(Luận văn thạc sĩ) multicriteria analysis for hyperscale data centers placement in vietnam

INTRODUCTION

Research background

Since the early 21st century, the IT industry has emerged as a major global economic driver, with over seven billion devices connected to the internet, making data a valuable commodity that requires effective management Businesses are increasingly outsourcing data management instead of relying on internal server systems According to Artizon's report, the global data center market was valued at $215 billion in 2021 and is projected to grow to $288.3 billion by 2027, with 400 new facilities established that year alone Significant growth is observed in countries like the US, China, Japan, Australia, the UK, Germany, India, Saudi Arabia, South Africa, and various Southeast Asian nations, driven by incentives such as tax exemptions and favorable energy policies.

Hyperscale data centers are data centers that are significantly larger than enterprise data centers with over 5,000 servers, and 10,000 square feet (Vertiv, 2021)

The rise of hyperscale data centers from major tech companies like Google, Facebook, AWS, Alibaba, and Microsoft has brought significant challenges, particularly concerning electricity consumption Data centers, which require near-constant operation, must adhere to strict uptime standards set by the Uptime Institute, with Tier 1 facilities needing 99.67% uptime and Tier 4 facilities demanding 99.99% uptime annually Currently, data centers account for 1% of global electricity usage, with a 2018 EU Commission study revealing that they consumed 2.7% of the EU's total electricity, projected to increase to 3.2% by 2030 Notably, Singapore's data centers utilize 7% of the nation's electricity.

In 2020, a ban on new data centers was lifted, paving the way for construction to resume By 2022, new regulations were introduced to enhance energy efficiency for newly opened data centers, reflecting a commitment to sustainable practices in the industry (Mah, 2022).

To address the challenges of sustainability, various global initiatives are promoting greener data centers, including the Science Based Targets initiative (SBTi), the Climate Neutral Data Center Pact, the Long Duration Energy Storage (LDES) Council, and RE100 The green data center market, valued at $35.58 billion in 2021, is projected to grow to $55.18 billion by 2027, as reported by Arizton’s “Green Data Center Market - Global Outlook & Forecast 2022-2027.”

Vietnam, classified as a Lower Middle-Income Country, aims to achieve developed status by 2045 The Vietnamese Government is prioritizing the growth of fintech, AI, e-commerce, software outsourcing, and education technology With a strong emphasis on 4.0 technology and digitalization, trends such as remote work, electronic administration, and social media are emerging Consequently, data centers are poised to become a critical industry, serving as the backbone of the Internet.

As of 2021, Vietnam's data center industry remains relatively small, primarily led by four major companies: FPT, VNTP, Viettel, and CMC In the past three years, there has been a notable emergence of smaller-scale cloud-based service operations Surprisingly, the Covid-19 pandemic not only failed to impede digitization efforts but also accelerated the urgent need for digital transformation in the country.

The data center market in Vietnam remains relatively small, with most facilities situated in major urban centers like Ho Chi Minh City, Hanoi, Danang, Binh Duong, and Can Tho In contrast, global trends indicate a shift towards relocating data centers to rural areas, particularly in the US, where one-third of such facilities are found outside city limits (Isberto, 2021) This movement is driven by lower land prices, reduced energy costs, and the affordability of constructing one or two-story buildings, allowing these data centers to leverage economies of scale for competitive advantage Additionally, environmental concerns within Vietnam's data center industry are minimal, with a lack of clear government regulations or corporate initiatives aimed at promoting greener practices in the market.

Research problem formulation

The location problem for hyperscale data centers is primarily a cost minimization challenge, as estimating potential revenue based on location characteristics is complex Unlike traditional facilities, hyperscale data centers operate online, competing on price, services, and marketing rather than physical location They serve as both data storage and processing plants for cloud computing, necessitating 24/7 availability to accommodate users across various time zones This unique operational requirement raises several critical questions regarding optimal site selection and cost efficiency.

 What are the key criteria affecting the selection of hyperscale data centers’ placement in Vietnam?

 How are these criteria ranks based on their importance to the selection decision?

Research objectives

This research aims to identify key criteria for selecting data center locations in Vietnam and develop a Multi-Criteria Decision-Making (MCDM) model through literature review and expert interviews By employing fuzzy Analytic Hierarchy Process (AHP) to analyze expert opinions, the study calculates weight scores for each criterion It further distinguishes the characteristics of data center placement by comparing expert models with non-expert models To illustrate the application of this selection process for managers and other stakeholders, a simulation is conducted.

Research scope

This study, conducted in Vietnam from October 2021 to May 2022, explores the decision-making process for selecting hyperscale data center locations, drawing insights from experts in the Vietnamese data center industry.

Research paper structure

The chapter introduces overview of the research background of the data center industry in Vietnam and in the world, research problem formulation, research objectives, research scope and research structure

This chapter contains the review of existing research in decision making, multicriteria analysis, and facilities location selection problem

This chapter discusses the research design, the process of data collection and the AHP methodology and fuzzy set methodology used

 Chapter 4: Data analysis and discussion

This chapter presents the research findings, highlighting their theoretical and practical implications for decision-making literature and the data center industry in Vietnam.

This chapter concludes the research paper and provide implication for stakeholders in the data center industry in Vietnam as well as research contribution and limitation h

LITERATURE REVIEW

Facilities location problem

Site selection is a critical challenge for businesses, influenced by location theory which examines the economic decisions of buyers and sellers (Gorter & Nijkamp, 2001) The optimal location theory identifies three key strategies: cost minimization, revenue maximization, and net benefit maximization Introduced by Weber in 1909, the Theory of the Location of Industry emphasizes minimizing transportation costs through a locational triangle Trade and location are interconnected, as highlighted by Krugman (1991) in modern economic growth theory Choosing a facility's location is a strategic decision with lasting implications for operating costs, delivery speed, and competitiveness, particularly in Just-In-Time and flexible distribution systems This decision impacts various business areas, including finance, marketing, human resources, and production (Yang & Lee, 1997) Additionally, location factors vary based on industry characteristics and product life cycles, with labor-intensive sectors like fast fashion prioritizing labor costs, while high-tech industries focus on quality of life to attract skilled labor.

Numerous studies have explored the site selection problem through various methodologies, employing mathematical models and scientific approaches in operations research Kochetov (2013) identified four fundamental discrete facility location models: the uncapacitated facility location problem, which aims to minimize transportation costs for users; the multi-stage facility location model, focused on optimizing the costs of opening facilities and transporting goods; and the facility location problem that incorporates user preferences to enhance decision-making.

In decision-making processes involving multiple stakeholders, choices are not solely focused on minimizing production and transportation costs; other factors, such as travel time, also play a significant role Additionally, competitive location strategies are crucial, particularly in scenarios where one firm acts as a first mover while another follows, with the primary objective being profit maximization for the first mover.

Data center location selection

The TIA Standard serves as an international guideline for data center infrastructure, aiding managers in making informed decisions about ICT infrastructure Developed by the Telecommunications Industry Association (TIA), the TIA 942 Standard is specifically tailored for data centers and adheres to ISO/IEC 17020/17021 While it does not impose strict location requirements, Annex F offers valuable insights on optimal site selection for data centers.

