x 5 + 2 x 0.144 x 5] x 1000 = $ 2160 / mth

(A)/30000= $0.072/ft

Accra, Ghana 0.223 / watt / month

[0.223 x 5 + 2 x 0.223 x 5] x 1000 = $ 3345 / mth

(A)/30000= $0.115/ft

Dakar, Senegal 0.302 / watt / month

[0.302 x 5 + 2 x 0.302 x 5] x 1000 = $ 4530 / mth

(A)/30000= $0.151/ft

Cape Town, SA

0.345 / watt / month

[0.345 x 5 + 2 x 0.345 x 5] x 1000 = $ 5175 / mth

(A)/30000= $0.172/ft


$0.07

$0.12

$0.15

$0.17

Cost of Power /sqft

Figure 17. Cost of Power per sq ft.

3.4.4 Cost of Cooling ResourcesAny electrical equipment that takes in power produces heat energy.

Heat energy creates a hostile environment for the operation of most

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electrical components. A date center has electrical infrastructure ranging

from powerful processor, routers and circuit boards. All these require a low

optimum temperature for optimal performance. Cooling these resources is

the responsibility of the cooling system. There are several types of cooling

system such as air cooled systems and water cooling system. They types of

cooling system are not the subject of this paper and therefore their

discussion is limited to that already mentioned. So that the cost of cooling

can be given as:

Cost power=U $ . grid PCooling+K2U $ . grid PCooling E.q 9

Pc ooling=L1 PConsumed Hardware E.q 10

The load on the cooling equipment is directly proportional to the power consumed by the computer hardware. Load factor is L1

Cost power=¿ E.q 11K2 is a representation of the amortization and maintenance costs associated with the cooling equipment per unit cost of power consumed by the cooling equipment.

Assumption

Based on study of cooling equipment purchase and preventive maintenance costs for a10-MW data center with typical redundancies, amortization and maintenance costs for the cooling infrastructure are approximately one-half the costs incurred for the power delivery [31].

U $ , A∧M , power=0.5 U $, A∧M , power

Table 15. Cost of cooling delivery

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Country Power cost KWH Working - U$,A&M, cooling = 0.5 U$, A&M Power = Lagos, Nigeria $0.18 ($0.18 x 24 hr x 30 days x 0.5) / 103 = 0.065 / watt / monthAccra, Ghana $0.31 ($0. 31x 24 hr x 30 days x 0.5) / 103 = 0.116 / watt / monthDakar, Senegal $0.42 ($0.42 x 24 hr x 30 days x 0.5) / 103 = 0.151 / watt / monthCape Town, SA $0.48 ($0.48 x 24 hr x 30 days x 0.5) / 103 = 0.1728 / watt / month

J 1= PratedP consumed hardware E.q 12

P Consumed hardware = (Given) 50% ie 5 kwhJ1 = 10 / 5 = 2 K2 = J1 U$,A&M cooling / U$,grid

= 2 x 0.5 / 1 = 1

Further, typical state-of-the art data center has a cooling load factor of 0.8, i.e. 0.8 W of power is consumed by the cooling equipment for 1 W of heat dissipation in the data center.

CostCooling=¿ E.q 13

Table 16. Cost of cooling per sq ftCountry U$,A&M, cooling Cost Cooling - (A) Rate /sqft

Lagos, Nigeria 0.065 / watt / month (1 + 1) (0.8 x 0.065 x 5) x 103 = $ 520

(A)/30000= $0.017/ft

Accra, Ghana 0.116 / watt / month (1 + 1) (0.8 x 0.116 x 5) x 103 = $ 928

(A)/30000= $0.030/ft

Dakar, Senegal 0.151 / watt / month (1 + 1) (0.8 x 0.151 x 5) x 103 = $ 1,208

(A)/30000= $0.040/ft

Cape Town, SA

0.178 / watt / month (1 + 1) (0.8 x 0.178 x 5) x 103 = $ 600

(A)/30000= $0.047 /ft

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$0.02

$0.03

$0.04

$0.05

Cooling Cost / sqft

Figure 18. Cost of Cooling per sq ft

The cost of cooling is highest in Cape Town followed by Dakar,

Senegal, then Dakar, Senegal and cheapest in Lagos, Nigeria.

