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Education 2012, 2(7): 239-246
DOI: 10.5923/j.edu.20120207.04
Virtual Labs in Higher Education of Computer Science
Why they are Valuable? How to Realize? How much will
It Cost?
Nane Kratzke
Lübeck University of Applied Sciences, Department of Electrical Engineering and Computer Science, 23562, Lübeck, Germany
Abstract Cost efficiency is an often mentioned strength of cloud computing. In times of decreasing educational budgets
virtual labs provided by cloud computing might be therefore an interesting option for higher education organizations or IT
training facilities. An analysed use case of a web technology lecture and a corresponding practical course of a computer
science study programme shows that is not possible to answer the question in general whether cloud computing approaches
are economical or not. The general implication of this finding for higher education is, that the application of cloud
computing can be only answered from a course specific point of view. This contribution shows why. But also how
universities, colleges or other IT training facilities can make profound and course specific decisions for or against cloud
based virtual labs from an economic point of view. The presented approach is inspired by Weinmans “mathematical proof
of the inevitability of cloud computing”. The key idea is to compare peak to average usage of virtual labs and relate this
ratio to costs of classical dedicated labs. The ratio of peak and average usage indicates whether a use case (from a pure
economical point of view) is cloud compatible or not. This contribution derives also some findings when cloud computing
in higher education has economical advantages or disadvantages. Regarding the analysed use case it turned out that virtual
labs are able to provide a more than 25 times cost advantage compared to classical dedicated approaches. Virtual labs can
be applied frictionless to classical as well as distance study programmes and virtual labs provide a convenient infrastructure
for project as well as problem based learning in computer science. Nevertheless provider of virtual labs should always
consider usage and resulting cost characteristics. This article shows how to do this.
Keywords Virtual Lab, Higher Education, Cloud Computing, Computer Science, Practical Course, Lecture, Project
Based Learning, Problem Based Learning, Cloud Economics
1. Introduction
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[1].
Cloud computing involves the provision of dynamically
scalable and often virtualized resources. Cloud computing is
used in e-learning scenarios as well because it fits very well
to e-learning requirements. When e-learning has a distinctive
remote aspect why delivering necessary educational re-
sources like labs has to be delivered still in a classical and
on-premise manner?
Accomp anying th e increas ing relev ance of clou d
computing in research literatu re and media there arise
manifold publications covering the application of cloud
computing to e-learning. E.g.[2] present a training and
* Corresponding author:
kratzke@ fh-lu ebeck.de (Nane Kratzke)
Published online at http://journal.sapub.org/edu
Copyright © 2012 Scientific & Academic Publishing. All Rights Reserved
education cloud. But only some publications concentrate on
aspects how to use cloud computing to provide virtual labs.
Oberg et al.[3] present a “virtual lab” system architecture
for academic high-performance computing but without
educational purposes. And a lot of other authors reflect about
to use cloud computing as technical infrastructure for
providing e-learning systems[4],[5],[6] digital campus
systems[7] or personalized learning environment systems[8]
but this has nothing to do with virtual laboratories in the
understanding of this contribution. Only Thiébaut et al.[9]
show a very interesting problem based education example of
processing Wikipedia dumps by applying cloud computing
technologies and compare them to dedicated cluster
solutions in practical college courses. This approach could
be named a ”virtual lab” but this is not done due to another
intent of the publication. 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.
Influenced by Barr[10] we define:
A Virtual Lab is a collection of compute, storage and
networking resources provided by an educational
240 Nane Kratzke: Virtual Labs in Higher Education of Computer Science Why they are valuable?
How to realize? How much will it cost?
organization for educational or research purposes. A virtual
lab can be provided to a single or a small group of students to
support student(s) in solving practical problems by providing
a necessary IT infrastructure. Provided resources are
available for short-term use, accessible via internet only and
are billed by actual resource consumption generated by
educational or research activities. Typically all provided
resources are rented by the educational organization from a
cloud service provider.