Table 2.1 Summary of site selection considerations from TIA 942 Standard Annex F

General  Follow national, state, and local codes

 Follow local, state, and federal accessibility guidelines and standards

 Follow seismic standards of the International Building Code Seismic Zone

 Free of asbestos, lead-containing paint, PCB’s, and other environmental hazards

 Below 3050m elevation for proper cooling Architectural site selection considerations

 Be one or two story buildings dedicated to data center

 If not dedicated to data center, other tenants need to be non-industrial

 If located on upper floor of multi-tenants building, need to have adequate infrastructure as required Electrical site selection considerations

 Local utility needs to be able to supply current and future needs for power

 If not, site needs to support self-generation, cogeneration or distributed generation equipment Mechanical site selection considerations

 Multi-tenant building needs to have air conditioning heat rejection equipment

 Need to have a pre-action sprinkler system dedicated to the data center h

 At least two diversely routed optical fiber entrance rooms with different local access provider offices

 Have dedicated access provider equipment located in the data center space in multi-tenants building

 Access 24 hrs/day, 7 days/week

When selecting a site for development, it is crucial to avoid locations that pose significant risks or hazards Specifically, steer clear of areas directly above parking garages, within a 100-year flood plain, near earthquake faults, on hillsides prone to landslides, and downstream from dams or rivers Additionally, sites should not be located within flight paths of airports, within 0.8 km of railroads or major interstate highways, or within 0.4 km of airports, research labs, chemical plants, landfills, rivers, coastlines, or dams It is also advisable to maintain a distance of at least 0.8 km from military bases and 1.6 km from nuclear, munitions, or defense plants, as well as to avoid adjacency to foreign embassies and high crime areas.

 proximity of police stations, fire stations, and hospitals

 alternate uses of the building after it is no longer needed as a data center

Multicriteria decision making

Decision making is a fundamental aspect of human life, attracting research interest for centuries The challenges of making decisions with multiple stakeholders and differing opinions have been tackled using methods like the Borda count and the Condorcet principle since the 1700s, further developed by Kenneth Arrow's social choice theory in 1951 To streamline this complex process, it's essential to analyze decision alternatives and the criteria for their evaluation, which falls under the field of Multi-Criteria Decision Making (MCDM) within operations research.

The Analytic Hierarchy Process (AHP), introduced by Saaty in 1980, is an effective multi-objective pair-wise comparison method It is commonly utilized to compute weights for various criteria in performance-related problems, facilitating informed decision-making.

8 resource management, corporate policy and strategy, public policy, political strategy, and planning The steps of AHP with a decision maker committee:

(1) Form a committee of decision maker

(4) Collect data and rank each potential location

FAHP, which integrates fuzzy set theory with the AHP method, effectively analyzes expert opinions while addressing uncertainty and the challenge of assigning precise numerical evaluations Initially introduced by van Laarhoven and Pedrycs in 1983, this method utilized logarithmic least squares to derive priority vectors, but it became time-consuming with an increase in criteria and sub-criteria To streamline the process, Chang proposed an extent analysis method in 1996 that offered simpler calculations and reduced time requirements This technique was further enhanced by Hue et al in 2022, as discussed in Chapter 3.

Multicriteria decision making criteria for facilities location problem

According to Yang & Lee (1997), location selection is a dynamic issue influenced by changing critical factors, often lacking an optimal solution Consequently, compromises are necessary when choosing a location, making the Analytic Hierarchy Process (AHP) model particularly applicable for addressing facility location challenges.

Table 2.2 Selected literature review for criteria for facilities location problem

Criteria for facilities location problem Source

 Market o Market growth potential o Proximity to market o Proximity to raw materials

 Transportation o Land transportation o Water transportation o Air transportation

 Labor o Cost of labor o Availability of skilled workers o Availability of semi-skilled workers

 Community o Housing o Education o Business climate

 Climate o Temperature o Rain o Humidity o Sunshine

 Geological o Earthquake intensity o Flood history o Interruption of earth

 Military o Active defensive o Non-active defensive o Frontier threats o Internal threats o Access to supported echelons o Density of supported echelons

 Economical o Native expert labors o Economic activities

Effective infrastructure is crucial for connectivity and economic growth, encompassing access to essential transport networks such as roads, railways, airports, and harbors Additionally, the availability of water resources, power lines, dispatching centers, and fuel stations plays a vital role in supporting various industries and enhancing logistics efficiency.

Qualitative factors for plant location

 Infrastructure availability (roads, sewer system, municipality services)

 Housing and residence availability for workers

Qualitative factors for distribution center location

 Provincial finance subsidies o Costs associated with logistics

 Coordination among supply chain members

 Transportation accessibility o Production and operation costs

 Tax structure and tax incentives

 Environmental o Ash management o Energy-saving o Effect on resources and nature reserves

 Soil o Distance from historical-tourist area o Greenhouse gas emission

 Social o Policy and legal support

 Changes in the energy policy o Work force

 Percentage of highly qualified people

 Minimum Wage o Impact on Society

New business unit in ICT industry

 Quantitative factors o Capital investment costs o Transportation costs o Operational costs

 Qualitative factors o Political and economic environment o Legal framework o Competition o Suppliers

 ICT specific factors o Human resources availability o Infrastructure availability o Cultural compatibility

METHODOLOGY

Research Process

The research design is a combination of the MCDM problem design and initial interviews to assess research necessity and ending interviews to assess research validity

Review literature to hypothesize the research problem

Conduct interviews with experts to assess research neccesity

Analyse the data from the interviews

Build a research model to conduct MCDM

Collect responses from experts and non-experts for the model

Analyze and interpret result of data analysis

Interview with experts to assess model clarity and usefulness

Simulation using updated expert model

Data collection for research necessity assessment

Two semi-structured interviews were conducted with an expert in Vietnamese 4.0 technology public policy and another expert in data center management, who has 14 years of experience in the IT/ICT industry Each interview consisted of 10 prepared questions, with additional inquiries made for clarification as needed Notably, Expert 2 highlighted the importance of uptime requirements and TIA standards in the discussion.

942 for location selection The interviews confirmed the necessity of the research and both raised the following issues:

(1) There is a lack of interest in environmental consequences regarding DC

(2) The site selection for DC are mainly made by firms and not by the Central Government There are no zoning requirements or incentives at National level

(3) There is a lack of qualified personnel to run DC

(4) There are difficulties in connecting with the national electrical grid Vietnamese electrical grid might not be stable in more rural areas.

MCDM model

In the context of facilities location models, various criteria such as environmental conditions, costs, labor, government policies, and infrastructure are essential for data center site selection, as highlighted in Table 2 Unlike traditional businesses, data centers do not compete based on their proximity; even when located next to each other, they do not negatively impact one another's operations, allowing for the exclusion of market competition from the criteria The significance of environmental factors, costs, labor availability, government regulations, and infrastructure is underscored by the TIA 942 Standard and insights from expert interviews, making them critical considerations in the decision-making process for data center locations.

Data centers are essential for business operations, and TIA 942 emphasizes the importance of locating them outside of 100-year flood plains to prevent severe water damage to electronics Additionally, data centers consume significant amounts of electricity, generating substantial heat that necessitates effective cooling systems Therefore, selecting locations with lower temperatures is a crucial factor in ensuring optimal performance and stability for these facilities.

Elevation plays a crucial role in data center site selection, as higher altitudes correlate with lower temperatures and decreased flood risks, although excessively high altitudes can hinder cooling machinery efficiency Data centers, as vital ICT facilities, require substantial electricity and high-speed internet bandwidth for optimal operation, along with water for cooling systems Cost considerations are essential, encompassing land use, construction, transportation, and operational expenses Labor factors include both the cost of labor and the availability of data center specialists in the region Additionally, government support is critical, involving incentives and energy policies that can impact site viability.

Research Methodology

The criteria in this research will be evaluated with Chang (1996) extensive analysis and Hue et al (2022) approach for Fuzzy AHP method for expert model

Fuzzy sets, introduced by Zadeh in 1965, provide a mathematical framework to represent uncertainty and imprecision in natural phenomena These sets have been widely applied in various fields, particularly in decision-making processes Jain's 1977 approach utilized fuzzy sets to transform qualitative terms into quantitative measures, leading to the development of fuzzy AHP, which integrates pairwise comparisons for enhanced decision-making.

