Investigating the Performance of Moodle Database Queries

in Cloud Environments

Karina Wiechork

1,2 a

and Andrea Schwertner Char

2 b

Information Technology Coordination, Federal Institute of Education Science and Technology Farroupilha,

Frederico Westphalen, Brazil

Department of Languages and Computer Systems, Federal University of Santa Maria, Santa Maria, Brazil

Keywords:

Cloud Computing, Benchmarks, Database, Performance.

Abstract:

Several computing services are being migrated to cloud environments, where resources are available on de-

mand and billing is based on usage. Databases in the cloud are increasingly popular, however their perfor-

mance is a key indicator that must be known before deciding to migrate a system to a cloud environment. In

this article, we present a preliminary investigation of the performance of database queries in Moodle, a pop-

ular Learning Management System, installed on cloud environments from Amazon Web Services and Google

Cloud Platform. Experiments and performance analysis were based on benchmarks Pgbench, Sysbench and a

Moodle Benchmark Plugin. We collected data and compare it with the results obtained on a local computer.

In the conﬁgurations we tested, the results show that the Moodle database at Amazon’s provider performed

better than Google’s. We made our data and scripts available to favour reproductibility, so to support decision

makers on the migration of a Moodle instance to a cloud service provider.

1 INTRODUCTION

Cloud computing is not only changing the way we

use software, but also the way we build it. As

more and more services migrate to the cloud, tradi-

tional components in software architecture may be

provided by cloud-based services. Since the early

days of cloud computing, the range of services has

grown and spanned from personal to corporate ap-

plications. Examples of cloud services include word

processors, spreadsheets, database managers and a lot

more (Miller, 2008).

The use of cloud computing platforms is advanta-

geous for a number of factors, among then: scalabil-

ity, which is a prime factor for distributed and high-

performance applications, the ease of conﬁguring the

instances for running applications, and the elimina-

tion of the initial cost to acquire and operate the re-

quired infrastructure.

Data management is a very important factor

within the context of organizations that keep all their

data in computerized environments. Data security,

scalability, and performance are all aspects that need

https://orcid.org/0000-0003-2427-1385

https://orcid.org/0000-0003-3695-8547

to be guaranteed in cloud computing environments.

There are many providers offering cloud-based

data management, including traditional, relational

database management systems (RDBMS). Many or-

ganizations rely on software built around traditional

RDBMS, as for example educational institutions and

their Learning Management Systems. Migrating such

systems to a cloud environment may be advantageous,

but is not a trivial decision.

In this article, we present a preliminary investi-

gation of the performance of database queries over

Moodle (Modular Object-Oriented Dynamic Learn-

ing Environment), a popular Learning Management

System (LMS), in cloud environments from distinct

providers. This open-source LMS is actively sup-

ported and is widely used by many educational in-

stitutions all over the world. Despite its popularity,

there is a lack of studies focusing on its performance

on cloud environments.

We decided to focus on the Moodle database be-

cause it is a major component within this LMS and

it depends on a third-party RDBMS (PostgreSQL).

We chose two major cloud storage providers, Ama-

zon Web Services (AWS) and Google Cloud Platform

(GCP), which offer a wide range of pricing plans, in-

cluding free cloud tiers. Performance was assessed

Wiechork, K. and Charão, A.

Investigating the Performance of Moodle Database Queries in Cloud Environments.

DOI: 10.5220/0009792202690275

In Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020) - Volume 1, pages 269-275

ISBN: 978-989-758-423-7

269

using different benchmark tools. The results and our

reproductible process can be useful to support deci-

sion makers on the migration of a Moodle instance

to a cloud service provider. The obtained results were

compared with compared with the experiments results

using the local installation of Moodle.

The rest of this paper is organized as follows.

Section 2 discusses some background topics and the

related works that comprised experiments involving

database performance. In Section 3, we present the

methodology and the environments we used. Sec-

tion 4 details the experiments and the results we ob-

tained. Lastly, section 5 concludes this paper with a

brief summary and suggestions for future research.

2 BACKGROUND AND RELATED

WORK

2.1 Moodle

Moodle is an e-learning platform based on free soft-

ware, it is known as LMS, as Virtual Learning En-

vironment (AVA) or Course Management System

(CMS). This platform offers mechanisms for sharing

digital content, as well as communication tools, cre-

ating courses, monitoring user actions that take place

during a course, and can be enriched with different

plugins, designed to meet the speciﬁc needs of a given

set of users. It is also possible, through this tool, to de-

velop evaluative activities, such as task delivery, ques-

tionnaires, among other actions in distance learning

activities.

