=Paper=
{{Paper
|id=Vol-2879/paper06
|storemode=property
|title=Some experience in maintenance of an academic cloud
|pdfUrl=https://ceur-ws.org/Vol-2879/paper06.pdf
|volume=Vol-2879
|authors=Vasyl P. Oleksiuk,Olesia R. Oleksiuk,Oleg M. Spirin,Nadiia R. Balyk,Yaroslav P. Vasylenko
|dblpUrl=https://dblp.org/rec/conf/cte/OleksiukOSBV20
}}
==Some experience in maintenance of an academic cloud==
https://ceur-ws.org/Vol-2879/paper06.pdf
Some experience in maintenance of an academic cloud
Vasyl P. Oleksiuk1,4 , Olesia R. Oleksiuk2 , Oleg M. Spirin3,4 , Nadiia R. Balyk1 and
Yaroslav P. Vasylenko1
1
Ternopil Volodymyr Hnatiuk National Pedagogical University, 2 M. Kryvonosa Str., Ternopil, 46027, Ukraine
2
Ternopil Regional Municipal Institute of Postgraduate Education, 1 V. Hromnytskogo Str., Ternopil, 46027, Ukraine
3
University of Educational Management, 52-A Sichovykh Striltsiv Str., Kyiv, 04053, Ukraine
4
Institute of Information Technologies and Learning Tools of NAES of Ukraine, 9 M. Berlynskoho Str., Kyiv, 04060,
Ukraine
Abstract
The article is devoted to the systematization of experience in the deployment, maintenance and servicing
of the private academic cloud. The article contains model of the authors’ cloud infrastructure. It was
developed at Ternopil Volodymyr Hnatiuk National Pedagogical University (Ukraine) on the basis of the
Apache CloudStack platform. The authors identify the main tasks for maintaining a private academic
cloud. Here they are making changes to the cloud infrastructure; maintenance of virtual machines (VM)
to determine the performance and migration of VM instances; work with VMs; backup of all cloud
infrastructure. The analysis of productivity and providing students with computing resources is carried
out. The main types of VM used in training are given. The number and characteristics of VM that can be
served by a private academic cloud are calculated. Approaches and schemes for performing backup are
analysed. Some theoretical and practical experience of using cloud services to perform backup has been
studied. Several scripts have been developed for archiving the platform database and its repositories.
They allow you to upload backups to the Google Drive cloud service. The performance of these scripts
for the author’s deployment of private cloud infrastructure was evaluated.
Keywords
cloud computing, private academic cloud, Apache CloudStack, G Suite, Google Drive
1. The problem statement
Today, many universities are creating their own cloud-based learning environments (CBLE).
Although there is currently no single concept for CBLE, scientists understand it as similar
concepts [1, 2, 3, 4], in general, it can be understood as an IT system consisting of cloud
services and providing learning mobility, group collaboration of teachers and students to
achieve educational goals [5].
CTE 2020: 8th Workshop on Cloud Technologies in Education, December 18, 2020, Kryvyi Rih, Ukraine
" oleksyuk@fizmat.tnpu.edu.ua (V. P. Oleksiuk); o.oleksyuk@ippo.edu.te.ua (O. R. Oleksiuk);
oleg.spirin@gmail.com (O. M. Spirin); nadbal@fizmat.tnpu.edu.ua (N. R. Balyk); yava@fizmat.tnpu.edu.ua
(Y. P. Vasylenko)
~ https://www.fizmat.tnpu.edu.ua/chairs/inform/infoemployees/186-oleksyuk (V. P. Oleksiuk);
https://www.fizmat.tnpu.edu.ua/chairs/inform/infoemployees/137-nadbal (N. R. Balyk)
0000-0003-2206-8447 (V. P. Oleksiuk); 0000-0002-1454-0046 (O. R. Oleksiuk); 0000-0002-9594-6602 (O. M. Spirin);
0000-0002-3121-7005 (N. R. Balyk); 0000-0002-2520-4515 (Y. P. Vasylenko)
© 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
165
As the analysis of the literature shows, many university CBLE are usually deployed according
to the hybrid model [6, 7, 8]. One of the most important components in the structure of this
environment is the private academic cloud [9]. It is now deployed according to the most
productive IaaS service model. A hybrid cloud is a cost-effective way to solve the problem of
insufficient computing resources. The private academic cloud allows the university to meet the
peak demand of students and faculty through a combination of local infrastructure and one or
more public clouds [10].
