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Are Cloud Enabled Virtual Labs Economical? - A Case Study Analyzing Cloud based Virtual Labs for Educational Purposes

  • Lübeck University of Applied Sciences

Abstract and Figures

Cost efficiency is an often mentioned strength of cloud computing (Talukader et al., 2010). In times of decreasing educational budgets virtual labs provided by cloud computing might be an interesting alternative for higher education organizations or IT training facilities. This contribution analyzes the cost advantage of virtual educational labs provided via cloud computing means and compare these costs to costs of classical educational labs provided in a dedicated manner. This contribution develops a four step decision making model which might be interesting for colleges, universities or other IT training facilities planning to implement cloud based training facilities. Furthermore this contribution provides interesting findings when cloud computing has economical advantages in education and when not. The developed four step decision making model of general IaaS * applicability can be used to find out whether an IaaS cloud based virtual IT lab approach is more cost efficient than a dedicated approach.
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A Case Study Analyzing Cloud Based Virtual Labs for Educational Purposes
Nane Kratzke
ubeck University of Applied Sciences
onkhofer Weg 239
23562 L
cloud, computing, virtual labs, higher education, IaaS, decision, making, cost, estimation
Cost efficiency is an often mentioned strength of cloud computing (Talukader et al., 2010). In times of de-
creasing educational budgets virtual labs provided by cloud computing might be an interesting alternative
for higher education organizations or IT training facilities. This contribution analyzes the cost advantage of
virtual educational labs provided via cloud computing means and compare these costs to costs of classical ed-
ucational labs provided in a dedicated manner. This contribution develops a four step decision making model
which might be interesting for colleges, universities or other IT training facilities planning to implement cloud
based training facilities. Furthermore this contribution provides interesting findings when cloud computing
has economical advantages in education and when not. The developed four step decision making model of
general IaaS
applicability can be used to find out whether an IaaS cloud based virtual IT lab approach is more
cost efficient than a dedicated approach.
Infrastructure as a Service. This contribution follows the IaaS, PaaS and SaaS definition of NIST (Mell
and Grance, 2011).
Cloud computing is one of the latest developments
within the business information systems domain and
describes a delivery model for IT services based on
the Internet, and it typically involves the provision of
dynamically scalable and often virtualized resources.
Nowadays cloud computing is used in e-learning sce-
narios as well because it fits very well to e-learning
requirements. When e-learning has a distinctive re-
mote aspect why delivering necessary educational re-
sources like labs has to be delivered still in a classical
and dedicated manner?
Accompanying the increasing relevance of cloud
computing in research literature and media there
arise manifold publications covering the application
of cloud computing to e-learning. E.g. (Cayirci
et al., 2009) present a training and education cloud.
But only some publications covering aspects how to
use cloud computing to provide virtual labs. (Oberg
et al., 2011) presents a ”virtual lab” system architec-
ture for academic high-performance computing but
without educational purposes. And a lot of other au-
thors reflect about to use cloud computing as tech-
nical infrastructure for providing e-learning systems
(Dong et al., 2009), (Ko and Young, 2011), (Masud
and Huang, 2011) digital campus systems (Li and Li,
2011) or personalized learning environment systems
(Liang and Yang, 2011) but this has nothing to do with
virtual laboratories in the understanding of this con-
tribution. Only (Thi
ebaut et al., 2011) show a very
interesting example of processing wikipedia dumps
by applying cloud computing technologies and com-
pare them to dedicated cluster solutions in practical
college courses. This could be named a ”virtual lab”
but it is not done by the authors due to a different
focus of their paper. So this contribution is about a
case study how to use cloud computing in computer
science study programs providing virtual IT labs for
practical courses in the higher education domain or
for IT training facilities. According to (Barr, 2010)
we define:
A Virtual Educational IT Lab is a collec-
tion of compute, storage and net-working re-
sources provided by an educational organiza-
tion for educational or research purposes. A
virtual lab can be provided to a single or a
small group of students to support these stu-
dent(s) in solving practical problems by pro-
viding a necessary IT infrastructure. Provided
resources are available for short-term use
and are billed by actual resource consump-
tion generated by educational or research ac-
tivities. Typically all provided resources are
rented by the educational organization from a
cloud service provider.