Moodle is a social construction education frame-

work and can be run on any computer that has

a Database Management System compatible with

SQL (Structured Query Language). The Moodle 3.8

database includes about 422 tables. Also, it features

a database abstraction layer called XMLDB, that is,

Moodle’s working code is the same in Maria DB, MS

SQL Server, MySQL, Oracle and PostgreSQL. Based

on the documentation on the ofﬁcial Moodle website,

PostgreSQL is the preferred engine to host the Moo-

dle database with large tables (Moodle, 2016).

In addition to our work, other research has also

investigated the performance of the Moodle environ-

ment, but in other areas. In the work Caminero et.al

(Caminero et al., 2013), tests the performance of three

LMS programs: LRN (Learn, Research, Network),

Sakai and Moodle. This study was concerned with

collecting measurements of memory and CPU con-

sumption. In the research Guo et.al (Guo et al., 2014),

the performances were compared with Moodle in a

physical and virtualized environment in cloud com-

puting, where the authors used the Siege pressure test

tool.

2.2 Cloud Computing Models

Cloud computing is a convenient abstraction of virtu-

alized computing resources, including hardware and

software, that are accessible over a network. Cloud-

based solutions have changed the way individuals and

organizations deal with the computing resources to

meet their needs. Instead of investing on hardware

and software maintenance, they may rely on pay-as-

you-go, scalable, cloud-based alternatives (Jamsa,

2013). Given the wide range of cloud computing

options, different models and taxonomies have been

proposed. In this work, we have initially considered

three widely accepted cloud service models proposed

by the National Institute of Standards and Technology

(NIST): Infrastructure as a Service (IaaS), Platform

as a Service (PaaS) and Software as a Service (SaaS)

(Mell and Grance, 2011).

Infrastructure as a Service (IaaS). Delivers a set of

virtual machines with associated storage, pro-

cessors, network connectivity and other relevant

resources. Rather than purchasing all required

equipment, consumers lease such resources as

part of a fully outsourced service (Mahmood and

Hill, 2011). Examples of this service are: Ama-

zon Web Services, Google Cloud Platform, Mi-

crosoft Azure, among others.

Platform as a Service (PaaS). The capability pro-

vided to the consumer is to deploy onto the cloud

infrastructure consumer-created or acquired ap-

plications created using programming languages,

libraries, services, and tools supported by the

provider. The consumer does not manage or con-

trol the underlying cloud infrastructure (Mell and

Grance, 2011). PaaS also allows you to avoid

spending and the complexity of buying and man-

aging software licenses or development tools and

other resources.

Software as a Service (SaaS). The capability pro-

vided to the consumer is to use the provider’s ap-

plications running on a cloud infrastructure. The

consumer does not manage or control the un-

derlying cloud infrastructure including network,

servers, operating systems, storage, or even indi-

vidual application capabilities (Mell and Grance,

2011). This is the case, for example, of the pack-

age Microsoft Ofﬁce 365, Onedrive, Dropbox,

Google Drive, e-mail services, among others.

ICEIS 2020 - 22nd International Conference on Enterprise Information Systems

270

Both IaaS and SaaS models are well suited for mi-

grating Moodle to the cloud. Indeed, there are

some companies offering a Moodle environment in

a SaaS model, preconﬁgured and ready to use, such

as: AWS Marketplace (Amazon, 2013), Bitnami (Bit-

nami, 2020), MoodleCloud (Moodle, 2019), among

others. On the other end, the IaaS model offers more

autonomy over the server, so this is the approach we

adopted in this work. We have chosen the providers

from Amazon and Google, two prominent companies

which offer a wide range of pricing plans, including

free instances which are convenient for preliminary

experiments.

2.3 Cloud Storage Providers

Cloud storage provider is a contracted company that

provides cloud-based storage infrastructure, platform

or service. Companies usually pay only by amounts

of contracted service, depending on usage. As a re-

sult, users can rent computing nodes in large commer-

cial clusters through various providers such as: Ama-

zon Web Service, Google Cloud Platform, Microsoft

Azure, among others.

2.3.1 Amazon Web Services

AWS is a cloud services platform that offers various

services and features to help companies grow. Some

services offered by AWS include compute engine,

storage, database, blockchain, networking, DevOps,

ecommerce, high performance computing, internet of

things, machine learning, mobile services, serverless

computing and web hosting. Because of this large

number of services available, companies like: Spo-

tify, Airbnb, Shazam, Adobe, Siemens, among others,

have chosen AWS as a service provider.