Various commercial and free platforms are used in universities to build private academic
clouds [11, 12, 13, 14]. A productive method of deploying private academic clouds is to use
solutions from leading cloud vendors such as Google Inc., Microsoft, Amazon and others. Google
Inc. offers researchers, universities, faculties, faculty and students grants and loans for teaching
and research. In particular, leading European educational institutions can access Google Cloud
within the Internet2 project [15]. Unfortunately, these opportunities are not currently available
for our country (Ukraine). For example, Microsoft Educator Grant is a program designed
specifically to provide access to Microsoft Azure to college and university professors teaching
advanced courses. As part of this program, faculty teaching Azure in their curricula are awarded
subscriptions to support their course [16]. In general, these programs are very useful and
productive. However, they are usually provided on a temporary basis and therefore cannot
completely replace the cloud-based IT infrastructure of universities and colleges.
Among the free platforms for cloud infrastructure deployment, CloudStack, Openstack, Prox-
mox, and Eucalyptus are the most suitable. Each of them has its advantages and disadvantages.
There are many attempts to compare these platforms. The authors of articles comparing such
platforms state [16, 17]:
• OpenStack has large community, offers wide integration with storage, network and
compute technologies, but is too complex to deploy and configure;
• Eucalyptus the longest-standing open source project is banking on its very tight tech-
nical ties to Amazon Web Services (AWS). The platform is configurable, but not very
customizable;
• Proxmox is open source platform. It can provide easy way to deployment cloud infras-
tructure. But it is not very suitable as a platform for a private cloud in the CLBE;
• CloudStack has well rounted GUI, can provide an advanced cloud infrastructure, but it is
very GUI centric built on single Java core.
We have deployed private academic cloud based on the Apache CloudStack platform.
It contains a management server, 4 hosts, 4 primaries and 1 secondary storage. We decided to
use hypervisors instead of containers. This is because the former are more versatile. In addition,
the use of hypervisors is safer than containers. To save computing resources, we have installed
primary storage on the hosts. We used VLANs to distribute traffic across individual networks.
These networks can be allocated to groups or individual students.
In general, our private academic cloud provides [17]:
• Development and Execution of student virtual machines;
• Aggregation of computing resources of several hosts;
• VM migration between repositories;
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• VM connections to each other through guest networks;
• Launching VMs within other VMs;
• Integration with Active Directory;
• Distribution of student accounts according to their academic groups.
There are many problems and corresponding tasks in the process private academic cloud
using.
The purpose of the article is to systematize author’s experience in maintenance the Cloud
Based Learning Environment.
The following tasks are required to achieve the goal of the research:
1. Analysis of maintenance tasks of academic clouds in foreign and Ukrainian universities.
2. Definition of maintenance tasks of academic cloud which has deployed by authors.
3. Listing and systematization of author’s experience in maintenance of academic cloud of
Ternopil Volodymyr Hnatiuk National Pedagogical University.
2. The private clouds maintenance tasks
As the experience of cloud infrastructure maintenance shows, this is an ongoing process. It
requires constant attention from engineers, network system administrators, teachers, and
student involvement. Scientists describe the experience of deploying an academic private cloud,
including determining the performance of hypervisors and storage [18]. The biggest challenge
for researchers was the transition from a prototype of an academic cloud to a productive one. In
this context, they addressed the problem of load balancing, elastic hypervisors, security threats.
Storage backup tasks are also important for such clouds.
The authors of the book “Data Backups and Cloud Computing” offer the concept of backup
to cloud storage. They say that both the cloud provider and the cloud consumers have to take
comprehensive steps to ensure appropriate configurations, hardening of the CBLE, appropriate
design and development, appropriate interoperability, and adequate testing [19].