Beside use cases like hosting websites, support soft-
ware development cycles, short-term system demon-
strations, data storage, disaster recovery and business
continuity, media processing and rendering, overflow
processing or large-scale scientific data processing
(Barr, 2010) mentions training use cases as very cloud
compatible and economical use cases. This paper
does not denial this postulation in general but ad-
vocates a more critical view like (Weinmann, 2011)
or (Mazhelis et al., 2011) do. Ongoing research
(Kratzke, 2011a), (Kratzke, 2011b), (Kratzke, 2012a)
shows that cost advantages of cloud computing are
deeply use case specific and you should be aware
of comparing non comparable use cases. And nev-
ertheless we have to consider some shortcomings of
cloud computing which are mentioned in literature
and should be considered in planning phases of vir-
tual labs.
Shortcomings of Cloud Computing. (Kratzke,
2012a) showed by analysing popular IT management
frameworks like COBIT, TOGAF or ITIL that – from
an IT management point of view cloud computing
provides more benefits than disprofits. Nevertheless
Kratzke showed also the presence of so called sub-
stantial show stoppers for cloud based approaches.
Especially security and compliance management as-
pects may come along with substantial ”showstop-
pers”. But these aspects are of minor relevance re-
garding cloud based virtual labs.
According to (Truong and Dustdar, 2010) or (Kratzke,
2012a) cloud computing is also characterized by an ex
ante cost intransparency. This very important weak-
ness (from an IT management and decision making
point of view) is even little reflected in literature so
far. To answer the question whether a cloud-based
approach is more cost efficient than a dedicated ap-
proach it has to be answered the question what costs
will be generated per month before a cloud based ap-
proach enters operation (Walker et al., 2010). This
is very difficult to answer ex ante because it is influ-
enced by a bunch of interdependent parameters. But
for profound decision making for or against cloud
based virtual labs exactly this question has to be an-
swered before a virtual lab is established in a dedi-
cated or cloud based manner.
Outline. Therefore this contribution has the follow-
ing outline. The research methodology is described in
section 2. An economical decision making model for
or against cloud based virtual labs is described briefly
in section 3. A corresponding case study of a com-
puter science study program is analyzed in section 4.
Section 5 shows derived findings from the analyzed
case study which are useful to plan virtual lab sup-
ported practical courses in higher education. Section
5 shows furthermore how to transfer the presented use
case to other computer science related lectures and
related practical courses and operationalizes simple
rules for controlling costs pragmatically. The contri-
bution ends with a summary and outlook in section
Especially the work of (Weinmann, 2011) is very in-
teresting from this decision making point of view be-
cause it shows how to decide for or against a cloud
based approach very pragmatically. This contribution
is about using the work of Weinman to build up a sim-
ple decision making model for or against cloud based
approaches. This decision making model has been ap-
plied in a presented case study covering the higher ed-
ucation domain.
For practical courses in higher university or college
education we want to find out whether it is more
economical to provide classical dedicated educational
labs or to use IaaS
providing virtual labs for student
practical courses.
The analyzed case study was a web technology lec-
ture for computer science students being held at
the L
ubeck University of Applied Sciences in 2011.
During the practical courses of this lecture students
formed groups of 5 or 6 persons in order to build up
a website for a scientific conference on robotic sail-
(project 1) or establish a google map based auto-
matic sailbot tracking service (project 2) for the same
conference. All groups were assigned cloud service
accounts provided by Amazon Web Services. The
groups were asked to use these accounts in order to
fulfill their projects in a complete cloud based man-
ner. The resource consumption of all groups were
measured by analyzing billing as well as usage data
provided by the cloud service provider Amazon Web
Services (AWS).
This contribution follows the IaaS, PaaS and SaaS def-
inition of NIST (Mell and Grance, 2011).
World Robotic Sailing Conference 2011, see
Both projects had three main phases (see table 1). The
training phase (calendar week 13 to 15) was about
to get all students into touch with the cloud service
provider tool suite. In the project phase (calendar
week 16 to 23) all groups had to develop a cloud based
solution for project 1 or project 2. All groups had to
proof that their solutions were adequate for a 24x7 op-
eration within a 24x7 phase (calendar week 21 to 24).
So project and live phase overlapped within a three
week period. Calendar week 24 was used for the sys-
tem presentations. The practical course of the lecture
finished with a migration phase. The best solutions
were used as website (project 1) or google maps based
automatic sailbot tracking service (project 2) for the
conference and were migrated to the destination envi-
ronment (which also was a cloud based environment)
in calendar week 25.