15 method and fuzzy set theory is superior in capturing experts’ subjective opinions, especially for qualitative criteria which does not have a quantifiable method to evaluate

According to Dubois and Prade (1978), we have the following definition, the fuzzy number T  ( , a a a a w 1 2 , 3 , 4 ; ) is a trapezoidal fuzzy number if its membership function is defined as:

  where f T L   x and f T R   x are respectively the left and right membership functions of T

If a 1 a 2  a 3 a 4 , T becomes a generalized triangular fuzzy number, and can be denoted by T  ( , a a a w 1 2 , 4 ; ) If w1, then T is a normal fuzzy number

The operations for fuzzy numbers are as follow:

(l1,m1,u1).k=(l1.k,m1.k,u1.k) where k is a positive real number

The combination of fuzzy concept and AHP method was extensively by Chang in 1996, converting the fuzzy sets into crisp sets The method can be described as follow:

Let T = {t₁, t₂, , tₙ} represent a set of objects, and G = {g₁, g₂, , gₘ} denote a set of goals Utilizing Chang's approach, an extent analysis is conducted for each goal gᵢ corresponding to every object This process yields m extent analysis values for each object, allowing for a comprehensive evaluation of the relationships between the objects and the defined goals.

M g (j1, 2, , )n are triangular fuzzy numbers (TFNs) h

Assuming that ( , , ) i j g ij ij ij

M  l m u are the values of the extent analysis of the ith object for m goals, the value of the fuzzy synthetic extent, S i can be defined as:

, , , , 1, 2, i i n n n j j i g g j i j n n n ij ij ij j j j n n n n n n ij ij ij i j i j i j

Letting S 1 ( ,l m u 1 1 , ) 1 and S 2 ( ,l m u 2 2 , 2 ) be two TFNs, the degree of possibility of S 1 S 2 is defined as follows:

The membership degree of possibility can be described as:

 where d is the ordinate of the highest intersection point of two membership functions

Figure 3.3 The comparison two fuzzy numbers h

The degree of possibility for a convex fuzzy number to be greater than k convex fuzzy numbers S i I ( 1, 2, , )k can be defined as:

The weight vector is expressed by:

Using normalization, the weight vectors, where W is a non-fuzzy number, can be calculated as:

Wang et al (2008) highlighted that Chang's method could irrationally assign zero weight to valuable decision criteria and alternatives, which may lead to their exclusion from decision analysis, as discussed in Chapter 4 Additionally, Liu et al (2020) and Hue et al (2022) noted that Chang's approach is ineffective for specific types of triangular fuzzy numbers, functioning only with normal fuzzy numbers Hue et al (2022) subsequently proposed a new approach to address these limitations.

The generalized triangular fuzzy comparison matrix is expressed by:

  where x ij  ( a b c w ij , ij , ij ; ij ) ,

1 (1/ ,1/ ,1/ ; ) ij ij ij ij ij x   c b a w for ,i j1, ,n and i j.

The fuzzy synthetic extents, S i are defined using the correct normalization formula presented by Wang et al (2008) as follow: h

, , ; min( ) i i n n n j j i i i i ij g g j i j n n n ij ij ij j j j n n n n ij n n n n i j ij ij k k i j kj ij k k i j kj j j

, , ; min( ) , i n n n n j g ij ij ij ij j j j j

The centroid indices of fuzzy synthetic extents, denoted as S_i, can be determined using the method proposed by Dat et al (2012) Given the values S_1, S_2, , S_n of the fuzzy synthetic extents, the centroid point of all fuzzy numbers is calculated as C_i = (x S_i, y S_i) for i = 1, 2, , n.

The distance between the centroid point ( i , ), 1, 2, , i S S i

C  x y i  n and the minimum point G(x min,y min), is determined by:

D S G  x  x  y   y where x min  min( ), g i y min  min( w ij ) l 1 m 2 d u 2 m 1 u 1

Figure 3.4 The distance between the centroid point ( i , ) i S S i

C  x y and the minimum pointG(x min,y min) h

The weight vector W ( ,w 1 ,w n ) T of the fuzzy comparison matrix can be defined as:

Table 3.1 Saaty's preferences and triangular fuzzy conversion in the pair-wise comparison process

Verbal judgements of preferences between alternative i and alternative j

Hue et al fuzzy triangular sets

Ci is equally important to Cj 1 (1,1,1) (1,1,1,1.0)

Ci is slightly more important than Cj

Ci is strongly more important than Cj

Ci is very strongly more important than Cj

Ci is extremely more important than Cj

Table 3.2 Reciprocal for Saaty's preferences and triangular fuzzy conversion in the pair-wise comparison process

Verbal judgements of preferences between criteria I and criteria j

Ci is equally important to

Ci is slightly more important than Cj

Ci is very strongly more important than Cj

Ci is extremely more important than Cj

Data Collection for MCDM

A questionnaire was distributed to 25 experts in the Vietnamese data center industry, all with a minimum of 5 years of managerial experience Out of the responses received, 9 were submitted, but only 5 were deemed valid, while 4 were invalid due to participants selecting the same numbers multiple times Upon follow-up to verify the responses, only 1 expert provided feedback and adjusted their answers, resulting in a total of 6 valid responses.

Expert Position Company Experience in

Operation Team Leader CMC Telecom More than 5 years

M&E & Infrastructure Manager HTC-ITC More than 5 years

Technical Manager Viettel IDC More than 10 years

Data Center Manager True IDC

CEO DCServices More than 20 years

A second questionnaire was administered in Vietnamese to professionals in the IT/ICT industry in Vietnam, targeting individuals with a minimum of two years of experience Out of 34 participants, 14 responded, but one did not work in the IT/ICT sector, resulting in 13 valid responses Among these, 31% had 2-5 years of experience, another 31% had 6-10 years, and 7% had 11 or more years of experience.

With 15 years of experience on average, 31% of participants boast over 15 years in the field They have worked across 11 companies, predominantly in the IT/ICT sector, with one participant having experience in a banking institution To simplify the process for non-expert participants, a modified 5-point scale was employed instead of Saaty's traditional 9-point scale.

Table 3.4 Saaty's preferences and triangular fuzzy conversion in the pair-wise comparison process for non-expert model

Verbal judgements of preferences between criteria i and criteria j

Ci is equally important to Cj

Ci is more important than Cj

Following the weight calculation for the expert models, two interviews were held to evaluate the validity and practicality of the developed model and its criteria The first expert, a public policy specialist, had previously participated in the assessment interview, while the second expert, a technical manager with over a decade of experience in the IT/ICT sector, currently works in the data center industry.

Following the data analysis of the Fuzzy AHP and the calculation of weights, two expert interviews were conducted to validate the model's effectiveness One interview featured the same policy expert previously consulted for the necessity assessment, while the other involved Expert 3, who contributed to the MCDM data collection process.

The data is then cleaned and analyzed using the calculation in part 3.4 on Excel h

ANALYSIS RESULTS AND DISCUSSION

Expert model analysis and discussion

The expert model will be analyzed using both Chang (1996) and Hue et al (2022) methods, with calculation detailed in Chapter 3

Table 4.1 Code for criteria and sub-criteria

Criteria & sub-criteria name Code

Availability of DC specialists in the area L2

In Chapter 3, Chang's calculation presents fuzzy matrices and priority vectors for two levels of comparison, as detailed in Tables 8 to 13 The final weights derived from this analysis are summarized in Table 14.

Table 4.2 Fuzzy comparison matrix and its priority vector for the first level’s criteria of expert model using Chang approach

Table 4.3 Fuzzy comparison matrix and its priority vector for the second level’s criteria of expert model (Environment) using Chang approach

Table 4.4 Fuzzy comparison matrix and its priority vector for the second level’s criteria of expert model (Accessibility to resources) using Chang approach

Table 4.5 Fuzzy comparison matrix and its priority vector for the second level’s criteria of expert model (Cost) using Chang approach

Co1 Co2 Co3 Co4 Synthetic extent

Table 4.6 Fuzzy comparison matrix and its priority vector for the second level’s criteria of expert model (Labor) using Chang approach

Table 4.7 Fuzzy comparison matrix and its priority vector for the second level’s criteria of expert model (Local Government support) using Chang approach

Table 4.8 Final weight for criteria and sub-criteria of expert model using Chang approach

Natural disaster history 1 Accessibility to resources

Availability of DC specialists in the area 0.819

Wang et al (2008) noted that the Chang method resulted in numerous zero weights for sub-criteria, with nine sub-criteria being disregarded entirely While it highlighted the significance of factors such as natural disaster history, land use cost, operational cost, availability of DC specialists, and energy policy, this limitation hampers the model's applicability due to the exclusion of many criteria Consequently, this research adopts the Hue et al approach in the following section to address the issue of fuzziness.

