A Research-backed Extended Taxonomy for Cloud Computing Elasticity
Raouia Bouabdallah
Higher Management Institute of Tunis, Tunis University, Bou Choucha Street, Tunisia
Elasticity, Taxonomy, Cloud Computing, Provisioning, Monitoring.
Elasticity is an important feature to characterize cloud computing from traditional Information Technology
(IT) infrastructure. It refers to the ability of the cloud provider to provision and release cloud resources, with
demand, appearing to be infinite in any quantity at any time.In this paper, we propose a taxonomy which is
an extended elasticity classifications compared to existing ones. Then, we discuss industrial and academic
research on cloud elasticity to identify the main issues and drawbacks. Finally, we propose a synthesis of the
studied works based on elasticity solutions’ characteristics provided from our taxonomy.
Cloud computing (Fontana de Nardin et al., 2021) is
a new paradigm in the IT evolution. In the literature,
there is no,universal definition of the cloud computing
(Chen et al., 2022). It is commonly accepted that the
cloud is characterized by a certain degree of elastic-
ity and on demand access capacity. A definition given
by the National Institute of Standards and Technolo-
gies (NIST) (Mell and Grance, 2011) defines cloud
Computing as a model for enabling on-demand net-
work access to a shared pool of configurable com-
puting resources that can be quickly put into service
through the interaction with the provider. This model
is composed of five essential characteristics (Karuna
Pande Joshi and Yesha, 2010) which are: on-demand
self-service, broad network access, resource pooling,
measured service (Sambit et al., 2020) and rapid elas-
ticity. On-demand self-service refers to the ability of
a cloud provider to provision cloud resources when-
ever the clients need them. Broad network access de-
scribes the network used to access resources hosted in
the cloud. Resource pooling describes a situation in
which the resources of the provider are pooled among
several clients. Measured service refers to the abil-
ity of the cloud provider to monitor, control and re-
port resources. Elasticity is a unique feature of the
cloud technology(BAR, 2020). It refers to the ability
of cloud provider to provision cloud resources, with
demand, appearing to be infinite in any quantity at
any time (Beltran, 2016). Efficient management is a
challenging task with cloud elasticity. To get a wide
view of the main cloud elasticity issues and gaps, a
profound survey of research efforts is required. In this
paper, we will propose an extended elasticity classifi-
cations taxonomy and we will discuss industrial and
academic research on cloud elasticity based on elas-
ticity solutions’ characteristics.
The structure of this paper is as follows. In Sec-
tion 2, we present related studies accordance with the
elasticity. Afterwards, we present, in Section 3, a syn-
thesis of the mentioned works within a classification
table. In Section 4, we provide an extended classifi-
cation in accordance with the scope, purpose, policy,
method, monitoring metric, etc.
To get a broad view of the elasticity problem, a review
of related work is required to identify the main issues
and drawbacks. Doing so, we present in this section
an overview of state-of-the-art solutions. We classify
these solutions regarding the elasticity policies used
for executing of monitoring actions. These policies
are classified into three models which are: reactive,
proactive and hybrid (e.g. proactive and reactive mod-
2.1 Reactive Elasticity
Many of the research is interested with the reac-
tive elasticity model. For instance, the authors in
Bouabdallah, R.
A Research-backed Extended Taxonomy for Cloud Computing Elasticity.
DOI: 10.5220/0011070500003200
In Proceedings of the 12th International Conference on Cloud Computing and Services Science (CLOSER 2022), pages 231-237
ISBN: 978-989-758-570-8; ISSN: 2184-5042
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
(P. Fawaz and Lionel, 2016) and (Paraiso et al., 2013)
propose a multi-cloud platform as a service (PaaS) ap-
proach based on a reactive model for monitoring the
distributed application through a Monitoring compo-
nent which is deployed on any cloud resource (e,g.
VM) hosting the application. This component is re-
sponsible for capturing any change in the state of the
application as well as collect, aggregate and report in-
formation about an application deployed on multiple
cloud environments. This approach adopts a reactive
model based on a set of elasticity rules to increase or
decrease the number of resources running the appli-
cation via a load balancer used to distribute the ap-
plication’s workloads. However, these rules are not
dedicated to specify the predication expression.