Training Project P M
CW 13
CW 14
CW 14
CW 15
CW 16
CW 18
CW 19
CW 20
CW 21
CW 22
CW 23
CW 24
CW 25
P = Presentation M = Migration
CW = Calendar Week
Figure 1: Project phases
(Weinmann, 2011) is stressing the following interest-
ing fact which is a crucial input for pragmatic decision
making for or against cloud based system implemen-
tations especially on a IaaS level of cloud computing:
A pay-per-use solution obviously makes sense
if the unit cost of cloud services is lower than
dedicated, owned capacity. [...]
[...] a pure cloud solution also makes sense
even if its unit cost is higher, as long as
the peak-to-average ratio of the demand curve
is higher than the cost differential between
on-demand and dedicated capacity. In other
words, even if cloud services cost, say, twice
as much, a pure cloud solution makes sense
for those demand curves where the peak-to-
average ratio is two-to-one or higher. (Wein-
mann, 2011)
According to Weinman the peak-to-average ratio is
the essential indicator whether a cloud based ap-
proach is economical reasonable or not. So it is
not necessary to estimate costs per month of a cloud
based solution exactly. It is sufficient to proof that
cloud based costs are smaller then a dedicated sys-
tem implementation. And this can be figured out by
analysing the peak usage as well as the average usage
of a system.
Due to page limitations we will use in this contribu-
tion the average to peak ratio at p in a simplified form
and refer to (Kratzke, 2012b) where the decision mak-
ing model is presented more detailly.
at p :=
According to (Weinmann, 2011) we have to compare
the costs of a classical dedicated approach with the
costs of a cloud based approach. On the IaaS level
it is common to be billed per service usage with a
granularity of the atomic timeframe t level. Which
would be in case of Amazon Web Service that you
are billed for a server instance per complete (or par-
tial) hour usage. Let us name our dedicated costs per
atomic timeframe d and our cloud costs per atomic
timeframe c. Cloud costs c can be easily figured out
because they are provided as pricing by their cloud
service providers
. Dedicated costs per atomic time-
frame d are a little more complex to calculate. Never-
theless for estimations we can assume, that they can
be defined via their amortizations. If a dedicated in-
stance can be procured for p value units
their dedi-
cated costs per atomic timeframe (ATF) can be calcu-
lated as follows
(p) =
(p) =
3 · 365 · 24h
(p) =
5 · 365 · 24h
Typical amortization timeframes are a 3 year or a 5
year hardware regeneration interval (see equation 2).
So a 500 $ server would have the following dedicated
costs per atomic timeframe of 1h over a amortization
interval of 3 years.
E.g. Amazon Web Services publishes
these cloud costs per atomic timeframe here:
E.g. US Dollars $ or Euro e
Be aware this assumption do not account typical ad-
ditional operational efforts like administration, cooling or
electricity. Nevertheless we do not want to calculate exact
costs we only want to know whether a cloud based approach
is more economical than a dedicated one. In this case it is
OK to give the dedicated side an advantage by not account-
ing aspects like administration, cooling, electricity, etc. al-
though these costs are included in the variable costs on the
cloud service provider side.
(500$) =
3 · 365 · 24h
According to (Weinmann, 2011) the peak-to-average
ratio pta should be greater than the relation between
the variable costs per atomic timeframe c and the ded-
icated costs per atomic timeframe d which can be ex-
pressed in the following form:
pta >
pta · d > c c <
at p
In other words equation 4 provides a clear decision
criteria to decide for or against a cloud based ap-
proach. By knowing your average to peak ratio
at p, your hardware procurement costs per instance
p as well as your hardware amortization timeframes
(which is typically 3 or 5 years) it is possible to calcu-
late a maximum of cloud costs per atomic timeframe
until a cloud based approach is economical (see
equation 5). Whenever a cloud service provider can
realize instance pricings below c
we decide for a
cloud based approach
in all other cases we should
realize the system in a dedicated approach
at p
All data used in this section has been collected by the
supporting cloud service provider AWS. The billing
data as well as usage data are standard informations
provided by AWS for each client. They are provided
by AWS in CSV or XML format and can be therefore
easily downloaded and processed by anyone. We used
billing as well as EC2 usage data provided by AWS.