3.4.5 Overall Model for Data Center Cost of Space, Power and Cooling

Cost SpacePower

Cooling

=[( $ft2 )( Adata center)(%Occupancy)]+[(1+K1+L1+ K2 L1)U $ .grid Pconsumed

hardware ] E.q 14

The cost of real estate is represented by the first term while the second term combines both the

cost of power for hardware and cooling resources. The equation can further be simplified by

introduction of critical space in place data center space and occupancy,

Cost SpacePower

Cooling

=[( $ft2 )( Acritical ' ft )]+[(1+K1+L1+K2 L1)U $ . grid Pconsumed

hardware ] E.q 15

Table 17. Overall Model cost of power, cooling and spaceCountry Cost Space – A Cost Power - B Cost Cooling – C Total Cost =

A+B+C

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Lagos, Nigeria $36.2/ft. $0.065/ft. $0.017/ft. $36.28/ft.Accra, Ghana $41/ft $0.116/ft $0.030/ft $41.146/ftDakar, Senegal $37.25/ft. $0.151/ft. $0.040/ft. $37.441/ft.Cape Town, SA $60/ft $0.172ft $0.047/ft $60.219/ft


$36.44 $41.15

$37.44

$60.22

Total Cost /sq ft

Figure 19. Total Cost per sq ft

Among the four cities, from estimation of cost of space, power and cooling,

Lagos emerges as the cheapest. It has a moderate real estate cost and

almost the least cost power supported by oil industry. Cape Town considers

most expensive in terms of Total cost. Dakar is somewhere in between and

might consider as second options to host datacenter for cloud provider.

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Chapter 4 Selection ResultsAfter studying and analyzing the pervious section, we compared decision process, network performance and the cost of power, space and cooling results to find the best location to host Data center for cloud provider.

4.1Comparing network performance

In this section, we compared the network results between West African

countries to see which country is better interacting with packets sent from

different continent around the globe even with ignoring bandwidth at this

moment. Analyzing the time that take packets requested and delivered.

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a. Round Trip Time & Latency from North America [ms]


50

100

150

200

250

300

23 27 30 27

220

265

182 182

Latecny RTT


As chart 12 proves that Nigeria has an average of 182 ms round trip time as

much as what Senegal has when packets sent from North America1, but

Nigeria has better latency time than Senegal. In same contract, Ghana and

South Africa have higher round trip time, but in latency South Africa has

higher number with 27 ms than Ghana.

b. Round trip time from Singapore [ms]

1 Packet sent from Denver, Colorado USA

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50100150200250300350400450500

28 4824 27

270

476

236274

RTT Latency


In Figure 13 we can clearly see that Senegal has the best latency time and round trip time among all the countries. Then Ghana comes in the second rank with good average RTT. On the other hand latency in Nigeria comes in second rank with third in RTT. South Africa performed worse than expected. South Africa has higher number of RTT and latency when packets received from Singapore.

c. Round trip time from Sydney


100

200

300

400

500

600

44 44 42 42

440493

428 448

Latency RTT

Figure14. RTT range between the 4 countries

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Considering the geographical distention between Sydney Africa, these numbers are reasonable. Figure13 shows higher RTT in South Africa, Nigeria, Ghana then Senegal. In term of latency, South Africa and Ghana has same time. Nigeria and Senegal also has same average latency time.

d. Round trip time from Sao Paulo, Brazil

Ghana South Africa 4 Senegal Nigeria 0

50

100

150

200

250

300

350

400

30 36 31 32

312343

281297

Latency RTT

Chart 15 RTT range between the 4 countries

Still South Africa and Ghana performed worse even when we consider geographical destination. Of course it is routing policy, but this number expected to be lower. Looking at the round trip times in previously mention charts, we will rank each country’s performance starting from 1 to4. Number 1 is best performance with lower RTT. The following table will demonstrate these results by rank performance.