Scientists from the Institute of Physics and Mechanics of the National Academy of Sciences
of Ukraine and Lviv Polytechnic National University have developed an effective method
of deduplication and distribution of data in cloud storage during the creation of backups.
Researchers have developed an intelligent system for such deduplication and tested it [20].
Junfeng Tian, Zilong Wang and Zhen Li also studied cloud data backup [21]. The authors
propose a scheme for data separation and backup and encryption. They state that their own
scheme resolves the conflict between data security and the survivability of the IT infrastructure
with the help of encrypted backup.
The Apache CloudStack cloud infrastructure redundancy model developed by Paul Angus is
very useful for our study. It creates a vendor agnostic API and UI in CloudStack for end users.
The author’s Framework abstracts the specifics of solutions, such that through the use of a
plugin, a 3rd party solution can deliver backup and recovery solutions [22].
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3. Definition of maintenance tasks of authors’ academic cloud
Here are the main tasks for servicing our sample private academic cloud:
1. Work with student accounts;
2. Making changes to the cloud infrastructure;
3. Creating VM templates;
4. VM service (system, student, teacher);
5. Determining the performance of individual hosts and the cloud as a whole;
6. Migration of VM specimens;
7. Stopping and restarting physical hosts;
8. Cloud infrastructure backup.
The first task involves creating student and faculty accounts. We authenticate users of the
academic cloud from a centralized database – LDAP-directory (Microsoft Active Directory). This
approach makes it possible to use single registration data to access all hybrid IT infrastructure
services. We used CloudStack domains to distribute students according to academic groups.
Adding users to them is possible in automatic (using links at the first successful authentication)
and manual mode. Unfortunately, due to the incompatibility of our users’ logins with the
Apache CloudStack platform, we had to choose the manual mode. To reduce the technical
work involved in finding LDAP directory entries, we have created several queries to filter user
account data.
Maintenance of our developed cloud infrastructure involves the implementation of tasks such
as:
• changing the parameters of the components of the cloud infrastructure – zones, clusters,
hosts, storages;
• creating and routing of virtual networks for individual groups or students;
• creating and modifying templates of compute offering services that determine the perfor-
mance of VM;
• creating and modifying network offering service templates such as VPN, DHCP, DNS
servers, Firewall, Load Balancer and others;
• creating projects for VM sharing by students.
When creating the service offering templates, we compared the characteristics of the hardware
hosts (CPU frequency, RAM) with the minimum guest OS requirements and the number of
students. To do this, we used the inequality:
𝐹 𝑅𝑄 = 𝑁𝑠𝑡 * 𝐹𝑂𝑆 < 𝐹 𝑅𝑄ℎ𝑜𝑠𝑡𝑠 ,
where 𝐹 𝑅𝑄 – the total frequency of VMs processors; 𝑁𝑠𝑡 – amount of students; 𝐹𝑂𝑆 – the
minimum frequency is recommended for the guest OS; 𝐹 𝑅𝑄ℎ𝑜𝑠𝑡𝑠 – total frequency of hardware
host processors. The last value can be found from the ratio:
𝑛
∑︁
𝐹 𝑅𝑄ℎ𝑜𝑠𝑡𝑠 = (𝑁𝑐𝑖 𝐹𝑐𝑖 ),
𝑖=1
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where 𝑁𝑐𝑖 – the number of cores in the processor of the i-th host, 𝐹𝑐𝑖 – CPU frequency of the
i-th host.
It is well known that the frequency of a modern processor is not constant. It can increase or
decrease depending on the mode of operation of the CPU. That’s why we use Processor Base
Frequency in the tables and formulas above. Processor Base Frequency describes the rate at
which the processor’s transistors are open and close. This processor frequency is measured by
each hypervisor in the Apache CloudStack platform.
Similarly, to determine the required amount of memory we used the inequality:
𝑀 𝐸𝑀 = 𝑁𝑠𝑡 * 𝑀 𝐸𝑀𝑂𝑆 < 𝑀 𝐸𝑀ℎ𝑜𝑠𝑡𝑠
As table 1 shows, the private academic cloud has a total frequency of about 50GHz. And the
total amount of memory is about 90 Gb. Regarding the frequency, two other opposite factors
should be taken into account:
• Table 1 shows the base frequency, and processors can run faster thanks to Turbo-Boost
technology;
• Hosts run other software (OS, databases, management servers, hypervisors). It also
consumes resources.