No special preprocessing of the data were performed
by AWS for this special use case analysis. So anyone
can run the same analysis by using a standard toolset
4.1 Cost Analysis
Table 1 shows all costs per group within the analysed
timeframe. In total the L
ubeck University of Applied
Sciences had to spend 847.01$ in providing (virtually)
unlimited amount of server instances to 49 students
organized in 9 groups within a timeframe of 13 cal-
endar weeks. This sounds impressive but says in fact
Only from an economical point of view.
Also only from an economical point of view.
We used only open source software. MySQL as a
database and R as a analytical and visualization tool.
nothing about how cost efficient this performed cloud
based approach was. Could we had reached the same
results with a classical dedicated approach?
CW 13 CW 14 – CW 17 CW 18 – CW 21 CW 22 – CW 25
Costs per Month (aligned to Weeks)
Calendar Weeks (CW)
Costs in USD
0 100 200 300 400 500
instancehour (62%)
datastorage (34%)
adressing (3%)
datatransfer (0%)
Main Cost Drivers
B 1 (5%)
B 2 (7%)
B 3 (7%)
B 4 (4%)
B 5 (6%)
A 1 (10%)
A 2 (31%)
A 3 (10%)
A 4 (19%)
Costresponsibilty of Groups
Histogram of Costs per Group
Cost Ranges in USD
# Groups
0 50 100 150 200 250 300
0 1 2 3 4
Figure 2: Cost Analysis
Let us have a look at the cost analysis provided in fig-
ure 2. Figure 2(A) shows the costs per month. AWS
is only providing billing data on a monthly basis so
we had to align months to the corresponding analysed
calendar weeks. By interpreting these graphs we must
Group Size Project Costs in $
A 1 5 WRSC Website 88.39$
A 2 6 WRSC Website 265.37$
A 3 4 WRSC Website 88.14$
A 4 6 WRSC Website 162.88$
B 1 6 Sailbot Tracking 41.17$
B 2 6 Sailbot Tracking 57.58$
B 3 6 Sailbot Tracking 57.46$
B 4 5 Sailbot Tracking 37.42$
B 5 5 Sailbot Tracking 48.58$
Table 1: Group Overview
have in mind the general project phases performed by
all student groups (see table 1). So please keep the
following phases in mind:
Week 13 - 15: initial training phase
Week 16 - 23: project/development phase
Week 21 - 24: (overlapping) 24x7 phase
Week 24: presentation
Week 25: migration
So figure 2(A) shows that most of the costs were gen-
erated in 24x7 phase (calendar week 21 - 24). So 24x7
seems to be expensive.
Figure 2(B) shows the main cost drivers. Almost 2/3
of the costs were generated by instancehours that
means running servers and being billed for per hour.
Almost 1/3 of the costs were generated by data stor-
age that means all costs which have to do with the
provision of server hard drives, storing backups or
other data storage services. Other costs like address-
ing (requesting IP addresses, DNS names, etc.) or
even data transfer had no relevant impact to the total
costs. So the main cost driver were instance hour
costs – or box usage as it is sometimes called.
Figure 2(C) and (D) shows that analyzed groups had
quite different cost responsibilities. The expected
value of cost responsibility would be 100%/9
Nevertheless the most cost efficient group was only
responsible for about 4% and the most cost ”lavish”
group was responsible for about 31% of the total
costs. It turned out that the A groups groups re-
sponsible for setting up the website – consumed more
resources than the sailbot tracking B groups (check
table 1). So cloud generated costs are use case
specific. Different problems result in different archi-
tectural solutions which results in different cost be-
13 14 15 16 17 18 19 20 21 22 23 24 25
Average Box Usage
Maximum Box Usage in an hour
Maximum and Average Box Usage
Calendar Week
Used Server Boxes
0 10 20 30 40 50
13 14 15 16 17 18 19 20 21 22 23 24 25
Accumulated Processing Hours per Week
Calendar Week
Processing Hours
0 500 1000 1500 2000
14 16 18 20 22 24
0.0 0.2 0.4 0.6 0.8 1.0
Average Box to Maximum Box Ratio
according to Weinman
Calendar Week
Avg to Max Box Usage Ratio
Figure 3: Analysed Box Usage
4.2 Usage Analysis
As we have found out in our analyzed use case – main
cost driver was server/box usage (or instance hours
see figure 2(B)). Thats why a detailed box usage
analysis has been performed and is shown in figure 3.