The service used at AWS for the tests in this

work was the Elastic Compute Cloud (Amazon EC2).

EC2 is a service whereby you can create virtual ma-

chines and run them on one of Amazon’s data centers.

You can choose from a variety of machine conﬁgura-

tions that have different processing powers, memory

conﬁgurations, and virtual hard drive sizes (Amazon,

2020). The service also provides full access to com-

puting resources and allows environment settings to

be changed.

2.3.2 Google Cloud Platform

GCP consists of a set of physical assets, such as

computers and hard disk drives, and virtual re-

sources, such as virtual machines (VMs), which are

hosted in Google’s data centers around the globe

(Google, 2020b). Some services available in GCP are

compute engine, storage, migration, Kubernetes en-

gine, databases, networking, cloud spanner, developer

tools, management tools, API management, Internet

of things.

The service used in this work was Google Com-

pute Engine. Google Compute Engine delivers vir-

tual machines that run on Google’s data centers and

global ﬁber networks. Compute Engine tools and

workﬂow are compatible with cloud computing, of-

fering load balancing of scaling from individual in-

stances to global instances (Google, 2020a). Some

companies that use GCP services are: HSBC, Twitter,

PayPal, Latam Airlines, LG CNS, among others.

As for the cloud providers used in our article, it

can be said that the features and tools are similar in

both, the list of available services is long and contin-

ues to grow. In both providers there is the availability

of price calculator. By Providing details about desired

services, you can see a price estimate.

2.4 Benchmarking Databases on the

Cloud

Database performance evaluation is a recurrent sub-

ject in research works addressing new technological

advances. Several benchmark tools exist and cloud

computing brought even more possibilities to this sce-

nario. Before elaborating our experiments, we re-

viewed some related works concerning database per-

formance evaluation.

In their studies, Ahmed et al. (Ahmed et al., 2010)

and Sul et al. (Sul et al., 2018) present the differences

in performance analizing the network, CPU perfor-

mance, memory and query time of the database, ac-

cording to the Sysbench parameters. The research of

Kasae et al. (Kasae and Oguchi, 2013) was performed

in hybrid cloud environments, where they used the

Pgbench PostgreSQL benchmark tool for database

performance evaluation.

In their work, Guo et. al (Guo et al., 2014)

uses the Siege benchmark, which was designed to al-

low Web developers to evaluate the performance of

their code and how it behaves on the Internet. Liu

et al. (Liu et al., 2014) however, used the Cloud-

Bench benchmark. The analysis tool is a framework

that automates the evaluation and benchmarking on

cloud scale, through the execution of controlled ex-

periments where full-app workloads are deployed au-

tomatically.

Abadi (Abadi, 2009) discusses the limitations

and possibilities of deploying data management tech-

niques on emerging cloud computing platforms such

as Amazon Web Services. The author also presented

some features that a Cloud Data Management System

Investigating the Performance of Moodle Database Queries in Cloud Environments

271

must have when designed for large scale data storage.

To the best of our knowledge, there is a lack of

studies focused on the performance of queries over

the Moodle database. Though general benchmarks

may be useful to assess some metrics, it is also im-

portant to consider application-speciﬁc queries and

tables.

2.5 Benchmarks

In this work we used some tools to compare the per-

formance of the Moodle database in the cloud. The

chosen tools were open-source and unrestricted re-

garding publication of the obtained results. A descrip-

tion of each of the benchmarks follows.

2.5.1 Pgbench

Pgbench is a software made for the execution of

benchmark tests on PostgreSQL. It runs the same se-

quence of SQL commands over and over, possibly in

multiple concurrent database sessions, and calculates

the average transaction rate (transactions per second)

(PostgreSQL, 2020). Transaction is an execution unit

that a client application runs in a database. Pgbench

is a contribution from PostgreSQL, which performs

load tests and assists the analysis of the performance

of your Postgres database.

2.5.2 Sysbench

Is a scriptable multi-threaded benchmark tool based

on LuaJIT. It is most frequently used for database

benchmarks, but can also be used to create arbitrar-

ily complex workloads that do not involve a database

server (Kopytov, 2019).

The Sysbench software is designed to measure pa-

rameters for a system running a database under in-

tensive load. In addition to database performance, it

also allows you to test ﬁle I/O performance, scheduler

performance, memory allocation and transfer speed

and POSIX (Portable Operating System Interface)

threads implementation performance.