Table 1
Main Characteristics of Academic Cloud’s Hardware
𝑁𝑐𝑖 𝐹𝑐𝑖 𝑀 𝐸𝑀𝑐𝑖 𝐹 𝑅𝑄ℎ𝑜𝑠𝑡𝑠
Host0 4 3200 16384 12800
Host1 4 3100 24576 12400
Host2 4 3100 16384 12400
Host3 4 3700 32768 14800
Sum 90112 52400
Comparing the data in table 2 and table 1, we can conclude that our academic cloud provides
about 50 VM with Linux without a graphical user interface (GUI), more than 40 VM with
Windows Workstation, about 35 VM with Windows Server and OC Linux with GUI.
Table 2
Basic calculations of our academic cloud’s performance
𝑂𝑆 𝐹𝑂𝑆 𝑀 𝐸𝑀𝑂𝑆 𝑁𝑠𝑡 𝐹 𝑅𝑄 𝑀 𝐸𝑀
OSLinuxNoGUI 500 500 20 10000 10000
OSLinuxGUI 1500 2000 20 30000 40000
OSWindowsWs 1000 2000 20 20000 40000
OSWindowsSrv 1500 2000 20 30000 40000
OSAdvLinux 2500 4000 20 50000 80000
We use the EVE-NG platform for modelling in the study of computer networks. It launches
its own VMs inside the main Apache CloudStack VM [23]. Such nested virtualization requires
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more resources. Therefore, Table 1 has a row named OSAdvLinux. For this OS, our cloud can
run about 20 instances. We have created in our cloud infrastructure some compute offering
templates based on the data provided.
Regarding VM migration, we used the approach described in [21]. Its authors propose to
evaluate the efficiency of the cloud infrastructure as an integrated indicator of the use of
resources of each instance of the VM. The authors indicate that a specific instance needs to
be migrated to another host to resolve the issue. They propose the concept of non-uniformity,
which is determined by the ratio:
⎯
⎸ 𝑛 (︂
⎸∑︁ 𝑟𝑖 − ¯𝑟 2
)︂
𝑝
𝑁𝑅 = ⎷ ,
¯𝑟
𝑖=1
where 𝑛 is the number of resources, 𝑟𝑖 is the projected use of the 𝑖-th resource, ¯𝑟 is the average
predicted value of the use of all resources of the 𝑝-th server. Academic cloud administrators
should minimize value 𝑁𝑅𝑝 .
To define an overloaded host, the concept of “hot spot” is used. The host will be “hot” if
at least one of its resources exceeds the limit value (“temperature”). To determine the host’s
“temperature” the amount of use of all its resources.
𝑛
∑︁
𝑡* = (𝑟𝑖 − 𝑟𝑡 )2 ,
𝑡∈𝑅
If the value of 𝑡 is greater than zero, then virtual machines should be migrated from the
*
appropriate host. Apache CloudStack system implements the appropriate functionality. Root or
domain administrator can transfer both disks of virtual machines and run them on another host.
Another way to solve the problem of lack of computing resources is CPU and RAM overcom-
mit. In this case, the Apache CloudStack system administrator sets the multiplier. This number
is multiplied by the total CPU frequency or amount of RAM. However, this method should not
be abused. This can lead to unpredictable consequences, such as denial of service to virtual
machines.
4. Designing and realization an academic clouds’ backup model
Experience shows that the task of backup is very important and time consuming. This is
primarily due to the large amounts of student VMs data in the private academic cloud. Large
companies develop a disaster recovery plan in this case. Large companies are developing
a disaster recovery plan in this case. But in educational institutions, IT services work to
perform such tasks. Therefore, they need to develop a model, identify potential risks in the IT
infrastructure, consider and implement an appropriate backup system.