Figure 3(A) shows the maximum and average box
usage per calendar week measured within the ana-
lyzed timeframe (calendar week 13 - 25). Figure 3(B)
shows the total sum of all processing hours generated
by all used server boxes/instances per calendar week.
Figure 3(C) shows the average to peak load ratio (ac-
cording to equation 1) per calendar week.
Within the initial training phase (calendar week 13
- 21) the usage characteristic shows an extremely high
maximum box usage but an astonishing low average
usage. This characterizes an extreme peak load situ-
ation and results in extreme low at p ratios (see equa-
tion 1 and figure 3(C)). According to equation 4 or
the definition of c
(equation 5) this shows a very
ideal cloud computing (peaky) situation. So train-
ing phases seems to be very economical interesting
cloud computing use cases which is also stated by
(Barr, 2010)
Within the project phase (calendar week 13 - 15)
the usage characteristic shows dramatically reduced
maximum as well as average box usages. Neverthe-
less the at p ratios (see equation 1 and figure 3(C))
stay in a very comfortable situation for cloud com-
puting approaches. So we still have a peaky usage
situation but on a dramatical lower level. So also de-
velopment phases seems to be very economical in-
teresting cloud computing use cases which is also
stated by (Barr, 2010)
The 24x7 phase (calendar week 21 - 24) shows raised
maximum as well as average box usages (see figure
3(A)). Also the at p ratios are raising within this phase
– nevertheless the peaky usage characteristic remains
but less distinctive. The 24x7 phase can be clearly
seen in the accumulated processing hours (see figure
3(B)) which shows a clear peak in calendar weeks 22
- 24. We have stated already in section 4.1 that 24x7
seems to be expensive and we can harden this conclu-
sion by our usage analysis. This might surprise some
The migration phase (calendar week 25) was charac-
terized by transferring the best solutions for the web-
site and sailbot tracking service into the operational
environment. Within this phase the systems run in
a steady load scenario as can be seen in figure 3(A)
(almost the same average box as well as maximum
usage) and in figure 3(C) (an at p ratio near 1.0). Ac-
cording to equation 5 this shows an extreme uncom-
fortable situation for cloud computing situations so
steady loads seem to be no economical interesting
cloud computing use cases.
(Barr, 2010) is chief evangelist of Amazon Web Ser-
vices – so this source can not be rated objective. Neverthe-
less our case study hardens his point.
Remember (Barr, 2010) is chief evangelist of Amazon
Web Services. He is not objective. Nevertheless our case
study hardens this point too.
It is often stated that cloud computing is good for host-
ing websites (a 24x7 job). This is true but only if not pro-
vided on IaaS level or when provided on IaaS level with a
clear peaky usage characteristic. Steady loads are seldom
economical IaaS use cases. So you should be very clear
about your use case, the related (un)peaky usage character-
istic and the IaaS, PaaS or SaaS provision level. No one
states that economical decision making for or against cloud
computing is obvious.
4.3 Economical decision analysis
As we have seen in sections 4.1 and 4.2 we can iden-
tify different phases which are more cloud compatible
than others from an ecomomical point of view. Train-
ing and development phases show very low at p ratios
(see figure 3(C)) and therefore indicate peaky usage
characteristics of resources which advantages cloud
computing realizations
(check equation 5). Other
phases with less peaky usage characteristics (like our
24x7 or migration phase) disadvantage cloud comput-
ing realizations.
So we have identified pro and cons for a cloud based
realization of our educational labs. But how to de-
cide? Now we are going to apply our decision model
presented in section 3.
Step 1: Determine the at p Ratio
First of all we have to calculate our overall average
to peak load ratio at p. Our analysis timeframe cov-
ered the calendar weeks 13 - 25. So an intuitive time-
frame for average building would be 13 weeks but
this implicates a continual usage of an educational lab
over a complete year which is very uncommon. In
the university or college business educational labs are
typically used one time per semester. In most cases
an educational lab can be used only one time a year
(per semester that means average building over 26
weeks) or even only one time per year (every second
semester that means average building over 52 weeks
which is a year). In our analyzed timeframe 7612
hours of instance usage were generated (the sum of
all bars of figure 3(B)). So equation 7 shows the av-
erage amount of servers which would be necessary to
provide 7612 processing hours within a 26 or 52 week
26 · 7 · 24h
52 · 7 · 24h
Now we can build up our average to peak ratio. Our
maximum server usage within an atomic timeframe of
1 hour was 49 servers (please check figure 3(A)). So
we get the following at p ratios for a 26 or 52 week
at p
at p
Step 2: Determine your dedicated costs
From an economical point of view.