2.5.3 Moodle Benchmark Plugin

This plugin was developed by (Pannequin, 2016) to be

installed in Moodle, in order to perform benchmark

tests that report: server speed, processor speed, hard

disk speed, database speed and Moodle page loading

speed, these tests create temporary test ﬁles. After

installing the plugin in the Moodle environment, the

POSIX a family of open standards for operating sys-

tems.

execution is performed by the superuser through the

Moodle interface.

At the end of the run, a report is generated stat-

ing a score. This score is the total time in seconds

that represents the sum of the tests mentioned above.

The higher the value of the tests, the less efﬁcient it

is. However, the objective of this work is to evaluate

the performance of the Moodle database in the cloud,

with this, we only use the values of the tests in the

database.

3 METHODOLOGY AND

ENVIRONMENT

In this section, we present the methodology used to

execute our experiments with Pgbench, Sysbench and

the Moodle Benchmark Plugin. The settings used for

Amazon and Google instances will be displayed, in

addition to our physical computer.

3.1 Methodology

The methodology for performance analysis is de-

scribed in Database Benchmarking (Scalzo et al.,

2007), which recommends restoring the database to

its initial state before running a new test, reproducing

the tests again, making comparisons with results.

To select which benchmarks and performance

analysis tools would be used for the experiments, we

carried out a series of small tests on the instances of

providers. The intention was to verify the behavior of

these tools, their results and its conﬁguration.

Data was imported into Moodle database to be

similar to an institution’s database. We included reg-

istrations of 10 courses and 627 users. The imported

data was exactly the same for all instances.

Our settings for running the experiments were the

same. We carry out the experiments on different days

and times, to ensure that the values have not suffered

momentary interference from the instances, but al-

ways running the same experiments simultaneously

in the instances. In each experiment, we performed

ﬁve runs and calculated the arithmetic mean to obtain

a result.

3.2 Environment

For these experiments two instances were conﬁgured

in the cloud, one on Amazon EC2 and another on

Google Compute Engine, both for free with conﬁg-

urations showed at Table 1. Google does not make

clear the CPU model. Thus, a benchmark was used to

verify the performance of the CPU (Kopytov, 2019),

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272

RAM (Kopytov, 2019) and hard disk (SourceForge,

2018). The performance of the hard drive and the pro-

cessor in the instance of Google was superior in rela-

tion to Amazon, while in the instance of Amazon the

RAM executed more transactions per second (TPS).

We run all the benchmarks on our local computer

in order to compare Google and Amazon results with

a signiﬁcant baseline. On this physical computer

we had full control and made sure there was no

additional workload while running the benchmarks.

Thus, this scenario is what we consider as a baseline.

Table 1: Conﬁgurations of the VMs utilized in the experi-

ments.

Service AWS GCP Local

Zone South

America

South

America

Localhost

Processor Intel(R)

Xeon(R)

CPU E5-

2676 v3

(haswell)

Intel(R)

Xeon(R)

CPU E5

(broad-

well)

Intel(R)

Core(TM)

i5-8250U

Speed 2.40 GHz 2.20 GHz 1.60 GHz

Cores (per

socket)

1 1 4

Threads

(per

socket)

1 1 2

Hypervisor Xen KVM no

RAM 1 GB 1 GB 8GB

L1 data

cache

32 KB 32 KB 32KB

L1 instruc-

tion cache

32 KB 32 KB 32 KB

L2 cache 256 KB 256 KB 256 KB

L3 cache 30720

56320

6144 KB

Operation

System

Ubuntu

18.04

Ubuntu

18.04

Ubuntu

18.04

4 EXPERIMENTS AND RESULTS

In this section, we present the results obtained in our

experiments, executed with the benchmarks Pgbench,

Sysbench and the Moodle Benchmark plugin. The

results obtained were compared with the results of the

tests using the local installation of Moodle.

According to Scheuner et al. (Scheuner et al.,

2014), “unfortunately, cloud benchmarking services

are complicated and prone to errors”. Based on this,

we opted to perform the same tests with three bench-

marks in both instances, during random days and

times. The same test was executed ﬁve times in each

transaction / client, and the arithmetic mean of the

tests was calculated. In each new test, reset the tables

used by Pgbench and Sysbench, each one composing

the parameters in an empirical way.

The experiments were performed focusing on

the goal of measuring only the performance of the

database. All test scripts and documented commands

are available at: https://github.com/karinawie/PAD-

UFSM.

4.1 Results with Pgbench

Pgbench performs load tests on the database in-

volving ﬁve clauses for each executed transac-

tion, SELECT, UPDATE and INSERT, thus analyz-

ing the bank’s performance. Before running Pg-

bench, it is necessary to make settings where Pg-

bench requires speciﬁc tables for the execution: pg-

bench accounts, pgbench branches, pgbench history

and pgbench tellers.