The development of a backup strategy requires the definition of the main goals and objectives
of the backup, tools and regulations. In general, the problem of back-up is relevant for almost all
IT infrastructures. When choosing a backup method, the following criteria are important [24].
The development of a backup strategy requires the definition of the main goals and objectives
of the backup, tools and regulations. In general, the problem of backup is relevant for almost all
IT infrastructures. When choosing a backup method, the following criteria are important:
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• backup time to the storage;
• recovery time from backup;
• the number of copies that can be stored;
• risks due to inconsistency of backups, imperfection of the backup method, complete or
partial loss of backups;
• overhead costs: the level of load on the servers when performing copying, reducing the
speed of service response, etc;
• the cost of renting all services and storage.
Currently, there are 3 main backup schemes such as:
• Full. This type of backup creates a complete copy of all data.
• Incremental. In this case, only files that have changed since the previous backup are
copied. The following incremental backup only adds files that have been modified since
the previous backup.
• Differential. The backup program copies each file that has changed since the last full
backup. Differential copying speeds up the recovery process.
To save material costs, we use almost no server equipment and powerful and high-speed
network storage in our academic cloud installation. Instead, we decided to use cloud services.
For example, the Google Drive service within the G Suite for education package offers virtually
unlimited disk space [25]. The disadvantage of such a repository is the significant time to upload
or download backups. This speed will be limited by the bandwidth of the university’s Internet
channel. The latter requirement can be considered acceptable, as our implementation of the
academic cloud is used primarily for training rather than for production.
To use Google Drive in our own scripts, we need to use the API of this service. This interface
is accessible through Google Developers Console, a software developer service. First you need
to create your own project. Credentials were created to access this project. We have chosen
to access OAuth 2.0 accounts. OAuth is an open authorization standard that allows a user or
application to give and access data without having to enter a login and password. Access tokens
are used for this purpose. Each access token provides access to a specific client to specific
resources and for a specified period of time [25]. After adding a new project, we created new
data for authentication, selected the type of application (desktop) and activated the appropriate
API (Google Drive API).
Our research was performed at the Joint Laboratory of the Institute of Information Tech-
nologies and Learning Tools of the National Academy of Educational Sciences of Ukraine, and
Ternopil Volodymyr Hnatiuk National Pedagogical University.
Our academic cloud deployment contains the following objects:
• One management server;
• Four hosts for running VMs instances;
• Four primary repositories containing disks of these VMs;
• One secondary repository for saving templates and ISO images.
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Because templates and ISO images do not change, but only new ones are added, we chose the
incremental method to back up the secondary storage. Its implementation was based on the
use of a ready-made utility for synchronizing storage files. Unfortunately, there is currently
almost no such high-quality utility like Google back-up and Sync, which is developed for OC
Windows. We analysed several tools such as:
• Gdrive (grive2). Google Drive client with the support for the new Drive REST API and
partial sync. It can’t provide continuously wait for changes in file system or in Google
Drive to occur and upload.
• Gnome-online-accounts. It is system utility located within system’s settings in Gnome
GUI. But it can only be executed in a graphical interface.
• GoSync is a Google Drive client with GUI support for Linux. It is designed under the GNU
General Public License. The client is not perfect enough, for example, it has automatic
regular synchronization every 10 minutes.
• Google-drive-ocamlfuse is a FUSE (Filesystem in Userspace) filesystem for Google Drive,
written in OCaml. FUSE is a free module for the kernel of Unix-like operating systems. It
allows developers to create new types of file systems available for users to mount without
the root privileges of Google Drive on Linux.
We used the latest utility. Here are its main features [26]:
• full read/write access to ordinary files and folders;
• read-only access to google docs, sheets, and slides;
• multiple account support;
• duplicate file handling;
• access to trash;
• storing Unix permissions and ownership;
• support symbolic links;
• streaming through read-ahead buffers.
Some problem was that the utility requires authorization using a browser in a graphical
interface. Therefore, we used an alternative authorization mode. Since we already had our own
OAuth2 client ID and client secret, we specified them in the command:
google-drive-ocamlfuse -id 12345678.apps.googleusercontent.com -secret abcde12345
As the command tries to start the browser on the server where there is no GUI we formed the
necessary URL as it is written in the documentation on Google Developers Console. After going
to this address, we received a verification code. This code gave access to folder synchronization
to the Google Drive.