First of all we have to find out how much would cost
us a dedicated server. At the L
ubeck University of
Applied Sciences our procurement office could pur-
chase the smallest possible server version
for about
3055$ (2167e
). Equation 2 told us to calculate our
dedicated costs per atomic timeframe (1h) in the fol-
lowing way for a 5 year amortization interval:
(3055$) =
5 · 365 · 24h
Step 3: Determine your maximal economical cloud
Equation 5 told us to calculate our c
costs in the
following way:
at p
at p
In other words. In our analyzed case study a cloud
service provider (regarding a 5 year amortization time
frame for servers) could be
28.57 times more expensive in case of a one time
per semester usable educational lab or even
55.56 times more expensive in case of a only one
time per year usable educational lab
then own dedicated costs – a cloud based educational
lab would be still more economical then a dedicated
one. Ok these figures are really impressive but do we
find appropriate resources within our maximal costs?
We have to figure this out in our last step 4 to make
a profound decision for or against a cloud based ap-
proach for our virtual lab.
Step 4: Determine appropriate cloud resources
Now we know our maximal cloud costs and have to
look if our cloud service provider can deliver appro-
priate and comparable resources. In our case this is
Amazon Web Services, but it could be any other IaaS
cloud service provider as well. We do this exemplar-
ily for a 26 week timeframe
. But it works abso-
lutely the same for all other timeframes or IaaS cloud
service providers as well.
Dell PowerEdge Server R610, 2.13 GHz Intel Xeon
processor, 8GB memory, 140 GB hard drive (valid on
28th October 2011) with approximately 2 ECU so the
AWS instance type pendant would be something between
a Standard Small (1 ECU) or Large (4 ECU) instance type.
Check out detailed instance type informations of AWS here:
Exchange rate 1.41$ = 1.00e from 28th October 2011.
Because in our special case we can use our educational
lab every semester – so two times a year.
Table 2 shows all instance types of AWS and their al-
located costs. Remember section 4.3 told us, that all
instance types cheaper than 1.99 $/h result into cloud
based solutions which are more economical than ded-
icated approaches.
AWS Instance Type ECU Price/h
Micro < 1 0.025$ yes -
Small (Standard) 1 0.095$ yes o
Large (Standard) 4 0.38$ yes o
XL (Standard) 8 0.76$ yes +
XL (High Memory) 6.5 0.57$ yes +
2x XL (High Memory) 13 1.14$ yes ++
4x XL (High Memory) 26 2.28$ no ++
Medium (High CPU) 5 0.19$ yes o
XL (High CPU) 20 0.76$ yes ++
Table 2: AWS Instance Types and Pricings, according to
AWS pricing informations on 28th Oct. 2011, EU (Ire-
land) Region, On-Demand Instances, Linux/UNIX Operat-
ing System
As you can see in table 2 all provided instance types
(except one
) of AWS in the EU Region are econom-
ical in the sense of section 3 and equations 4 and 5.
The most similar instance types listed in table 2 are
marked as ’o’. (’-’ stands for worser, ’+’ for better
and ’++’ for much better than a dedicated reference
So in our analysed use case the Medium (High CPU)
or may be even the Small (Standard) AWS instance
types (see table 2) are the most comparable systems
to our dedicated reference system (Dell PowerEdge
Server R610). Both provide variable cloud costs
clearly below our maximum costs of 1.99$/h (see
equation 9). So in our analysed use case a virtual IT
lab for education is much more economical than
a dedicated approach. Thats why we decided to
implement cloud based IT labs for our practical
courses instead of classical dedicated educational
In section 5.1 we derive some general findings from
our use case analysis which might be useful to find in-
teresting practical courses in higher education of com-
4x XL (High Memory) instance type is not economical
reasonable but this instance type is not comparable to our
reference system because it is much more powerful.