Beside use cases like hosting websites, support software
development cycles, short-term system demonstrations, data
storage, disaster recovery and business continuity, media
processing and rendering, overflow processing or large-scale
scientific data processing Barr mentions training use cases as
very cloud compatible and economical use cases[10]. This
paper does not denial this postulation in general but
advocates a more critical view like[11] or[12]. Ongoing
research[13],[14],[15] show that cost advantages of cloud
computing are deeply use case specific and cloud customers
have to be highly aware of comparing non comparable use
cases.
2. Research Methodology
Therefore this contribution adopts the approach of
Weinman for cloud computing decision making[12]. This
decision making model has been applied in a case study.
The analysed case study was a web technology lecture for
computer science students (bachelor) being held at the
Lübeck University of Applied Sciences in summer 2011.
The lecture and practical courses were repeated in winter
2011/2012 as well as summer 2012 with varying problems of
comparable complexity to solve. During the practical
courses of this lecture students formed groups of 5 or 6
students in order to build up a website for a scientific
conference on robotic sailing (project 1) or establish a
Google map based automatic sailbot tracking service (project
2) for the same conference. All groups were assigned cloud
service accounts provided by Amazon Web Services (AWS).
The resource consumption of all groups were measured by
analysing billing as well as usage data provided by the cloud
service provider AWS.
Figure 1. Project Phases
All projects had three main phases (see figure 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 operation (calendar week 21 to 24). So project and
24x7 phase overlapped within period of three weeks.
Calendar week 24 was used for the system 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 environment (which also was a cloud based
environment) in calendar week 25.
The project phases have been designed to generate peaky
usage characteristics (training and project) as well as
constant load scenarios (24x7, presentation and migration). It
is very well understood that cloud economics prefer peaky
usage characteristics but show disadvantages covering
constant load scenarios[1],[10]. So the setting (see figure 1)
was chosen to cover both relevant aspects of cloud
economics. Both aspects should appear in pure (only peak
loads, CW 13–20; only constant loads, CW 25) and in a
combined manner (constant loads with additional peak loads,
CW 21 - 24) in order to have the analytical opportunities to
derive interesting aspects how to operate virtual labs
efficiently.
3. Decision Making Model
Weinman[12] stresses the following interesting fact which
is a crucial input for pragmatic decision making for or
against cloud based system implementations especially on a
IaaS level of cloud computing:
“[...] 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.”[12]
According to Weinman the peak-to-average ratio is the
essential indicator whether a cloud based approach 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 than a
dedicated system implementation. 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 the average to peak
ratio atp in a simplified form and refer to[16] where the
decision making model is presented in more detail.
atp := avg/max = 1/pta (1)
In cloud computing it is common to be billed for service
usage per hour. So let us name our dedicated costs per hour d
and our cloud costs per hour c. Cloud costs c can be easily
figured out because being provided as pricing information by
cloud service providers. Dedicated costs per hour d are a
little more complex to calculate. Nevertheless for
Education 2012, 2(7): 239-246 241
estimations we can assume, that they can be defined by their
regeneration intervals. If a dedicated instance can be
procured for p value units their dedicated costs per hour
within that regeneration interval can be calculated in the
following way (for example for a typical 3 or 5 year
interval): d3year(p) = p / 3 365 24 h (2)
d5year(p) = p / 5 365 24 h (3)
So a 500$ server would generate approximately two cents
of dedicated costs per hour regarding a regeneration interval
of 3 years.
d3year(500$) = 500$ / 3 365 24 h 0.019 $/h
According to[12] the peak-to-average ratio pta should be
greater than the relation between the variable costs c and the
dedicated costs d which can be expressed in the following
form: pta > c/d pta d c < d/atp (4)
In other words this formula provides a clear decision
criteria to decide for or against a cloud based approach. By
knowing your average to peak ratio atp, your hardware
procurement costs per instance p as well as your hardware
regeneration timeframes (which are typically 3 or 5 years) it
is possible to calculate a maximum of reasonable cloud costs
cMAX. Whenever a cloud service provider can realize instance
pricings below cMAX a cloud based approach is reasonable –
in all other cases a cloud based approached should be
avoided (just from an economical point of view, of course
there might exist other higher order considerations).
cMAX := d/atp (5)
4. Analysed Case Study
In section 4.1 we will present our results from a cost
perspective and in section 4.2 from a usage perspective. Let
us have in mind, that our students had a virtually unlimited
amount of servers they could use to solve their problem (see
section 2). The following sections will analyse how this
virtual unlimited amount of resources have been used over
time and what this implicates to economical applicability of
virtual labs in higher education.