For security reasons, we decided to sync not the secondary storage itself, but a copy of it
from the backup drive (Backup_Secondary task, see figure 1). So, we first synchronized local
folders with the command:
rsync -azvh /export/secondary /export/sync_secondary/arch_cloud
where /export/secondary – the secondary storage of Apache CloudStack infrastructure;
/export/sync_secondary/arch_cloud – the local copy of this storage.
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Figure 1: Academic cloud infrastructure backup scheme.
To synchronize the /export/sync_secondary/arch_cloud folder, the following command has
been added to the server task scheduler: google-drive-ocamlfuse /export/sync_secondary
It runs every time a server with secondary storage is loaded.
A backup of all databases is required to restore the Apache CloudStack cloud infrastructure.
These are such databases:
• Cloud. It contains all objects of cloud infrastructure.
• Cloud_usage. A database that contains generalized data on resource consumption by the
end user. It is used to obtain statistics and compile reports.
Since the backup of these databases is quite small, we decided to store all backups in the
cloud storage (Backup_Database task, see figure 1). The traditional database for the Apache
CloudStack platform is MySQL. The main utility for backing up MySQL databases is mysqldump.
Its syntax involves entering a login name and password. Because the shell script in Linux is
written as a plain-text file, it will contain the name of the user’s password (usually the root)
of the database. This is a potential security risk for the entire server. In order not to leave
open the data for authorization of the database user, we used the “login path” option. A “login
path” is an option group containing options that specify which MySQL server to connect to
and which ac-count to authenticate as. To create or modify a login path file, we have used the
mysql_config_editor utility. In general, the commands for creating and archiving a database
dump are as follows:
/usr/bin/mysqldump –login-path=DailyBackup -u root -A >
$BACKUP_DIR/"archive_cloud_all_""$date_daily"".sql"
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tar -czf $BACKUP_DIR/"archive_cloud_all_""$date_daily"".sql.tgz"
$BACKUP_DIR/"archive_cloud_all_""$date_daily"".sql"
The variable $date_daily contains the current date of the archive. This allows you to see the
date of archiving directly in the file name.
To upload the files to the server, we used a ready-made script from github [27]. Here is its
launch:
upload.sh "arch_cloud/DB" "$entry" $upl_file folder_ID "application/x-gzip"
where
• arch_cloud/DB – folder for uploading files;
• $entry – full path to the file;
• $upl_file – file name to download;
• folder_ID – Google Drive folder ID;
• application/x-gzip – file MIME-type.
A special refresh_token token is required to provide long-term access of the up-load.sh script
to Google Drive. It can be obtained by curl-calling a URL such as:
curl --silent "https://accounts.google.com/o/oauth2/token" --data "code=&
client_id=&client_secret=&redirect_uri=urn:ietf:wg:oauth:2.0:oob&
grant_type=authorization_code"
In general, the scheme of backuping of cloud infrastructure is shown in figure 1.
Performing backup of primary repositories (Backup_Primary task (250,251,252,253)) has some
difficulties. An analysis of Internet sources, management server databases, and storages files
showed that the Apache CloudStack platform does not typically use full copies of disk templates
for each VM. This means that full backups should be made to reduce the risk of inconsistencies
in primary repository archives.
Additionally, it would be good to prepare a cloud platform, stopping all VMs. Of course,
students need to form an understanding of the need to turn off their own VM. However, in
practice this is not always possible. Therefore, it is necessary to stop all VM programmatically,
by means of a script. This can be done using the API features of the Apache CloudStack platform.
Using API functions allows the developer to access data about cloud infrastructure objects. It
is also possible to change the state of these objects. To generate a query that contains API
functions, you must specify:
• URL of the management server;
• Service construct “api?”. It contains the path to a certain API-function, and indicates the
beginning of the parameters that are transmitted using the GET method.
• Command. It is the name of the API-function.
• ApiKey. The key, that can be generated for each user account.
• Additional query options separated like GET queries using the “&” character.