In our case the Dell PowerEdge Server R610
puter science which are likely to show similar cost
characteristics like the analyzed use case. Neverthe-
less our analysis shows also in section 5.2 that the
here presented approach is transferable to other lec-
tures and related practical courses as well. We op-
erationalize some general advices in section 5.3 for
setting up cloud based virtual labs and develop some
rules for virtual lab users to control costs pragmati-
5.1 General Findings
Cloud computing economics love peak load sce-
narios. As equation 4 showed cloud computing be-
comes more and more economical as the average to
peak ratio nears 0 - this indicates that peaky as well
as seldom usage of educational labs argue for cloud
based virtual lab approaches, extreme continual us-
age of labs argue against cloud based approaches from
an economical point of view. Nevertheless we always
have to analyze the individual characteristic of a prac-
tical course by measuring its average to peak ratio in
real world.
Cloud generated costs are use case specific. Differ-
ent problems result in different realization architec-
tures generating different costs as well as usage char-
acteristics. So be aware of comparing non comparable
use cases! You should run for each practical course
the here mentioned cost analysis and decide for or
against a cloud computing based virtual lab approach
after you have figured out your practical course spe-
cific average to peak ratio.
24x7 seems to be expensive and of no economical
interest for cloud computing if not associated with
a peaky usage characteristic. So try to avoid 24x7
tasks in practical courses whereever possible. This
will save a lot of money.
One of the main cost driver is box usage per com-
plete (or partial) hour. Second relevant cost driver
is data storage. Data transfer seems to have a mi-
nor impact to costs. This might be only valid for our
analyzed use case and should be handled with care.
Please check out other publications like (Mazhelis
et al., 2011) for communication intensive use cases
if you plan to use communication/data transfer inten-
sive use cases in your practical courses.
Constant loads over time seem to be no econom-
ical interesting use cases for cloud computing. If
you have to deal with this kind of use cases in prac-
tical courses (which is typically very unlikely) think
about classical dedicated approaches. Cloud comput-
ing approaches might be only feasible in these cases
due to their convenience but not necessary economi-
cal reasonable.
5.2 Transferability of the Approach
Because of the identified cost advantages in the ana-
lyzed use case there was one more question arising.
Is it possible to transfer made experiences to other
lectures and practical courses as well? Therefore all
module descriptions of computer science (near) study
programs of the L
ubeck University of Applied Sci-
ences were analyzed. The following lectures and re-
lated practical courses showed potential being sup-
ported by virtual labs
Industrial Networks and Databases
Database Management (and Engineering)
Integrated Informationsystems
Distributed Systems
Operating Systems (if Linux/UNIX based)
It turned out that in each analyzed computer science
related study program between 8.33% up to 23.10%
of all practical courses could be supported by virtual
labs according to the module descriptions of the study
programs. Whether these practical courses will show
similar cost advantages is up for ongoing research.
Nevertheless it seems very likely from made experi-
ences. So it should by obvious to the reader that the
presented approach is easily transferable to other lec-
tures and related practical courses.
To identify interesting lectures and practical courses
it is good to know what is making a practical course
virtual lab compatible. Our identified courses have
the following virtual lab relevant educational require-
ments in common:
a practical course requires databases
a practical course requires distributed processing
a practical course requires webtechnologies on
server instances
a practical course requires linux/unix based in-
a practical course requires parallel processing
a practical course requires large-scale data pro-
Whenever there exist a practical course with one of
the above mentioned educational requirements a vir-
tual lab might be a reasonable option.
Most of the mentioned lectures and related practical
courses are in german. So lecture titles are translated.
5.3 Advices for Transferring
We learned a lot about providing virtual labs to stu-
dents via cloud computing means. Some of our made
experiences are operationalized as advices for every-
one who is planning similar approaches.
Advice Nr. 1: It is likely that Cloud Computing
will be new for students. So plan an initial training
phase with the cloud tooling of the service provider
which is necessary to solve the problems of the prac-
tical course. We did this with by providing very de-
tailed step by step manual. This seems to be an effec-
tive way. And force your students to be present in the
first initial training phase.
Advice Nr. 2: If you are planning to use virtual
clouds in more than one practical course you might
think about a virtual lab training course in one of
the first semesters of a study program. This might
avoid double and triple trainings.
Advice Nr. 3: Let your students play within their
virtual lab in presence phases, at home, at university,
wherever they want. Cloud computing gives flexibil-
ity. Use it! You are not bounded to presence phases
any more if training phase is passed.
Advice Nr. 4: Watch your costs! We identified two
main cost drivers. First cost driver is instance oper-
ating hours. Whenever an instance is running money
flows. Make this clear to students by setting up rules.