All data used in this section has been collected by the
supporting cloud service provider AWS. The billing data as
well as usage data was provided by AWS in CSV or XML
format. Different cloud service providers may provide other
but similar data formats. We used regular billing as well as
EC2 usage data provided by AWS to all their customers. So
anyone can run similar analytics by using a standard toolset
(we used MySQL as a database and R as a analytical and
visualization tool).
4.1. Cost Analysis
Table 1 shows all costs per group. In total the Lübeck
University of Applied Sciences had to spend 847.01$ in
providing (a virtual) unlimited amount of server instances to
49 students organized in 9 groups for a timeframe of 13
calendar weeks. This sounds impressive but says in fact
nothing about how cost efficient the virtual lab approach was.
Could have been reached the same result with a classical
dedicated approach?
Let us have a look at the cost analysis provided in figure 2.
Figure 2(A) shows the costs per week and indicates that most
of the costs were generated 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 server uptime – that means running
servers and being billed for per hour (server hours). Almost
1/3 of the costs were generated by data storage – that means
all costs which have to do with the provision of server hard
drives, backups or other data storage services. Other costs
like network (requesting IP addresses, DNS names, etc.) or
even data transfer had no relevant impact to the total costs.
So the main cost driver was server uptime, the second
relevant one was data storage.
Table 1. Group Overview
Group
Students
Project
Costs in $
A1
5
WRSC Website
88.39$
A2
6
WRSC Website
265.37$
A3
4
WRSC Website
88.14$
A4
6
WRSC Website
162.88$
B1
6
Sailbot Tracking
41.17$
B2
6
Sailbot Tracking
57.58$
B3
6
Sailbot Tracking
57.46$
B4
5
Sailbot Tracking
37.42$
B5
5
Sailbot Tracking
48.58$
Figure 2(C) show that analysed groups produced quite
different costs. The expected value of cost responsibility
would be 100%/9 ≈ 11.11%. Nevertheless the most cost
efficient group (B 4) was only responsible for about 4% and
the mos t cost ”lavish” group (A 2) was responsible for about
31% of the total costs. It turned out that the A groups
(conference website) consumed more resources than the B
groups (sailbot tracking groups). This is not so surprising:
Different problems result in different costs! This is well
accepted in cloud economics literature. Cloud generated
costs are use case specific. Different problems result in
different architectural solutions generating different cost
behaviours[14],[15]. In other words cost (dis-)advantages
are course or even task specific and therefore have to be
figured out for each course.
More surprising: It seems to turn out that groups with
better grades produce significantly less costs. Nevertheless
we did not have collected enough data to harden this
correlation.
4.2. Usage Analysis
Figure 3(A) shows the peak and average server usage per
calendar week measured within the analysed timeframe
(calendar week 13 - 25). Figure 3(B) shows the average to
peak ratio (atp) per calendar week. The atp is good indicator
to measure how cloud compatible a solution is[12]. An atp
ratio near 1.0 indicates non peaky usage characteristics
which advantages classical dedicated approaches. An atp
ratio near 0.0 indicates very peaky usage characteristics and
therefore an appropriate cloud computing use case (you may
want to step back to section 3 to figure this out).
242 Nane Kratzke: Virtual Labs in Higher Education of Computer Science Why they are valuable?
How to realize? How much will it cost?
During the initial training phase (calendar week 13 - 15)
the usage characteristic shows an extremely high maximum
server usage but an astonishing low average server usage.
This characterizes an extreme peak load situation and results
in extreme low atp ratios (see figure 3(B)). According to
equation 4 or the definition of cMAX this shows a very ideal
cloud computing (peaky) situation. So training phases
seems to be very economical interesting cloud computing
use cases which is also postulated but not proven by
Barr[10].