In this work (d. Alfonso et al., 2013), the authors
propose a system called CLUES (Cluster Energy Sav-
ing System) which is a general power management
tool based on Dynamic Power Management (DPM)
approach to optimize the energy consumption of the
cluster . This system uses an automated mechanism
to power on or power off the resources (e.g., vir-
tual machines) according to the current workload and
integrates different resource managers by using Re-
source Manager Connectors. However, these connec-
tors are basically used to the batch systems which pro-
vide control over batch jobs, than the cloud systems
(e.g., opennebula and openstack). The cloud systems
can not provide monitoring information about the de-
ployed resources such as the number of handled jobs
regarding the processing units (cores). The authors,
in this work, propose also a reactive model to han-
dle the energy consumption. This model executes an
action when the measured value is over or under a
given threshold defined by the client. However, the
proposed model does not take into account a time in-
terval that the scale out (down) condition should sat-
isfy before executing an action. This may result in
oscillation of the system.
Another reactive model of elasticity was proposed
by (Mohamed et al., 2016) to optimize the provi-
sion of resources in the cloud. This work proposes
an extension of the Open Cloud Computing Interface
(OCCI) for monitoring and reconfiguring resources.
To do so, It defines a list of OCCI Entities and Mix-
ins to enable cloud resources elasticity. This elas-
ticity was defined by adding elasticity rules based
on an event-condition-action approach for monitor-
ing the metric data obtained from cloud infrastructure.
But, this work does not provide a predictive elastic-
ity mechanism to dynamically minimize the resource
consumption over time.
Most of these previous researchers ( such as
(P. Fawaz and Lionel, 2016), (d. Alfonso et al., 2013)
and (Mohamed et al., 2016)) apply threshold-based
rules to perform the reactive model. Most of them
allow clients to set rules for the provisioning of re-
sources based on two threshold values per perfor-
mance metric, which are: the upper threshold (ThrU)
and the lower threshold (ThrL). However, (Koperek
and Funika, 2012) is based on four threshold values
including: the upper threshold (ThrU), slightly below
the upper threshold (ThrbU), lower threshold (ThrL)
and slightly above the lower threshold (ThroL).
RESERVOIR (Resources and Services Virtualiza-
tion without Barriers) is a FP7 project (Rochwerger
et al., 2009) that aims to provide to all clients ser-
vices oriented computing without requiring a large
capital investment in the infrastructure. To do so,
the RESERVOIR refers to the peer-to-peer federated
clouds to distribute data centers owned by separate
providers. When a cloud provider does not have the
needed computational resources to serve its clients, it
rents these ones from another cloud provider. This
project has two main actors which are: the Service
Provider and Infrastructure Provider (Gal
an et al.,
2009). The Service Provider represents the entity that
needs to lease an IT capacity (e.g., hardware) from
a cloud infrastructure provider, instead, it uses an in-
house one. Doing so, it defines its requirements in a
Service Definition Manifest (Chapman et al., 2012).
This is used to specify one or more virtual machine
images in a single file named OVF package which
is based on the DMTF’s Open Virtualization Format
(OVF) standard. The Service Definition Manifest may
also define elasticity rules using Key Performance In-
dicators (KPIs). The Infrastructure Provider is the
essence of the RESERVOIR service cloud. It repre-
sents a cloud vendor that provides resources as vir-
tual machines as a service in a pay-as-you-go finan-
cial model.
2.2 Proactive Elasticity
Some researchers are interested with the proactive
elasticity to adjust the provisioning of cloud re-
sources. The time series analysis approach is a proac-
tive model in nature. This approach is used to predict
a sequence of metric data measured over a time. It
includes a set of forecast methods such as: the mov-
ing average, auto-regression, exponential smoothing
and polynomial regression. For example, the authors
in (Gong et al., 2010) use the moving average as a
forecast method to automatically predict the resources
required to the application. Unfortunately, this work
provides poor results. Others (Roy et al., 2011) use a
second order auto-regressive moving average method
(ARMA) to identify the number of resources used by
CLOSER 2022 - 12th International Conference on Cloud Computing and Services Science
the application. It is worth to note that the proactive
model is not only used to identify the workload of the
application as explained previously in (Gong et al.,
2010) and (Roy et al., 2011), but also is used to opti-
mize the number of VM migrations in the cloud data
center. For example, Subhadra and Anil (Shaw and
Singh, 2015) hope, by this migration, to minimize the
number of physical machines with reduce the perfor-
mance degradation comes by unneeded movement of
VMs. The optimization of the number of VM mi-
grations depends on the current as well as the future
CPU utilization of physical hosts using the exponen-
tial smoothing as a forecast method.