Rule Nr. 1: Shut down all instances after finishing
your experiments. Some cloud providers have APIs
to control resources. You may think about developing
garbage collector scripts shutting down every running
instance at midnight? This will discipline even lazy
students. Most cloud service providers provide differ-
ent instance types which can be formalised in another
rule. Rule Nr. 2: Use the smallest instance types for
experiments. It is always possible to upscale.
Second most relevant cost driver is data storage gen-
erated by not running instances. Rule Nr. 3: Use as
few instances as possible. Every instance produces
storage costs. Some cloud providers provide detailed
access control settings. Use them to let no group (ac-
count) instantiate more than a limited amount of in-
stances in parallel.
Advice Nr. 5: And to increase a general cost
awareness it is good to establish another rule. Rule
Nr. 4: If two groups show the same performance. The
one with less costs will get the better grade. So re-
source consumption will not become a key criteria for
grading. But our experiences show that this rule dis-
ciplines a lot.
Table 3 summarizes all relevant rules to be considered
by virtual lab users.
Rule Description
1 Shut down all instances after finishing experiments.
2 Use the smallest possible instance types for experiments.
3 Delete all unnecessary stopped instances.
4 Generated costs are considered for grading.
Table 3: Cost Control Rules for Virtual Lab Users
Not mentioned so far but very important. Cloud
based virtual labs are scalable. The here presented
approach is working with 10 students. It is also work-
ing with 100 or even 1000 students. We only have to
provide more virtual labs but do not have to invest into
dedicated hardware with a typical three or ve year
or even longer financial commitment. That provides
flexibility and provides the possibility to manage pe-
riods of time which a significant step-up or step-down
of students.
Nevertheless not all practical courses are virtual lab
compatible. This might be of technical, functional or
of economical reasons. This contribution presented a
pragmatical four step decision making model for
or against IaaS based virtual IT labs for educational
purposes primarily from an economical point of view.
The decision making is pragmatical and inspired by
(Weinmann, 2011). We applied this decision making
model (see section 3) in a concrete use case of prac-
tical educational labs in the higher education domain
(colleges, universities, etc., see section 4) and showed
that it can be very economical to use cloud based ed-
ucational labs. It turned out that cloud based educa-
tional labs (in the analysed use case) have a more than
25 to 50 times cost advantage (see section 4.3 [step
3]) to classical dedicated approaches. So cloud com-
puting seems to be a very promising and economical
variant of providing educational labs for university
or college practical courses which is mainly due to an
inherent peaky usage characteristics of practical uni-
versity or college courses (see figure 3). This is a very
essential finding for the L
ubeck University of Applied
Sciences and might be of interest for other colleges,
universities or IT training facilities as well.
Furthermore this contribution refined some general
findings to identify cloud compatible lectures and
practical courses (see section 5.1) and showed that
the approach of virtual labs is transferable to a signif-
icant amount of practical courses or lectures in com-
puter science study programs (see section 5.2). Other
study programs were not analyzed but the here pre-
sented virtual lab approach is not necessarily bound to
computer science study programs. Our transferability
analysis showed that all courses with requirements of
providing databases, distributed processing capabili-
ties, parallel processing or large-scale data processing
are interesting candidates. These requirements might
be also typical for engineering study programs.
Finally this contribution refined some advices for ap-
plying virtual labs in practical courses and proposes
some easy mind-keeping rules for efficient cost con-
trol (see section 5.3 and table 3).
Outlook. In our ongoing research we plan to transfer
the here mentioned cloud based virtual lab approach
to more lectures and their related practical courses in
order to double check our conclusions and to harden
our postulations. By collecting more usage as well
as billing data from different courses we hope to get
an even better understanding of relevant cost driving
parameters. By collecting this data we hope to im-
prove a more precise cost estimation. It is furthermore
planned to develop a kind of management software
for setting up and managing virtual labs. A key fea-
ture would be an automatic average to peak ratio cal-
culation based on provided usage data by a cloud ser-
vice provider in order to answer the question whether
a cloud based approach is more economical than a
dedicated one.
Thanks to Amazon Web Services for supporting our
ongoing research with several research as well as ed-
ucational grants. Thanks to my students and Michael
Breuker for using cloud computing in practical edu-
cation. This contribution would not exist without their
engagement. Let me thank Alexander Schlaefer and
Uwe Krohn for organizing the World Robotic Sailing
Championship 2011 in L
ubeck and their confidence
in our students.
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