Figure 2. Cost Analysis
Figure 3. Usage Analysis
During the project phase (calendar week 16 - 23) the usage
characteristic shows dramatically reduced maximum as well
as average box usages. Nevertheless the atp ratios (see figure
3(C)) stay in a very comfortable zone for cloud computing
approaches. So we still have a peaky usage situation but on a
significantly lower level. So also development phases seem
to be very economical interesting cloud computing use cases
being also postulated not also not proven by Barr[10].
The 24x7 phase (calendar week 21 - 24) shows raised
maximum as well as average box usages (see figure 3(A)).
Also the atp ratios are higher – nevertheless the peaky usage
characteristic remains but less distinctive.
The migration phase (calendar week 25) was characterized
by transferring the best solutions for the website and sailbot
tracking service into the operational environment. During
this phase the systems run in a constant load scenario as can
be seen in figure 3(A) (almost the same average bo x as well
as maximum usage) and in figure 3(B) (an atp ratio near 1.0).
This shows an extreme uncomfortable situation for cloud
computing economics – so constant loads seem to be no
economical interesting virtual lab use cases.
4.3. Economical Decision Analysis
As we have seen in sections 4.1 and 4.2 we can identify
different phases being more cloud compatible than others
from an economical point of view. Training and
Education 2012, 2(7): 239-246 243
development phases show very low atp ratios (see figure
3(B)) and therefore indicate peaky usage characteristics of
resources which advantages cloud computing. Other phases
with less peaky usage characteristics (like our 24x7 or
migration phase) disadvantage cloud computing approaches.
So we have identified pro and cons for a cloud based
realization of educational labs. How to decide? Now we are
going to apply our decision model presented in section 3.
Step 1: Determine the atp Ratio
Our analysis timeframe covered the calendar weeks 13 -
25. So an intuitive timeframe for average building would be
13 weeks – but this implicates a continual usage of an
educational lab over a complete year (very uncommon).
University or college educational labs are typically used one
time per semester. Therefore an educational lab can be used
only one time per semester (that means average building
over 26 weeks) or even only one time per year (that means
average building over 52 weeks). In our case 7612 hours of
server usage were generated. So the average amount of
servers to provide 7612 processing hours within a 26 or 52
weeks timeframe are:
avg26w = 7612 h / (26 7 24 h) 1.74
avg52w = 7612 h / (52 7 24 h) 0.87
Now we can calculate our average to peak ratio.
Maximum server usage was 49 servers per hour (see figure
3(A)). So we get the following atp ratios for a 26 or 52 week
timeframe. atp26w = 1.74 / 49 0.035
atp52w = 0.87 / 49 0.018
Step 2: Determine dedicated costs
First of all we have to find out how much would cost us a
dedicated server. Let us assume for demonstration reasons
that we could purchase an appropriate server for 500$.
Equation 3 tells us to calculate our dedicated costs per hour
in the following way for a three year regeneration interval:
d3year(500$) = 500$ / (3 365 24 h) 0.019 $/h
Step 3: Determine maximal cloud costs
Furthermore equation 5 tells us to calculate our cMAX costs
in the following way:
cMAX(26w) = d3year
(500$) / atp26w = 0.019 $/h / 0.035 0.54 $/h
cMAX(52w) = d3year
(500$) / atp52w = 0.019 $/h / 0.018 1.06 $/h
In other words: A cloud service provider (regarding a 3
year amortization time frame for servers) could be
28.57 times more expensive in case of a one time per
semester usable educational lab (1 / atp26w) or even
55.56 times more expensive in case of a only one time
per year usable educational lab (1 / atp52w) then own
dedicated costs.
Step 4: Determine appropriate cloud resources
Now we know our maximal cloud costs and have to look if
a cloud service provider can deliver appropriate resources. In
our case this is Amazon Web Services, but it could be any
other IaaS cloud service provider as well. We do this
exemplarily for a 26 week timeframe. But it works
absolutely the same for all other timeframes or IaaS cloud
service providers.
Table 2 shows all instance types of AWS and their
allocated costs. Section 4.3 (step 3) told us, that all server
instance types cheaper than 0.54 $/h result into cloud based
solutions being more economical than dedicated approaches.