Table 19. Ranks countries based on RTT ranges

Country Nigeria Senegal South Africa

Ghana

North America

1 1 4 3

Singapore 2 1 4 3Sydney 2 1 4 3Brazil 2 1 4 3

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We can clearly see that Senegal in the first rank with lower RTT than other countries. Nigeria in second rank, Ghana third and finally South Africa.

Table 20 Files downloaded from different hosts

IP address Country File Type Size Time Bandwidth

197.255.124.0 Ghana PDF 2.4 M 14 s 170 KB/s

196.45.48.50 Nigeria PDF 6.5 M 97 s 70.1 KB/s

41.86.110.240 South Africa PDF 6.0 M 26 s 241 KB/s

196.207.216.71 Senegal PDF 1.3 M 4 Ms 3. 12 KB/s

To understand if these results reflect the connectivity in each country, we build Table 21 to show every country and its bandwidth according to the last statistics from net index [37].

Table 21. Internet speed in the targeted countries.

Rank in the world Country Speed

109 Senegal 6.14 Mbps

112 Ghana 5.95 Mbps

128 South Africa 5.19 Mbps

134 Nigeria 4.76 Mbps

From Table 20 we can see why Senegal performed better than other countries. In same time, we can see that Nigeria came in the second rank in Table 20 even it has lower bandwidth comparing to other countries. So, to understand more why Nigeria performed better even with low bandwidth, we reviewed trace route utility and analyzed the results and see how packets move from hop to other.

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Table 22. Nodes deliver packets to Nigeria.

Form USA & Brazil

11 209.130.172.38 (209.130.172.38) 234.611 ms 234.558 ms 234.548 ms

From Sydney & Singapore

18 ix-11-0-0.core1.SZ5-Seixal.as6453.net (195.219.129.10) 430.550 ms 432.227 ms 428.877 ms

Assumption; we found that from any zone, there are different paths packet take to reach Lagos. Ghana has different case; most of the time receives packet always from same hop as section 3.3 explained. That may be the reason for good respond time that Nigeria recorded.

4.2 Comparing Space, Power and Cooling costs


$36.44 $41.15

$37.44

$60.22

Total Cost /sq ft

Analyzing Total cost from chapter 3 we can clearly see that Nigeria is the best choice to host data center facility for a cloud provider. This country

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boosts by oil production which affects electricity prices and make the market attractive for investments. Also, oil production will guarantee stability of electricity tariffs per Kwh for a long period.

After these comparisons, we are recommending Nigeria to be our future location to host data center for cloud provider. The following reason support our recommendation

4.3Politic system & economy affects cloud

As Africa’s most populous country, oil-rich Nigeria ranks with South Africa as one of the most powerful countries on the region. Since its restoration to democracy in 1999, Nigeria has made progress on strengthening government institutions and fighting the corruption engendered by its oil wealth.Like the United States, Brazil, and India, Nigeria is structured as a federation, a structure it inherited from its British colonial rulers. It has thirty-six states, one federal territory, and 774 municipalities (plus Abuja). Power resides in the central government, which controls most of the country’s revenues and resources [34].In April 6, 2014, the economy of Nigeria has overtaken South Africa’s largest economy after gross domestic product data shows for the first time in two decades. The economy of Nigeria increased more than three-quarters to $488 billion. The number of industries increased to 46% from 33% with greater weight to sectors such as telecommunications and financial services [32]In contrast, South Africa is taking over cloud computing services in the continent, but it is about to be taken dramatically by Nigeria, a new report by Cisco and World Wide Worx has predicted [33]. According to the report, Cloud computing is booming in Africa’ major economies, such as Nigeria and South Africa.According to the report, 50 percent of South Africa medium and large

52

businesses are using cloud services; the other 48 percent are using cloud services in Kenya. Nigeria, on the other hand, 36 percent of businesses are using cloud. 44 percent of Nigerian businesses claim that, by the end of 2014, they will increase the percentage to 80 percent. The key rapid in cloud computing of Nigeria related to the growing confident that IT decision makers have in the country.