2.3 Hybrid Elasticity
A few pieces of research involved both the proac-
tive and reactive models. For instance, the authors in
(Shaw and Singh, 2015) propose reactive and proac-
tive models for resolving bottlenecks of the web ap-
plication in order to satisfy the response time require-
ment. The reactive elasticity method is applied to re-
solve the bottleneck by scaling up the resources re-
quired by the web application. The proactive elastic-
ity method is applied when the allocated resources are
not required during a period of time to scale down the-
ses resources whenever possible. This model is based
on the time series analysis approach and is developed
using the polynomial regression method.
In (Bauer et al., 2019), the authors propose a re-
active and proactive models to scale up and down
cloud resources meeting the SLA. The authors used
the queering theory method as a proactive model to
scale down the resources and the thresholds method
as reactive model to scale them up. This work does
not propose a novel reactive model method. More-
over, the proposed proactive model uses the number
of requests handled per time unit as a control metric
instead of the hardware metrics such as memory and
CPU usage.
In (Urgaonkar et al., 2008), the authors propose
propose a reactive and proactive models. The predic-
tive method is used over long time scales( hours and
days). While, the reactive model is used over short
time scales ( seconds and minutes).
We present an overview of state-of-the-art solutions
((P. Fawaz and Lionel, 2016), (d. Alfonso et al.,
2013), (Rochwerger et al., 2009), AgentRW11 , etc
) that focus on the monitoring. It is worth noting
that these solutions handle partially the problem of
the bottleneck situations, especially the CPU bottle-
neck which is due to the high traffic web application.
Moreover, these situations attempt to provide moni-
toring for applications in cloud. But almost all the
proposed situations give tooling solutions to monitor
cloud applications behavior. It is worth noting that
almost all of these cited works either do not target
monitoring at different models which are reactive and
proactive, or do not provide an efficient solution to do
that. In Figure 1, we propose a synthesis of the stud-
ied works based on elasticity solutions’ characteris-
tics provided from our taxonomy that we will detail
in the next section.
Through the analysis of the studied solutions, we
note that these solutions follow different methods,
strategies and techniques so as to build their mech-
anisms. A review of related studies ((Coutinho et al.,
2015), (Najjar et al., 2014), (BAR, 2020)) in neces-
sary to identify the different classifications that have
been suggested in accordance with the elasticity so-
lutions’ characteristics. We push further these studies
((Coutinho et al., 2015), (Najjar et al., 2014)) and we
propose a taxonomy which is an extended elasticity
classifications compared to existing ones. This taxon-
omy is summarized in Figure 2 and is based on the
following characteristics: the scope, purpose, policy,
method, provider, monitoring metric and standard.
4.1 Scope
This characteristic defines in which cloud layers the
elasticity actions are applied (Al-Dhuraibi et al.,
2018). The decisions made by the cloud provider
or by the user of the cloud technology can be exe-
cuted either in the infrastructure or the application-
platform levels. We assume that these decisions are
represented by the actions of the provisioning of new
resources and releasing the unused ones. When these
decisions are performed at the infrastructure level, the
provider, which has an elasticity controller, converts
the client’s requirement to actions based on the virtu-
alization technology like VMs or containers. In case
the decisions of elasticity actions are performed on
the application or platform level, the elasticity con-
troller is embedded in the application which can be
either one tier or multi-tiered (Herbst et al., 2013).
This controller interacts with the cloud infrastructure
so as to add or release resources.
A Research-backed Extended Taxonomy for Cloud Computing Elasticity
4.2 Purpose
The elasticity solutions have different purposes which
are: increase (or decrease) the infrastructure capacity,
energy, performance and cost. Increase or decrease
the infrastructure capacity refers to the strategy ap-
plied by the cloud provider to re-size its infrastructure
capacity according to the amount of clients using this
one. The energy purpose aims at optimizing the total
energy consumption of IT equipments by reducing
the amount of servers used to run virtual machines.