4.1.2 Using Hue et al method

According to the calculations by Hue et al outlined in Chapter 3, Tables 15 to 26 present the fuzzy matrices and priority vectors derived from two levels of comparison The final weight is summarized in Table 27.

Table 4.9 Aggregated pair wise comparison matrices from experts for the first level’s criteria using Hue et al approach

Table 4.10 Fuzzy comparison matrix and its priority vector for the first level’s criteria of expert model using Hue et al approach

Criteria Synthetic extent Centroid point

Table 4.11 Aggregated pair wise comparison matrices from experts for the second level’s criteria (Environment) using Hue et al approach

Table 4.12 Fuzzy comparison matrix and its priority vector for the second level’s criteria of expert model (Environment) using Hue et al approach

Table 4.13 Aggregated pair wise comparison matrices from experts for the second level’s criteria (Accessibility to resources) using Hue et al approach

Table 4.14 Fuzzy comparison matrix and its priority vector for the second level’s criteria of expert model (Accessibility to resources) using Hue et al approach

Table 4.15 Aggregated pair wise comparison matrices from experts for the second level’s criteria (Cost) using Hue et al approach

Table 4.16 Fuzzy comparison matrix and its priority vector for the second level’s criteria of expert model (Cost) using Hue et al approach

Table 4.17 Aggregated pair wise comparison matrices from experts for the second level’s criteria (Labor) using Hue et al approach

Table 4.18 Fuzzy comparison matrix and its priority vector for the second level’s criteria of expert model (Labor) using Hue et al approach

Table 4.19 Aggregated pair wise comparison matrices from experts for the second level’s criteria (Local Government support) using Hue et al approach

Table 4.20 Fuzzy comparison matrix and its priority vector for the second level’s criteria of expert model (Local Government support) using Hue et al approach

Table 4.21 Final weight for criteria and sub-criteria for expert model using Hue et al approach

Criteria Weight score Sub-criteria Weight score

Natural disaster history 0.548 Accessibility to resources

Availability of DC specialists in the area 0.719 Local Government support

In comparison to Chang’s method outlined in section 4.1.1, Hue et al.'s approach yielded no zero values for the problem, while still maintaining relative differences in the importance of criteria, facilitating comparison and ranking Consistent with section 4.1.1, the dominant sub-criteria include Natural Disaster History, Land Use Cost, Operational Cost, Availability of DC Specialists, and Energy Policy Notably, the Environment criterion is assigned a higher weight, approaching that of Accessibility to Resources and Labor criteria This indicates that employing Chang's method may result in a loss of significance for certain criteria.

Non-expert model analysis and discussion

The non-expert model is synthesized using the method developed by Hue et al The fuzzy matrices and priority vectors for two levels of comparison are detailed in Tables 28 to 39, leading to the final weight presented in Table 40.

Table 4.22 Aggregated pair wise comparison matrices from non-experts for the first level’s criteria using Hue et al approach

Table 4.23 Fuzzy comparison matrix and its priority vector for the first level’s criteria of non-expert model using Hue et al approach

Criteria Synthetic extent Centroid point

Table 4.24 Aggregated pair wise comparison matrices from non-experts for the second level’s criteria (Environment) using Hue et al approach

Table 4.25 Fuzzy comparison matrix and its priority vector for the second level’s criteria of non-expert model (Environment) using Hue et al approach

Table 4.26 Aggregated pair wise comparison matrices from non-experts for the second level’s criteria (Accessibility to resources) using Hue et al approach

Table 4.27 Fuzzy comparison matrix and its priority vector for the second level’s criteria of non-expert model (Accessibility to resources) using Hue et al approach

Table 4.28 Aggregated pair wise comparison matrices from non-experts for the second level’s criteria (Cost) using Hue et al approach

Table 4.29 Fuzzy comparison matrix and its priority vector for the second level’s criteria of non-expert model (Cost) using Hue et al approach

Table 4.30 Aggregated pair wise comparison matrices from non-experts for the second level’s criteria (Labor) using Hue et al approach

Table 4.31 Fuzzy comparison matrix and its priority vector for the second level’s criteria of non-expert model (Labor) using Hue et al approach

Table 4.32 Aggregated pair wise comparison matrices from non-experts for the second level’s criteria (Local Government support) using Hue et al approach

Table 4.33 Fuzzy comparison matrix and its priority vector for the second level’s criteria of non-expert model (Local Government support) using Hue et al approach

Table 4.34 Final weight for criteria and sub-criteria for non-expert model using Hue et al approach

Criteria Weight score Sub-criteria Weight score

Natural disaster history 0.245 Accessibility to resources

Availability of DC specialists in the area 0.501 Local Government support

The expert and non-expert models exhibit significant differences in their evaluation criteria for hyperscale data center locations While both prioritize the same top two criteria, experts rank Cost as the most crucial factor, whereas non-experts place greater importance on Local Government support Additionally, non-experts consider Environmental criteria as the third most important, in stark contrast to experts, who view it as the least significant These variations highlight a deeper divergence in understanding between experts and non-experts regarding the complexities of data center location decisions.

Data centers must maintain constant uptime, making potential service disruptions a critical flaw in their infrastructure According to the Uptime Institute (2009) and expert rankings, the history of natural disasters is a vital factor in evaluating data center reliability, although non-experts tend to prioritize it lower than other criteria Similarly, while experts emphasize the importance of stable electricity for uninterrupted operations, non-experts do not rank it as highly Additionally, operational costs are deemed crucial by experts, whereas non-experts place greater importance on transportation costs.

Inconsistencies in labor criteria highlight differing perspectives between experts and non-experts regarding the availability of data center (DC) specialists Experts deem this availability crucial, while non-experts view two sub-criteria as nearly equally important The initial interviews reveal that Vietnam faces a significant shortage of highly skilled professionals in the DC industry, complicating the placement of hyperscale data centers in rural areas This challenge arises because available DC specialists tend to favor urban locations, where the quality of life is more appealing.

Experts and non-experts differ in their views on the significance of local government policies, with experts prioritizing energy policy, while non-experts emphasize the importance of incentives for businesses in selecting data center locations.

Additional comments collected from experts

Expert 1 questioned the inclusion of Labor cost in Operational cost and that commonly data centers are to be located within 30km of city centers as requirements

Experts discussed the implications of relocating to rural areas, emphasizing that such moves could equate to higher compensation for specialists One expert highlighted the importance of analyzing output costs, while another sought clarification on a scale ranging from 9 to 1, prompting the provision of an alternative Excel file for ease of use Additionally, considerations such as the availability of optical fiber networks, electricity infrastructure, oil transportation, safety, and environmental factors were deemed essential The discussion also included Uptime and TIA 942 requirements as critical components in the analysis.

Validity and usefulness assessment interviews

Public policy expert highlighted the absence of local government policies or tax incentives for data centers in Vietnam, making these factors irrelevant for site selection Additionally, the expert suggested that environmental criteria are often intuitively assessed, leading to the dismissal of areas like Central and Upper Northern Vietnam due to their mountainous geography, which contributes to their low ranking in location evaluations.

Both experts agreed that Operational costs might already include Labor cost, and this might confuse survey participants The importance of electricity and DC specialists were agreed by both experts h

Ranking of sub-criteria

The composite weights for each sub-criteria are outlined, with Table 41 illustrating the original expert model and Table 42 showcasing the revised model that excludes the Local Government support criteria for current location selection The most significant weight is attributed to the availability of data center specialists in the area, followed by operational costs and construction costs, which hold the second and third largest weights, respectively.