4.4Number of users

According to PC tech, Nigeria has a population of 168 million according to 2012 statistic. Nigerians are totally busy using the internet mostly on their phones. There are more than 48.4 million users in Nigeria alone which is more than the number of users in Egypt and Morocco combined [35]. Figure 20 shows the massive numbers of users in Nigeria compare to others.

Figure 20. Number of users in Nigeria compare to other African

countries

4.5Submarine cables

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Nigeria has five submarine cables connected to Lagos on coast line. Next tables show these cables with their current capacity

Table 23. Submarine cables land on Nigeria.

Marine cable

length Landing points

Design 2Capacity

Lit capacity 3

First use

Main One 14,000 Km 3 1280 Gbit/s N/A July 2010GLO-1 9,800 Km 8 2.5 Tbit/s 640 Gbit/s April 2011WACS 14,916 Km 14 5.12 Tbit/s 500 Gbit/s May 2011ACE 17,000 Km 21 5.12 Tbit/s N/aA December

2012SAT-3/WASC

14,350 17 340/440 Gbit/s

N/A 2007

Chapter 5Conclusion In this research, we investigate the methodology and decision process for selecting data centers for cloud providers. We collect the related data for the decision process and analyze the choices of countries for the ideal data center locations. The results show Senegal, Nigeria, South Africa and Ghana are better locations than other and Nigeria is the best candidate. In general Africa has a good opportunity to host datacenter for a cloud provider. They have basic elements to be part of the global communication with high standers like what Asia has.

2 * Design capacity – the maximum traffic-carrying capability of the system if it were fully equipped using today’s technology.

3 * Lit capacity – the actual traffic-carrying capability of the system today, based on what has been equipped to date.

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http://en.wikipedia.org/wiki/SAT-3/WASC_(cable_system)

http://en.wikipedia.org/wiki/ACE_(cable_system)

http://en.wikipedia.org/wiki/WACS_(cable_system)

http://en.wikipedia.org/wiki/GLO-1_(cable_system)

http://en.wikipedia.org/wiki/Gigabit

http://en.wikipedia.org/wiki/Main_One_(cable_system)

Finally, Ping and Trace route provide a valuable active end-to-end performance for scanning and monitoring networks in the world. They give extensive quantitative and real-time information about the current situation of the networks around the world. Ping and Trace route data can provide information about networks that need to improvements, such as Ghana.

Chapter 6Lesson learned This chapter discusses the lessons learned during this project and explains the challenges I encountered and the solutions explored. There are huge numbers documents and topics related to Data costs, cloud providers and network measurements. They took a lot of time to understand, and analyze. Dealing with continent as Africa with variety of languages and lack of data was not the easy job as I expected before starting my project. Collecting and analyzing data and numbers from different resources was hard task

55

that took a lot of time and effort to organized them and put them logical way. Networking data changes in every second, monitoring and average these data was a hard job.

5.1 Challenges

a. Reading about the topic

Reading and researching books, papers, articles and documents about Africa’s networks is painful work because of the lack the continent has. I tried to understand the basic needs of information technology to provide a report that in reality could help and provide real value. Exploring network providers, network capabilities, number of users, bandwidth, etc., in every targeted country was complicated job. Reading and researching topics that are out of my scope such as politics systems, business, natural disasters, population and other unrelated topics to information technology, but it plays critical rules in the main goal of this project.

b. Languages

There is variety of languages in Africa, Portuguese, French, Spanish, Afrikaans and other fourteen local languages. English considers as a second language in most of the continent. That was a big challenge because most of their websites and resources have their own native language. That created an obstacle to read and gain data from local or official websites. Even with translating these pages still there are miss translating the continent as whole.

c. Gaining data

Collecting precise data such as, cost per Kwh was difficulties. Even with official website states tariffs per Kwh prices there are local blogs which are

56

claiming the total opposite. Another example, I had to talk and speak to real estate agents in different countries to estimate the real value of renting lands to host data center facility land with all the requirements needed such as power grid and water etc,. Some time I had to pretend begin investor just to make these agents wants to give more data.

d. Conflict data

There are so much of conflict data in this continent. Easily you can find four to five official resources taking about same topic but using different values. That includes universities, agencies and government’s websites. Also, there are conflict data between official sources and other national sources, for example, between utility prices is different in Ghana’s main utilities company, united nation and Wikipedia. Finding the most accurate data was another type of challenge.