As you can see in table 2, AWS provides several instance
types in the US West Region being economical in the sense
of section 3 and equations 4 and 5. The most appropriate
instance types for the analysed course would be the Micro or
Small (Standard) instance types. Both provide a significant
cost advantage. But it would also be possible to realize
exercises using XL (High Memory) server instances. Even
server instances of this type would be economical reasonable
for a cloud based approach.
So in our analysed use case a virtual IT lab for education is
much more economical than a dedicated approach.
Table 2. AWS Instance Types and Pricings, according t o AWS pricing
information on 23th Apr. 2012, US West Region, On-Demand Instances,
Linux/UNIX Operating System
AWS Instance Type
ECU
Memory
Price/h
Economical
Micro
< 1
613 MB
$0,025 / h
Yes
Small (Standard)
1
1,7 GB
$0,090 / h
Yes
Medium (Standard)
2
3,75 GB
$0,180 / h
Yes
Large (Standard)
4
7,5 GB
$0,360 / h
Yes
XL (Standard)
8
15 GB
$0,072 / h
No
XL (High Memory)
6,5
17,1 GB
$0,506 / h
Yes
2 XL (High Memory)
13
34,2 GB
$1,012 / h
No
4 XL (High Memory)
26
68,4 GB
$2,024 / h
No
Medium (Hi-CPU)
5
1,7 GB
$0,186 / h
Yes
XL (Hi-CPU)
20
7 GB
$0,744 / h
Yes
5. Conclusions
In section 5.1 we derive some general findings from our
use case analysis useful to find interesting practical courses
in higher education of computer science. These courses are
likely to show similar cost characteristics compared to the
analysed use case. Section 5.2 will show that the here
presented approach is transferable to other lectures and
related practical courses as well. Therefore some general
advices are provided in section 5.3 for setting up cloud based
virtual labs accompanied by some pragmatic cost control
rules.
5.1. General Findings
Cloud computing economics are good for peak load
scenarios being common in higher education practical
courses. As equation 4 showed cloud computing becomes
more and more econo mical as the peak to average ratio
increases (or the atp decreases). This indicates that peaky as
well as seldom usage of educational labs argue for cloud
based virtual lab approaches, extreme continual usage of labs
argue against cloud based approaches from an economical
point of view. Nevertheless we always have to analyse the
244 Nane Kratzke: Virtual Labs in Higher Education of Computer Science Why they are valuable?
How to realize? How much will it cost?
individual characteristic of a practical course by measuring
its specific average to peak ratio.
Cloud generated costs are use case specific. Different
problems result in different realization architectures
generating different costs as well as usage characteristics. 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 specific atp ratio.
24x7 as well as constant load used cases 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 if possible. This will
save a lot of money.
One of the main cost driver is server uptime. Second
relevant cost driver is data storage. Data transfer seems to
have a minor impact to costs. This might be only valid for
our analysed use case and should be handled with care.
Please check out other publications (like Mazhelis et al.[11])
for co mmunication intensive use cases if you plan to use
communication/data transfer intensive use cases in your
practical courses.
5.2. Transferability of the Approach
One interesting question is whether it is 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 Lübeck
University of Applied Sciences were analysed. The
following lectures and related practical courses showed
potential being supported by virtual labs.
Industrial Networks and Databases
Information Technology
Web Technologies
Database Management (and Engineering)
Integrated Information Systems
Distributed Systems
Operating Systems (if Linux/UNIX based)
Regarding all analysed module descriptions of according
computer science related study programs it turned out that
8.33% to 23.10% of all practical courses are likely
candidates to be supported by virtual labs . Whether these
practical courses will show similar cost advantages is up for
ongoing research. Nevertheless it seems very likely
regarding made experiences. To identify virtual lab
compatible lectures and practical courses it is helpful to
know what the identified courses have in common. That is
what we found out so far:
a course requires databases
a course has distributed processing requirements
a course requires web technologies on servers
a course requires Linux/Unix based servers
a course requires parallel processing capabilities
a course requires large-scale data processing
Whenever there exist a practical course with one of the
mentioned educational requirements a virtual lab might be a
reasonable option.