Table 4.35 Composite weights of all sub-criteria from expert model and their rankings against each other

Criteria Sub-criteria Composite weight

Availability of DC specialists in the area 0.122 2

Table 4.36 Composite weights of expert model and their rankings for selecting current locations (Expert model minus Local government support sub-criteria)

Criteria Sub-criteria Composite weight

Availability of DC specialists in the area 0.164 1

Simulation example

Due to time constraints and challenges in obtaining consensus among experts from the same company, a location comparison was not feasible Since strategies vary significantly between different companies, it is inappropriate to seek comparisons from experts across diverse firms Instead, a simulation was conducted to illustrate the necessary steps for managerial purposes Data was gathered from three non-experts through a survey format (see Appendix 3).

This analysis compares three distinct locations in Northern Vietnam: Bac Ninh, Quang Ninh, and Son La, utilizing the sub-criteria and weights outlined in Table 42 Son La, a mountainous province, offers significantly lower land costs but is located farther from Hanoi, the regional hub In contrast, Bac Ninh is the nearest to Hanoi and features flat terrain with a robust development of industrial parks aimed at relocating factories from the capital Quang Ninh stands out as a coastal province, providing convenient access to ports, the sea, and a high-speed optical fiber network.

Table 4.37 Overall rating of 3 sites Sub-criteria Weight Son La Bac Ninh Quang Ninh

DC specialists in the area 0.164 0.070 0.470 0.459

In this simulation, the suitable location is Son La h

CONCLUSION AND DICUSSION

Conclusion

The research considered both Chang and Hue et al method for solving fuzziness in experts’ opinions and agreed that Hue et al is the superior method for comparison

A comparative analysis of two weight sets, one created by experts and the other by non-experts, reveals significant disparities in opinions This study highlights the influence of expertise on decision-making and underscores the importance of recognizing location as a critical factor in expert assessments.

Cost is the primary concern for businesses in the data center industry when selecting a location, with operational costs being particularly significant Other key factors include labor and local government support, while environmental criteria and resource accessibility are less critical Electricity is a major concern, with stable electricity being the most crucial aspect of resource accessibility, energy policies being vital for local government support, and operational costs being central to overall cost considerations Additionally, the history of natural disasters is a significant environmental factor, especially in Vietnam, which is prone to floods, storms, and landslides due to its geography The need for stable electricity aligns with expert insights, and the MCDM analysis highlights the importance of training more data center specialists in Vietnam to support the industry's growth.

Research implications

In selecting real locations for hyperscale data centers, businesses should prioritize four key criteria due to the current absence of zoning policies, incentive programs, and energy regulations from both local and central governments in Vietnam Energy policies are crucial for the sustainability of the data center industry, making collaboration with local authorities essential to address long-term electricity consumption and environmental impacts This highlights a significant policy gap in Vietnam's data center sector, which is poised to play a vital role in the country's future energy consumption Therefore, it is imperative for businesses to engage with local governments to secure support, particularly in alignment with national digitization efforts.

Secondly, there is a clear opportunity for businesses capable of training DC specialists as the demands are still very high

Vietnam's abundant locations for wind and solar energy present an opportunity for hyperscale data centers to relocate to rural areas, enhancing their access to sustainable technologies While the Vietnamese data center industry is still in its infancy, establishing pioneering green data centers could provide significant long-term benefits for businesses.

Finally, since AHP application is a subjective initial assessment based on subjective judgments of experts, further studies using more rigorous and more objective methods should be applied for this problem.

Research contribution

The research focused on the Vietnamese data center industry, providing valuable insights into the challenges faced in Vietnam as a transitioning economy, where cost remains a primary concern Additionally, it contributes to the body of knowledge on facility location issues by examining a specific type of facility without considering market competition factors.

Limitation

A significant limitation of this research is the insufficient time available to gather data from experts The study focuses on technical and operations managers at data centers who have over five years of experience Since the experts come from diverse companies, the rankings provided do not reflect a singular business strategy but rather a general assessment Consequently, a thorough analysis to determine optimal data center locations cannot be performed Instead, a simulation illustrates how businesses can replicate the process to identify suitable locations by collaborating with experts from the same company who share a unified business strategy.

MCDM, or Multi-Criteria Decision Making, leverages diverse expert opinions to assess location-related challenges, making it a subjective evaluation method However, incorporating computational simulation modeling could enhance the rigor of this approach in future applications.

Arizton, (2022) Green Data Center Market - Global Outlook & Forecast 2022-

2027 Retrieved from https://www.arizton.com/market-reports/green-data-center- market

Chang, D.-Y (1996) Applications of the extent analysis method on fuzzy AHP

European Journal of Operational Research, 95(3), 649–655 doi:10.1016/0377-

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Ertuğrul, İ., & Karakaşoğlu, N (2007) Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection The International Journal of Advanced

The European Digital Strategy (2021) emphasizes the importance of energy-efficient cloud computing technologies and policies to foster an eco-friendly cloud market This initiative aims to enhance sustainability in digital services by promoting energy efficiency and reducing the environmental impact of cloud computing For more details, refer to the full document available at the European Commission's digital strategy website.

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HBS (Harvard Business School) (1989), Note on Facility Location, Publishing Division, Harvard Business School, Boston, MA

In their 2022 study published in Sustainability, Hue et al explored the prioritization of factors influencing lecturer research productivity through an enhanced Fuzzy Analytic Hierarchy Process The research highlights key determinants that affect academic output, providing valuable insights for improving research efficiency in educational institutions The findings emphasize the importance of a structured approach to understanding and enhancing lecturer performance in research activities For more details, refer to the study (doi:10.3390/su14106134).

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QUESTIONNAIRE FOR EXPERTS

You are invited to take part in a research study focused on identifying the criteria for locating newly constructed hyperscale data centers in Vietnam This study is led by Investigator Quynh Pham, with the support of co-supervisors Prof Dr Tanabu Monotari from Yokohama National University in Japan and Dr Luu Quoc Dat from the University of Economics and Business at Vietnam National University in Hanoi.

You are invited to participate in this questionnaire, since you are an expert in the data center industry Please take your time with the questionnaire

The questionnaire is expected to take approximately 15-20 minutes to complete

Participant information will remain confidential, with names, addresses, and personal data not requested If such information is provided, it will be excluded from the questionnaire to ensure anonymity Responses will solely be utilized for research purposes and report writing.

The insights and results gathered will fulfill the requirements for the MBA program at Vietnam Japan University, part of Vietnam National University in Hanoi Furthermore, these findings may be utilized in seminars, conference presentations, and research publications.

I understand that I may refuse to participate in this study freely

I also understand that if, for any reason, I wish to stop participating, I will be free to do so I agree to participate in this study

What is your current job title?

Where are you currently working at?

How many years have you worked in the IT or ICT industry?

How many years have you worked in the data center industry?

Have you been consulted on the problem of data center location in your job before?

Have you participated in the decision making of data center location in your job before?

The data center industry in Vietnam is currently small and dominated by a few key players, but the emergence of cloud-based services over the past three years indicates growth potential With the government's emphasis on 4.0 technology and digitalization, data centers are poised to become a critical industry supporting the backbone of the Internet The COVID-19 pandemic accelerated the need for digitization, highlighting the importance of robust infrastructure for remote work, electronic administration, and social media As a result, the data center sector is expected to become increasingly competitive in the near future.

The data center market in Vietnam is still developing, with most hyperscale facilities situated in urban centers like Hanoi, Ho Chi Minh City, Danang, and more recently, Can Tho; the only exception is a data center in Binh Duong Province, near Ho Chi Minh City In contrast, international trends, particularly in the US, show that one-third of data centers are moving to rural areas, allowing for better energy optimization through green technologies and access to natural resources The primary challenge of relocating data centers to the countryside is the significant initial investment required This research seeks to identify the decision criteria and their importance for selecting locations for new hyperscale data centers in Vietnam, based on expert opinions.

Below is the hierarchy for decision making h

Goal: Locate a new hyperscale data center

Criteria: Five criteria are chosen in the evaluation

1 Environment: It refers to environmental conditions of the location, including temperature, elevation and history of natural disasters

2 Accessibility to resources: It refers to the distance from required resources to operate hyperscale data centers

3 Cost: It refers to the cost of building, operating and transporting required by locating data center in the location

4 Labor: It refers to the human resources necessity for a new data center

5 Local Government support: It refers to the support given by local Government including tax and other incentives and energy policies

When evaluating environmental importance on a scale from 1 to 9, where 9 signifies extremely important and 1 indicates equally important, please assess the relative significance of the options listed in the left column compared to those in the right column.