5.2Technical challenges

a. Understanding cables systems

Totally was new topic for me. Submarine cables system is huge industry with different famous IT provider competitions. Different submarine cables land in different zones, each has different capacity with different usage. Some of the privates, others are commercial, etc.

b. Track packets

It was challenge to keep track on packet sent from different zones through submarine cables. I learned how to determine which path my packets take and which submarine cables it use. I learned how to determine the causes of delay and which address is responsible of delay. That actually fascinating.

c. Network tools

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It was time consuming to find the appropriate tools that satisfy my research. Different tools are available but not all of tools have the needed capabilities. Measuring latency from different zones required different remote machines. I had to run nine machines between Linux and windows servers in different zones send and receive packets.

In the end, I’m thankful for this opportunity to learn and help the knowledge about continent that indeed in need

d. Web servers

At the beginning of the project, I surfed the internet looking for web server providers in the targeted countries. I did communicate with customer representative and they confirm the service. However, when I started the project, I talked with technical support in those providers. Unfortunately, there were no physical web servers in West Africa countries. I talked with all providers who claim having web servers in the region, but no one has actual data center there. They advertise lies to attract customers. However, that made the project even harder because most of given data will be based on assumptions and research. Gathering data from different resources was time consuming.

e. Locating IP address

Finding IP address in Africa was trickiest job in this project. There were few organizations that have their physical servers within the region. Only few governments and university are hosting their websites locally. I can confidently say that, from every 10 website, I browsed I found one hosted locally. South Africa was on average because their modern infrastructure helps the businesses to provide web services within the country. Indeed, the region is looking for such data center to host their data locally, safer and cheaper.

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6. Bibliography

[1] Amazon Web Services. (2014). Global Infrastructure. Retrieved from http://aws.amazon.com/about-aws/globalinfrastructure/ [2] Rath, John. DATA CENTER SITE SELECTION. Tech. N.p.: n.p., n.d. Print. [3] "BlackSwan Zine." Data Center: 10 Places You Don’t Want to Build. Blackswanzine, 2010. Web. 2 Jan. 2014. [4] Chang, Shin-Jyh Frank, Susmit Harihar Patel, and James Marc Withers. "An optimization model to determine data center locations for the army enterprise."Military Communications Conference, 2007. MILCOM 2007. IEEE. IEEE, 2007.

[5] Hugos, M. H., & Hulitzky, D. (2011). Business in the cloud: What every business needs to know about cloud computing. New York: Wiley.

[6] Lanier, J. (2013). Who owns the future?. London: Allen Lane.

[7] Wang, L. (2012). Cloud computing: Methodology, systems, and applications. Boca Raton, FL: CRC Press.

[8] Agrawal, G. P. (2010). Fiber-optic communication systems (Vol. 222). John Wiley & Sons.

[9] "How to Meet a Growing Need for More Data Center Capacity."Http://www.ibm.com/us/en/. IBM, Sept. 2012. Web. Oct. 2013.

[10] Nozick, Linda K., and Mark A. Turnquist. "A two-echelon inventory allocation and distribution center location analysis." Transportation Research Part E: Logistics and Transportation Review 37.6 (2001): 425-441.

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[11] Ebrahimi, Khosrow, Gerard F. Jones, and Amy S. Fleischer. "A review of data center cooling technology, operating conditions and the corresponding low-grade waste heat recovery opportunities." Renewable and Sustainable Energy Reviews 31 (2014): 622-638.

[12] Zennaro, M., et al. "Scientific Measure of Africa's Connectivity." Information Technologies & International Development 3.1 (2006).