5.3. Advices for setting up Virtual Labs
A lot can be learned about providing virtual labs to
students via cloud computing means. Some of our made
experiences are provided as advices for everyone planning
similar approaches (see table 3).
Table 3. Advices
Advice
Description
1
It is likely that Cloud Computing will be new for students.
So plan an init ial training phase with to get students into
touch with the cloud tooling of the service provide. You
could do this by providing very det ailed step by step
manuals. This seems to be an effective way.
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.
3
Cloud computing provides flexibility. Use it!
Let your students play within their virtual lab in presence
phases, at home, at university, where and when ever they
want.
4
Watch your costs!
We further found out that costs are mainly generated by
two or three main cost drivers. Everyone should concentrate
on controlling these few cost drivers by applying simple
rules. Table 4 provides some effective rules for computing
intensive use cases.
Table 4. Cost Control Rules for Virtual Lab Users
Rule
Description
1
Shut down all instances after finishing experiments.
2
Use the smallest possible instance types.
3
Delete all unnecessary and stopped instances.
4
Generated costs are considered for grading.
First cost driver is server uptime. Whenever a server is
running money flows. This has to be made very clear to
students by implementing Rule 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 different 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. And every
instance produces storage costs, cover this with Rule Nr. 3:
Delete all unnecessary instances. Some cloud providers
provide detailed access control s ettings. Use them to let no
group (account) instantiate more than a limited amount of
instances in parallel.
And to increase a general cost awareness it is good to
establish Rule Nr. 4: Generated costs are considered for
grading. If two groups show the same performance, the one
with less generated cost will get the better grade. So resource
Education 2012, 2(7): 239-246 245
consumption will not beco me key criteria for grading. But
our experiences show that the rules shown in table 4
discipline a lot and are practicable.
6. Summary
Not mentioned exp licitly so far. Cloud based virtual labs
are extremely scalable. The presented approach is working
with 10 students. It is also working with 100 or even 1000 or
more students and stays economical. Higher education
organizations only have to provide more virtual labs but do
not have to invest into dedicated hardware with a typical
three or five year or even longer financial commitment (e.g.
for buildings). That provides flexibility and options to
manage periods with a significant increase or decrease 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 pragmatic
model to decide for or against virtual labs from an
economical point of view. We applied this decision making
model (see section 3) in a concrete use case (see section 4)
and it turned out that cloud based educational labs can have a
more than 25 to 50 times cost advantage (see section 4.3)
compared to classical dedicated approaches. But be aware.
Cost advantages are course specific. There exist no general
cost advantage of cloud computing.
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 significant amount of practical
courses in computer science related study programs (see
section 5.2). Other study programs have not been analysed
but the presented virtual lab approach is not necessarily
bound to computer science study programs. Transferability
analysis showed that courses with requirements of providing
databases, distributed processing capabilit ies, parallel
processing or large-scale data processing are interes ting
candidates. These requirements might be also typical for
practical courses in engineering study programs. The
concept of a virtual lab can be applied frictionless to
classical as well as distance study programmes and it
provides a convenient (and often cost effective)
infrastructure for project as well as problem based learning
in computer science and other engineering related study
programmes. In our case a virtual lab can be provided for
approximately 18 USD per semester and student.
ACKNOWLEDGEMENTS
Thanks to Amazon Web Services for supporting our
ongoing research with several research as well as educational
grants. This paper is a streamlined version of a similar paper
submitted to CSEDU2012 conference being accepted as a
full paper. Thanks to all reviewers for their valuable
comments how to improve this paper. I appreciated all the
valuable discussions around this paper during the
CSEDU2012 conference – especially the idea to make it
easily accessible for everyone by using a free and online
journal specialized on education. Thanks to my students and
Michael Breuker for using cloud computing in practical
education. This contribution would not exist without their
engagement. Finally let me thank Alexander Schlaefer and
Uwe Krohn for organizing the World Robotic Sailing
Championship 2011 in Lübeck and their confidence in our
students.
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