Temperature Natural disaster history Natural disaster history

With respect to ACCESSIBILITY TO RESOURCES, using the scale from 1 to 9 (where

9 is Extremely important and 1 is Equally important), please indicate the relative importance of the option in the left column to the option in the right column

With respect to COST, using the scale from 1 to 9 (where 9 is Extremely important and

1 is Equally important), please indicate the relative importance of the option in the left column to the option in the right column

Land use cost Construction cost

Land use cost Transportation cost

Land use cost Operational cost

When evaluating LABOUR, please rate the relative importance of the options in the left column compared to those in the right column on a scale from 1 to 9, where 9 signifies Extremely important and 1 indicates Equally important.

DC specialists in the area

When evaluating LOCAL GOVERNMENT SUPPORT, please rate the importance of the options in the left column compared to those in the right column on a scale from 1 to 9, where 9 signifies Extremely important and 1 indicates Equally important.

When evaluating potential locations for hyperscale data centers, please rate the importance of the options listed on the left compared to those on the right using a scale of 1 to 9, where 9 signifies extreme importance and 1 indicates equal importance.

Government support Accessibility to resources

Please share your thoughts and recommendation to the research h

Thank you for your participation

Any inquiries can be addressed at 20117014@st.vju.ac.vn or quynhpham42@gmail.com h

Bạn được mời tham gia nghiên cứu về tiêu chí chọn địa điểm cho trung tâm dữ liệu siêu cường tại Việt Nam, do Phạm Trúc Quỳnh, học viên thạc sĩ tại Đại học Việt Nhật, thực hiện Nghiên cứu được hướng dẫn bởi Giáo sư Tiến sĩ Tanabu Monotari từ Đại học Quốc gia Yokohama và Tiến sĩ Lưu Quốc Đạt từ Đại học Kinh tế, Đại học Quốc gia Hà Nội Sự tham gia của bạn rất quan trọng vì bạn là chuyên gia trong lĩnh vực trung tâm dữ liệu Vui lòng đọc kỹ nội dung khảo sát.

Khảo sát sẽ kéo dài khoảng 15-20 phút để hoàn thiện bao gồm phần giới thiệu dự án và nội dung

Thông tin cá nhân của người tham gia khảo sát sẽ được bảo mật và không chia sẻ cho bên thứ ba Tên, tuổi, địa chỉ và các thông tin cá nhân khác sẽ không được yêu cầu; nếu có, chúng sẽ được loại bỏ và không tiết lộ Các câu trả lời sẽ chỉ phục vụ cho mục đích nghiên cứu và viết báo cáo Kết quả nghiên cứu sẽ được sử dụng cho chương trình MBA tại Đại học Việt Nhật, Đại học Quốc gia Hà Nội, và có thể được trình bày tại hội thảo hoặc trong các công trình nghiên cứu xuất bản.

Phần 1: Thông tin cho người tham gia

Tôi hiểu rằng tôi có thể từ chối tham gia vào nghiên cứu này

Tôi cũng hiểu rằng nếu vì bất kỳ lý do gì, tôi muốn ngừng tham gia, tôi sẽ được tự do làm như vậy

Tôi đồng ý tham gia nghiên cứu này

Phần 2: Thông tin về người tham gia

Xin vui lòng cung cấp thông tin về quá trình làm việc và chuyên môn của Quý anh/chị Hiện tại, Quý anh/chị đang đảm nhiệm chức vụ gì?

Quý anh/chị hiện đang công tác ở đâu?

Quý anh/chị có kinh nghiệm bao nhiêu năm trong ngành IT/ICT?

Quý anh/chị có kinh nghiệm bao nhiêu năm trong mảng trung tâm dữ liệu?

Quý anh/chị đã từng được tham vấn về vấn đề lựa chọn địa điểm cho trung tâm dữ liệu trong công việc chưa?

Quý anh/chị đã từng được tham gia vào quá trình lựa chọn địa điểm cho trung tâm dữ liệu trong công việc chưa?

Phân tích đa tiêu chí cho vị trí đặt trung tâm dữ liệu hyperscale tại Việt Nam cho thấy ngành trung tâm dữ liệu đang được điều hành chủ yếu bởi những công ty lớn như FPT, VNPT, Viettel và CMC Trong ba năm gần đây, các dịch vụ điện toán đám mây quy mô nhỏ đang ngày càng gia tăng Với sự thúc đẩy của Chính phủ về công nghệ 4.0 và số hóa, trung tâm dữ liệu đã trở thành một ngành trọng tâm, đóng vai trò xương sống của Internet Đại dịch Covid-19, mặc dù bất ngờ, đã không ngăn cản sự phát triển của số hóa mà còn làm tăng nhanh chóng nhu cầu về dịch vụ này Xu hướng làm việc từ xa, quản trị điện tử và mạng xã hội đã tạo ra một nền tảng vững chắc, khiến ngành trung tâm dữ liệu trở nên cạnh tranh hơn bao giờ hết.

Thị trường trung tâm dữ liệu ở Việt Nam hiện vẫn còn nhỏ, với phần lớn các trung tâm hyperscale tập trung ở các thành phố lớn như Hà Nội, TP HCM, Đà Nẵng và Cần Thơ, ngoại trừ một trung tâm tại Bình Dương Trong khi đó, xu hướng toàn cầu cho thấy nhiều cơ sở dữ liệu đang được di chuyển ra khỏi các đô thị về vùng nông thôn, đặc biệt là ở Mỹ, nơi một phần ba trung tâm dữ liệu nằm ở khu vực này Việc chuyển các trung tâm dữ liệu ra ngoài thành phố không chỉ giúp tối ưu hóa tiêu thụ năng lượng thông qua công nghệ xanh mà còn tận dụng tài nguyên nước tự nhiên, điều này yêu cầu không gian rộng lớn hơn.

Nghiên cứu này tập trung vào việc xác định các tiêu chí quyết định và trọng số của chúng dựa trên ý kiến của các chuyên gia, nhằm lựa chọn vị trí phù hợp cho các trung tâm dữ liệu hyperscale mới tại Việt Nam.

Dưới đây là bảng phân cấp cho việc ra quyết định h

Mục tiêu: Xác định vị trí trung tâm dữ liệu siêu cấp mới

Tiêu chí: Năm tiêu chí được chọn trong đánh giá

1 Môi trường: là điều kiện môi trường của địa điểm, bao gồm nhiệt độ, độ cao và lịch sử thiên tai

QUESTIONNAIRE FOR NON-EXPERTS

You are invited to take part in a research study focused on the criteria for locating newly built hyperscale data centers in Vietnam This study is led by Investigator Quynh Pham, with guidance from co-supervisors Prof Dr Tanabu Monotari from Yokohama National University in Japan and Dr Luu Quoc Dat from the University of Economics and Business at Vietnam National University in Hanoi.

You are invited to participate in this questionnaire, since you are a professional working in the IT/ICT industry Please take your time with the questionnaire

Participant information will remain confidential, as names, addresses, and other personal details are not requested If such information is provided, it will be excluded from the questionnaire to ensure anonymity Responses will solely be utilized for research purposes and report writing.

The insights and results gathered will fulfill the requirements for the MBA program at Vietnam Japan University, part of Vietnam National University in Hanoi Furthermore, these findings may be utilized in seminars, conference presentations, and academic research publications.

Have you worked in the data center industry?