[13] Nozick, Linda K., and Mark A. Turnquist. "A two-echelon inventory allocation and distribution center location analysis." Transportation Research Part E: Logistics and Transportation Review 37.6 (2001): 425-441.

[14] Cloud Computing in Africa Situation and Perspectives. Tech. ITU, 20 Apr. 2012. Web. Feb.-Mar. 2014.

[15] Data Center Map." - Colocation, Cloud, Managed Hosting Etc. N.p., n.d. Web. Mar. 2014. <http://www.datacentermap.com/>.

[16] Submarine Telecoms INDUSTRY REPORT. Rep. Terabit Consulting, 2013. Web.

[17] African Studies Center, University of Pennsylvania. African Studies Center, University of Pennsylvania. Web. 15 Feb. 2014.

[18] Pappa, Farrah. "The Do's and Dont's of Data Center Planning." The Do's and Dont's of Data Center Planning. Web. 15 Feb. 2014.

[19] Map of Earthquakes Today." Map of Earthquakes Today. N.p., n.d. Web. 15 Mar. 2014.

[20] Earthquakes .Web. Feb. 2014. <Earthquake Hazards Program>.

[21] P&S. "Tsunami Strikes African Coast." Tsunami Strikes African Coast. N.p., n.d. Web. 15 Mar. 2014.

[22] "Nigeria Economy Set to Leapfrog South Africa on Data Revamp."Bloomberg.com. Bloomberg, 7 May 2014. Web. 11 Apr. 2014.

[23] Nigeria. Official Website. Web. Feb. 2014. <http://www.nigeria.gov.ng/index.php>.

[24] "Selecting A Data Center Site: Intel's Approach." N.p., Feb. 2014. Web. Feb. 2014. <http://connectedsocialmedia.com/intel/7426/it-best-practices-data-center-site-selection/>

[25] Selecting a Location for an HPC Data Centre. Tech. N.p.: n.p., 2013. Web. <http://webcache.googleusercontent.com/search?q=cache:http://www.prace-project.eu/IMG/pdf/hpc-centre-location-whitepaper-2.pdf>.

[26] "Africa’s International Bandwidth Growth to Lead the World." Africa's International Bandwidth Growth to Lead the World. N.p., n.d. Web. 29 Mar. 2014.

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[27] Hurricanes VS Tornadoes. Diffen, n.d. Web. Mar. 2014.

[28] Parekh, Sanjay M., et al. "Systems and methods for determining collecting and using geographic locations of Internet users." U.S. Patent No. 6,757,740. 29 Jun. 2004.

[29] Nmap Free Security Scannar. Web. Jan. 2014. <http://nmap.org/>.

[30] TATA, n.d. Web. Apr. 2014. <http://lg.as6453.net/bin/lg.cgi>.

[31] Patel, Chandrakant D., and Amip J. Shah. "Cost model for planning, development and operation of a data center." (2005).

[32] "Nigeria Becomes Africa's Biggest Economy." BBC. N.p., 06 Apr. 2014. Web. 06 Apr. 2014. <www.bbc.com%2Fnews%2Fbusiness-26913497%E2%80%8E>.

[33] "Cloud Uptake Set to Explode Across Africa." Cisco. Web. 26 Mar. 2014. <http://www.cisco.com/web/ZA/press/2013/112813.html>.

[34] Hanson, Stephanie. "Nigeria’s Creaky Political System." Council on Foreign Relations, Apr. 2007. Web. <http://www.cfr.org/nigeria/nigerias-creaky-political-system/p13079>.

[35] "Top 10 Internet Countries in Africa." PC Tech Magazine. N.p., Feb. 2014. Web. Apr. 2014. <http://pctechmag.com/2014/02/top-10-internet-countries-in-africa/>.

[36] "Political System." Wikipedia. Web. Mar. 2014. <http://en.wikipedia.org/wiki/Political_system>.

[37] "GLOBAL BROADBAND." Netindex. N.p., n.d. Web. Feb. 2014. <www.netindex.com>.

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