In the graphs presented in Figures 1 and 2, the ver-

tical axis shows the values of TPS and the horizontal

axis the variations according to the number of cus-

tomers. The Pgbench experiments were executed ﬁve

times for every 20 clients, then we doubled the num-

ber of clients and executed the same ﬁve executions

until reaching 280 clients, that is, we started with 20

clients after 40, 80, 160 and ﬁnished with 280, in total

twenty-ﬁve runs for each instance using Pgbench. At

each rerun of the scripts, the base has been reset.

In the Pgbench results, we present the arithmetic

mean of the ﬁve executions. The duration of the test

was measured in TPS. The higher the result value, the

more efﬁcient the performance. It is possible to see

in the graphs that in both experiments Amazon was

more efﬁcient in all results compared to the Google

instance. Compared to our baseline, the performance

of our instances in the cloud is lower, but we need to

take into account the hardware conﬁgurations.

Figure 1: TPS values in the instances.

Investigating the Performance of Moodle Database Queries in Cloud Environments

273

Figure 2: TPS values between the instances.

4.2 Results with Sysbench

Sysbench executes a speciﬁed number of threads and

they all execute requests in parallel. We started the

tests with 20 threads, then doubled the value to 40, 80

until we reached 160 threads. The last execution had

to be ﬁnalized in 160 threads, because from this value

the instances in cloud present difﬁculties in fulﬁlling

the request.

On each run, the test database was reset and the

tests were performed again. We repeated the same

command ﬁve times by changing only the value of

the thread. Finally, we averaged the TPS. Figures 3

and 4 show the results and the average response time

variation of the experiment. Amazon’s performance

was superior compared to Google’s, according to the

settings we use.

Figure 3: Divergent values throughout the test between the

instances.

4.3 Results with Moodle Benchmark

Plugin

The benchmark plugin was installed on each Moodle

environment in both instances and local. As the objec-

tive of this work is to evaluate the performance of the

Moodle database in the cloud, we use only the items

related to the performance in the database, which are

Figure 4: Divergent values throughout the test between the

instances.

items numbered 6, 7 and 8. Item 6 inserts 25 courses

temporarily. Item 7 performs the same selection one

hundred times and item 8 performs the same selection

250 times, but different from item 7.

The experiments had the duration of their execu-

tions counted in seconds. The lower the average time

in seconds, the better the provider’s performance. We

add the results of the 3 items (6, 7 and 8) in the ﬁve ex-

ecutions of each instance, then we calculate the arith-

metic mean.

Figure 5 shows the results of experiments with the

plugin. Again, compared to instances in the cloud, the

Amazon instance performed better than the Google

instance.

Figure 5: Experiment duration in seconds.

5 CONCLUSIONS

This article presented a preliminary performance in-

vestigation of the Moodle database in cloud environ-

ments. In our experiments, we used the free ver-

sion of the cloud providers AWS and GCP. We in-

stalled Moodle in instances on both providers. We

used the Pgbench and Sysbench database benchmarks

and the Moodle Benchmark Plugin to gather perfor-

mance metrics. We run all the benchmarks on our

local computer in order to compare results with a sig-

niﬁcant baseline.

According to the data we gathered from the bench-

marks in the environments we considered, it is possi-

ICEIS 2020 - 22nd International Conference on Enterprise Information Systems

274

ble to observe that Amazon instance presented bet-

ter performance in all cases, compared with instance

Google. Due to the inherent characteristics of cloud

environments, this results may change within certain

limits when we chose different service/billing plans,

so this remain to be investigated. Also, it may be use-

ful to gather more performance metrics from speciﬁc

cases of Moodle usage, according to the characteris-

tics of each educational institution. To encourage re-

productibility and further work, we made our data and

scripts available in a public repository.

Our choice of a IaaS service model granted us au-

tonomy to install different benchmarks and to perform

measurements directly from our instances. This may

not be possible when Moodle is provided in a SaaS

model, but such alternative still deserves to be inves-

tigated. Obviously, performance is not the only fac-

tor when deciding to migrate Moodle to the cloud,

but simplicity and cost effectiveness are useless when

we are not sure how the system will perform under a

cloud environment.

As a suggestion of future work, it is intended to

conduct experiments with other cloud providers not

contemplated in the study, use other benchmarks for

performance evaluation. Another important aspect is

the possibility, if there are ﬁnancial resources, to per-

form the cost-beneﬁt comparison on a paid way. For

the customer, may need to hire a service that performs

best, but that ﬁts within your budget.

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