SECTION C: Multi-criteria Analysis to Locate Hyperscale Data Center in Vietnam This research aims to survey the criteria to select hyperscale data centers in Vietnam

Hyperscale data center can be defined as a data center with more than 5,000 servers and has the size of more than 1,000 square meters

Below is the hierarchy for decision making

Using the scale from 1 to 5 (in which, 5 is Very more important, 3 is More important and 1 is Equally important), please compare the criteria h

For example: If criteria Temperature is more important than criteria, select as follow:

With respect to ENVIRONMENT, using the scale from 1 to 5 (in which, 5 is Very more important, 3 is More important and 1 is Equally important), please compare the criteria

Temperature Natural disaster history Natural disaster history

With respect to ACCESSIBILITY TO RESOURCES, using the scale from 1 to 5 (in which, 5 is Very more important, 3 is More important and 1 is Equally important), please compare the criteria

4 5 Very more impor tant Water resources

With respect to COST, using the scale from 1 to 5 (in which, 5 is Very more important,

3 is More important and 1 is Equally important), please compare the criteria

Land use cost Construction cost

Land use cost Transportatio n cost

Land use cost Operational cost Construction cost

With respect to LABOUR, using the scale from 1 to 5 (in which, 5 is Very more important, 3 is More important and 1 is Equally important), please compare the criteria

Labor cost Availability of DC specialists in the area h

With respect to LOCAL GOVERNMENT SUPPORT, using the scale from 1 to 5 (in which, 5 is Very more important, 3 is More important and 1 is Equally important), please compare the criteria

With respect to the goal to find LOCATION FOR HYPERSCALE DATA CENTER, using the scale from 1 to 5 (in which, 5 is Very more important, 3 is More important and

1 is Equally important), please compare the criteria

Government support Accessibility to resources

Please share your thoughts and recommendation to the research

Bạn được mời tham gia nghiên cứu về tiêu chí chọn địa điểm cho trung tâm dữ liệu siêu cường tại Việt Nam, do Phạm Trúc Quỳnh, học viên thạc sỹ tại Đại học Việt Nhật, thực hiện Nghiên cứu được hướng dẫn bởi Giáo sư Tiến sỹ Tanabu Monotari từ Đại học Quốc gia Yokohama, Nhật Bản, và Tiến sỹ Lưu Quốc Đạt từ Đại học Kinh tế, Đại học Quốc gia Hà Nội Sự tham gia của bạn là quan trọng vì bạn làm việc trong ngành IT/ICT Xin vui lòng đọc kỹ nội dung khảo sát.

Thông tin cá nhân của người tham gia sẽ được bảo mật và không chia sẻ cho bên thứ ba Tên, tuổi, địa chỉ và các thông tin riêng tư sẽ không được yêu cầu; nếu có, chúng sẽ bị loại bỏ khỏi khảo sát Các câu trả lời chỉ được sử dụng cho mục đích nghiên cứu và viết báo cáo Kết quả nghiên cứu sẽ phục vụ cho chương trình MBA tại Đại học Việt Nhật, Đại học Quốc gia Hà Nội, và có thể được trình bày tại hội thảo hoặc trong các nghiên cứu xuất bản.

Xin vui lòng cho biết về lịch sử công tác và chuyên môn của Quý anh/chị, bao gồm chức vụ hiện tại mà Quý anh/chị đang đảm nhiệm.

Quý anh/chị có kinh nghiệm bao nhiêu năm trong ngành IT/ICT??

☐Không làm trong ngành ICT/IT

Quý anh/chị có làm việc trong mảng trung tâm dữ liệu không?

Phần 3: Phân tích đa tiêu chí cho vị trí đặt trung tâm dữ liệu hyperscale tại Việt Nam

Nghiên cứu này nhằm mục đích khảo sát các tiêu chí chọn địa điểm cho trung tâm dữ liệu hyperscale tại Việt Nam

Trung tâm dữ liệu hyperscale được định nghĩa là trung tâm dữ liệu với 5,000 máy chủ và có diện tích hơn 1,000 mét vuông

Dưới đây là bảng phân cấp cho việc ra quyết định

Khả năng tiếp cận tài nguyên là yếu tố quan trọng để vận hành các trung tâm dữ liệu hyperscale, bao gồm việc gần nguồn nước phục vụ cho làm mát, đảm bảo nguồn điện ổn định để tránh gián đoạn, có các địa điểm thuận lợi để tiếp nhận phần cứng cho nâng cấp hoặc bảo trì, và băng thông tốc độ cao để duy trì kết nối hiệu quả cho trung tâm dữ liệu.

Chi phí liên quan đến việc thiết lập trung tâm dữ liệu bao gồm các khoản chi phí sở hữu đất, chi phí xây dựng mới, chi phí vận hành và chi phí vận chuyển cần thiết để đưa trung tâm dữ liệu vào hoạt động tại địa điểm đã chọn.

4 Nhân công: là nhu cầu nhân lực cho một trung tâm dữ liệu mới

5 Hỗ trợ của Chính quyền địa phương: là sự hỗ trợ của Chính quyền địa phương bao gồm các chính sách khuyến khích và các chính sách năng lượng

Sử dụng thang điểm từ 1 đến 5 (trong đó 5 là Rất quan trọng hơn và 1 là Quan trọng như nhau), vui lòng so sánh các tiêu chí với nhau

Ví dụ: Nếu tiêu chí Nhiệt độ quan trọng hơn tiêu chí Độ cao, chọn như sau

Để đánh giá tiêu chí Môi trường, chúng ta sử dụng thang điểm từ 1 đến 5, trong đó 5 thể hiện mức độ quan trọng cao nhất và 1 thể hiện mức độ quan trọng thấp hơn Vui lòng tiến hành so sánh các tiêu chí này với nhau để xác định mức độ ưu tiên trong từng khía cạnh môi trường.

Nhiệt độ Lịch sử thiên tai Lịch sử thiên tai Độ cao

Để đánh giá khả năng tiếp cận tài nguyên, chúng ta sử dụng thang điểm từ 1 đến 5, trong đó 5 thể hiện mức độ rất quan trọng và 1 là mức độ quan trọng như nhau Việc so sánh các tiêu chí này giúp xác định rõ ràng mức độ ưu tiên và tầm quan trọng của từng yếu tố trong quá trình tiếp cận tài nguyên.

Nguồn nước Nguồn điện ổn định

Nguồn nước Địa điểm trung tâm

Nguồn nước Băng thông tốc độ cao h

Nguồn điện ổn định Địa điểm trung tâm Nguồn điện ổn định

Băng thông tốc độ cao Địa điểm trung tâm

Băng thông tốc độ cao

Khi đánh giá tiêu chí Chi phí, chúng ta sử dụng thang điểm từ 1 đến 5, trong đó 5 thể hiện mức độ Rất quan trọng và 1 là mức độ Quan trọng như nhau Việc so sánh các tiêu chí với nhau giúp xác định rõ ràng sự ưu tiên và tầm quan trọng của từng yếu tố trong quá trình ra quyết định.

Chi phí sử dụng đất

Chi phí xây dựng mới Chi phí sử dụng đất

Chi phí vận chuyển Chi phí sử dụng đất

Chi phí vận hành Chi phí xây dựng mới

Chi phí vận chuyển Chi phí xây dựng mới

Chi phí vận hành Chi phí vận chuyển

Với tiêu chí Nhân công, chúng tôi sử dụng thang điểm từ 1 đến 5, trong đó 5 thể hiện mức độ quan trọng cao nhất và 1 là mức độ quan trọng thấp nhất Vui lòng tiến hành so sánh các tiêu chí này với nhau để xác định sự ưu tiên và tầm quan trọng tương đối của từng yếu tố.

DC chất lượng cao tại địa phương h

Dựa trên tiêu chí Hỗ trợ của Chính quyền địa phương, hãy sử dụng thang điểm từ 1 đến 5 để so sánh các tiêu chí, trong đó 5 thể hiện mức độ Rất quan trọng và 1 là mức độ Quan trọng như nhau.

Để xác định vị trí đặt trung tâm dữ liệu hyperscale, cần đánh giá các tiêu chí bằng thang điểm từ 1 đến 5, trong đó 5 thể hiện mức độ quan trọng cao nhất và 1 là mức độ quan trọng tương đương Việc so sánh các tiêu chí này sẽ giúp đưa ra quyết định chính xác và hiệu quả cho việc lựa chọn vị trí tối ưu.