• Response format (JSON or XML).
• Signature of the request.
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Regardless of the protocol (HTTP or HTTPS) used to access the Apache Cloud-Stack API
functions, the request must be signed. This allows the platform to confirm that the request
was sent from a trusted accounting request that has the authority to execute the appropriate
command. To sign a request, the developer must have an API key and an account secret key.
They are generated by the platform administrator [28].
Here is our bash-script to stop all working users’ VMs.
mysql --login-path=DailyBackup -D cloud -e "SELECT uuid FROM vm_instance WHERE type =
\"User\" and state = \"running\";" > uuid.txt
sed -i ’1d’ uuid.txt
while read LINE; do php -q cloudstackapi.php "$LINE" ; done < uuid.txt
In the first line we receive in a file from a database the list of user VM with a running state.
The next command clears the first line because it does not contain a VM. The third line runs
the cloudstackapi.php script. It generates a signature and calls the stopVirtualMachine API.
Another way to back up the current state of the VM is to create their snapshots. The Apache
CloudStack platform provides 2 types of images [22]:
• VM Snapshot – a hypervisor-driven point-in-time image of a virtual machine’s disks. The
exact mechanism of this is dependent on the hypervisor.
• Volume snapshot – a point-in-time image of a specific volume. The process usually
involves taking a VM snapshot and then copying the required volume to secondary
storage and the deleting the VM snapshot.
This approach requires additional space on the secondary storage or data coping on the
user’s local disk. Such images can be taken by students from the web interface of the Apache
CloudStack platform. Performing this action and turning off their own VMs after the end of
their use are important components of ICT competence of the student.
However, experience shows that not all students perform these actions. Therefore, these are
also worth automating with scripts. Among the API functions of the Cloud-Stack platform are
relevant [29].
Another task of backing up our academic cloud is to estimate the time required to upload
data to the cloud storage. Currently (October 2020) the sizes of our academic cloud storage is
approximately as follows:
• primary250 – 120 Gb;
• primary251 – 80 Gb;
• primary252 – 140 Gb;
• primary253 – 80 Gb;
• secondary – 100 Gb.
Since we make a full copy of the primary storage, we need to download about 400 GB to the
cloud storage each time. Let the speed of the Internet channel at night be 80 Mbps (10 Mbytes
per second). Then it will take 11 hours to download 400*1024 MB. That’s a lot. Therefore, we
balanced Internet access through 2 providers. At the time of backup, our router routes hosts
cloud0 and cloud1 through the first provider, and cloud2 and cloud3 through the second. In
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this case, a full backup takes about 5 hours and 30 minutes. This time is also significant, but is
acceptable.
Another disadvantage of our scheme is the significant time required to download backups
from the Google Drive service. However, this time will be significant if the management or
storage servers fail. This means that we must back up the entire OS of the management server
to fast local area network storage.
5. Conclusions
The private academic clouds should be used in cloud based learning environment, as they are
necessary for education of future ICT specialists. Virtualization is one of the most up-to-date and
advanced technologies for modelling many ICT objects. Despite the availability of educational
grants from leading cloud vendors, many universities are deploying their own private academic
clouds. During the production phase, administrators have a lot of work to do to maintain and
support these academic clouds. Among these tasks, one of the most important is to ensure the
productivity and elasticity of the cloud. Solving them will allow them to load the maximum
number of VMs in the cloud infrastructure.
An important task in the maintenance of the academic private cloud is the backup of its
components. To solve it effectively, you need to use different backup schemes such as full,
incremental, differential. To save data, it is advisable to use both cloud and local storage. In
any case, administrators should determine how long it will take to build and restore the entire
cloud infrastructure. It is also advisable to use the API functions of the cloud platform. This
will automate some maintenance tasks.
We see the prospects for our further research of our installation of a private academic cloud
in the development of more efficient scripts based on a differential circuit. They should reduce
the time it takes to create and copy all backups. According-ly, the time to recover data from
it will be reduced. Also relevant the study of new versions of cloud platforms regarding the
emergence of ready-made modules for backup. Probably, they will allow to solve many current
problems.
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