Môi trường Khả năng tiếp cận tài nguyên

Môi trường Hỗ trợ của

Chính quyền địa phương Khả năng tiếp cận tài nguyên

Khả năng tiếp cận tài nguyên

Chi phí Hỗ trợ của

Nhân công Hỗ trợ của

Xin Quý anh/chị ủng hộ ý kiến bổ sung hoặc chia sẻ cho nghiên cứu

Chân thành cảm ơn sự tham gia của Quý anh/chị

Bất kỳ câu hỏi nào cũng có thể gửi về 20117014@st.vju.ac.vn hoặc quynhpham42@gmail.com h

QUESTIONNAIRE FOR COMPARING LOCATION

You are invited to participate in a research study focused on identifying the criteria for locating newly built hyperscale data centers in Vietnam This study is led by Investigator Quynh Pham, with the collaboration of Prof Dr Tanabu Monotari from Yokohama National University in Japan and Dr Luu Quoc Dat from the University of Economics and Business at Vietnam National University in Hanoi.

You are invited to participate in this questionnaire, since you are a professional working in the IT/ICT industry Please take your time with the questionnaire

Participant information will remain confidential, as names, addresses, and personal data are not requested If such information is provided, it will be excluded from the questionnaire to ensure anonymity Responses will solely be utilized for research purposes and report writing.

The insights and data gathered will fulfill the requirements for the MBA program at Vietnam Japan University, part of Vietnam National University in Hanoi Furthermore, these findings may be utilized in seminars, conference presentations, and research publications.

Have you worked in the data center industry?

This research focuses on identifying the key criteria for selecting hyperscale data centers in Vietnam, utilizing a structured multi-criteria analysis to facilitate informed decision-making.

Based on the above criteria, please compare each of the location below based on each sub-criteria for 3 locations: Bac Ninh, Quang Ninh, Son La

For sub-criteria TEMPERATURE, please compare the three locations h

For sub-criteria ELEVATION, please compare the three locations

For sub-criteria NATURAL DISASTER HISTORY, please compare the three locations

For sub-criteria WATER RESOURCES, please compare the three locations

For sub-criteria STABLE ELECTRICITY, please compare the three locations h

For sub-criteria CENTRAL LOCATION, please compare the three locations

For sub-criteria HIGH SPEED BANDWIDTH, please compare the three locations

For sub-criteria LAND USE COST, please compare the three locations

For sub-criteria CONSTRUCTION COST, please compare the three locations h

For sub-criteria TRANSPORTATION COST, please compare the three locations

For sub-criteria OPERATIONAL COST, please compare the three locations

For sub-criteria LABOR COST, please compare the three locations

For sub-criteria AVAILABILITY OF DC SPECIALISTS, please compare the three locations h

Please share your thoughts and recommendation to the research

Bạn được mời tham gia nghiên cứu về tiêu chí chọn địa điểm cho trung tâm dữ liệu siêu cường tại Việt Nam, do Phạm Trúc Quỳnh, học viên thạc sỹ tại Đại học Việt Nhật, thực hiện Nghiên cứu được hướng dẫn bởi Giáo sư Tiến sỹ Tanabu Monotari từ Đại học Quốc gia Yokohama và Tiến sỹ Lưu Quốc Đạt từ Đại học Kinh tế, Đại học Quốc gia Hà Nội Sự tham gia của bạn rất quan trọng vì bạn làm việc trong ngành IT/ICT Xin hãy đọc kỹ nội dung khảo sát.

Thông tin cá nhân của người tham gia sẽ được bảo mật và không chia sẻ với bên thứ ba Tên, tuổi, địa chỉ và các thông tin cá nhân khác sẽ không được yêu cầu; nếu có, chúng sẽ được loại bỏ khỏi khảo sát Các câu trả lời chỉ được sử dụng cho mục đích nghiên cứu và báo cáo Kết quả nghiên cứu sẽ phục vụ cho chương trình MBA tại Đại học Việt Nhật, Đại học Quốc gia Hà Nội, và có thể được trình bày tại hội thảo hoặc trong các nghiên cứu xuất bản.

Xin vui lòng cung cấp thông tin về lịch sử công tác và chuyên môn của Quý anh/chị, bao gồm chức vụ hiện tại mà Quý anh/chị đang đảm nhiệm.

Quý anh/chị có kinh nghiệm bao nhiêu năm trong ngành IT/ICT??

☐Không làm trong ngành ICT/IT

Quý anh/chị có làm việc trong mảng trung tâm dữ liệu không?

Phần 3: Phân tích đa tiêu chí cho vị trí đặt trung tâm dữ liệu hyperscale tại Việt Nam

Nghiên cứu này nhằm mục đích khảo sát các tiêu chí chọn địa điểm cho trung tâm dữ liệu hyperscale tại Việt Nam

Dưới đây là bảng phân cấp cho việc ra quyết định

Khả năng tiếp cận tài nguyên là yếu tố quan trọng trong việc vận hành các trung tâm dữ liệu hyperscale, bao gồm việc tiếp cận nguồn nước để làm mát, nguồn điện ổn định để đảm bảo hoạt động liên tục, các địa điểm thuận lợi để tiếp nhận phần cứng cho nâng cấp hoặc bảo trì, và băng thông tốc độ cao để đảm bảo kết nối mạng hiệu quả cho trung tâm dữ liệu.

Chi phí để thiết lập trung tâm dữ liệu bao gồm các khoản chi phí liên quan đến sở hữu đất, xây dựng mới, vận hành và vận chuyển cần thiết cho địa điểm.

4 Nhân công: là nhu cầu nhân lực cho một trung tâm dữ liệu mới

5 Hỗ trợ của Chính quyền địa phương: là sự hỗ trợ của Chính quyền địa phương bao gồm các chính sách khuyến khích và các chính sách năng lượng h

Dựa trên các tiêu chí trên, mời Quý Anh/Chị đánh giá điều kiện cho từng tiêu chí cho 3 địa điểm sau: Bắc Ninh, Quảng Ninh, Sơn La

Với tiêu chuẩn NHIỆT ĐỘ, hãy so sánh ba địa điểm sau:

Với tiêu chuẩn ĐỘ CAO, hãy so sánh ba địa điểm sau:

Với tiêu chuẩn LỊCH SỬ THIÊN TAI, hãy so sánh ba địa điểm sau:

Với tiêu chuẩn NGUỒN NƯỚC, hãy so sánh ba địa điểm sau:

Với tiêu chuẩn NGUỒN ĐIỆN ỔN ĐỊNH, hãy so sánh ba địa điểm sau: h

Với tiêu chuẩn ĐỊA ĐIỂM TRUNG TÂM, hãy so sánh ba địa điểm sau:

Với tiêu chuẩn BĂNG THÔNG TỐC ĐỘ CAO, hãy so sánh ba địa điểm sau:

Với tiêu chuẩn CHI PHÍ SỬ DỤNG ĐẤT, hãy so sánh ba địa điểm sau:

Với tiêu chuẩn CHI PHÍ XÂY DỰNG, hãy so sánh ba địa điểm sau:

Với tiêu chuẩn CHI PHÍ VẬN CHUYỂN, hãy so sánh ba địa điểm sau:

Với tiêu chuẩn CHI PHÍ VẬN HÀNH, hãy so sánh ba địa điểm sau:

Với tiêu chuẩn GIÁ NHÂN CÔNG, hãy so sánh ba địa điểm sau:

Với tiêu chuẩn NGUỒN LỰC CHUYÊN VIÊN DC TẠI ĐỊA PHƯƠNG, hãy so sánh ba địa điểm sau:

Tiêu đề	Multicriteria Analysis For Hyperscale Data Centers Placement In Vietnam
Tác giả	Pham Truc Quynh
Người hướng dẫn	Prof. Tanabu Monotari, Dr. Luu Quoc Dat
Trường học	Vietnam National University, Hanoi Vietnam Japan University
Chuyên ngành	Business Administration
Thể loại	master’s thesis
Năm xuất bản	2022
Thành phố	Hanoi

Định dạng
Số trang	94
Dung lượng	2,52 MB