The cost purpose is closely related to the energy
one but also covers the operational cost of such a
resource, hosted in the cloud, with maintaining the
required performance. The performance refers to
the ability of the cloud provider to retain Quality
of Service (QoS) metrics so as to respond to the
client’s requirement. By getting a broader view to the
elasticity objectives, we notice that there are a plenty
of perspectives. The cloud IaaS providers look to
maximize their profit in accordance with minimizing
its infrastructure. The cloud PaaS providers search
to reduce their payments. The clients (end-users)
seek to minimize the cost they pay to cloud provider,
while still receiving a good Quality of Service (QoS).
4.3 Policy
The policy refers to the required interactions for the
execution of elasticity actions. Regarding related
studies (Pham, 2016), there are two types of poli-
cies : the manual and automatic. The manual pol-
icy is performed by a manual intervention from the
client which is responsible for provisioning and mon-
itoring his virtual environment. The provision of a
service in the cloud has five phases executed in a se-
quential order (Karuna Pande Joshi and Yesha, 2010),
which are: the requirements, discovery, negotiation,
composition and consumption. In the requirement
phase, the client details his requirements. Then, the
provider searches the cloud services corresponding to
the client’s need in the discovery phase. Afterwards,
the negotiation phase occurs when there is conflicts
between the cloud provider and client about a service.
In the composition phase, the provider combines the
services required by the client to provide a single ser-
vice. Finally, this service is delivered to the client
in the consumer phase. It is worth to note that the
manual policy is used in several cloud providers such
as Rackspace and Microsoft Azure. However with
the automatic policy, all the elasticity actions are per-
formed automatically, which could be classified into
the reactive and proactive models.The reactive model
Studied Solutions
Scope Purpose Policy Method Provider Monitoring Metric Standard
Manual provisioning
(P. Fawaz and Lionel, 2016)
(Paraiso et al., 2013) X X X X X X X X X
(d. Alfonso et al., 2013) X X X X X X
(Mohamed et al., 2016) X X X X X X X X
(Shaw and Singh, 2015) X X X X X X X
(Bauer et al., 2019) X X X X X X X
(Rochwerger et al., 2009) X X X X X X X X
Figure 1: Synthesis of related work.
CLOSER 2022 - 12th International Conference on Cloud Computing and Services Science
reacts based on thresholds or rules, due to the ob-
served workload changes. Several industrial cloud
providers use this model such as: Amazon EC2 and
Rightscale, as well as academic researches such as:
(Han et al., 2014) and (Liu et al., 2013). Whereas,
the proactive model reacts to the predicted workload
changes. Most of the reviewed works ( such as (Da-
woud et al., 2011), (Liu et al., 2013) and (Sharma
et al., 2011)) that used this model could fit in one of
these three groups of technique: the time series anal-
ysis, queuing theory and control theory.
4.4 Method
It is related to the applied methods to increase (or
decrease) the size of cloud resource. With regard to
the underlining studies (Pham, 2016), there are two
kinds of methods : the coarse-grained or fine-grained.
The coarse-grained scaling refers to the ability of the
cloud provider to provision resources from an external
one. By the way, the fine-grained refers to the ability
of the cloud provider to scale resources in the fron-
tier of its infrastructure capacity. Generally within the
same provider, the resources can be scaled in two dif-
ferent manners: vertical and horizontal. The vertical
elasticity consists in increasing or decreasing charac-
teristics of resources (e.g virtual machine instances)
such as the CPU, memory, etc. However, the hori-
zontal elasticity refers to the ability to increase or de-
crease the number of allocated resources (e.g virtual
machine instances) needed to run an application in the
cloud provider.
4.5 Provider
It refers to the number of cloud providers that the elas-
tic solutions support simultaneously, which could be
a single cloud or a federation of clouds. A single
provider means that the elasticity control is applied
on only one cloud provider. While in case of feder-
ated clouds, the elasticity control is executed across
a set of cloud providers. The examination of review
works show us that there are two cloud federation
models: vertical federation and horizontal federation
(Celesti et al., 2010). The vertical federation reaches
all levels of cloud service models such as IaaS, PaaS
and SaaS. For instance, a SaaS cloud provider puts
his services on the top of a PaaS cloud provider like
Microsoft Azure Service Platform and Google App
Engine. However, the horizontal federation of clouds
deals with one cloud service model level such as IaaS.
4.6 Monitoring Metric
The monitoring metrics describe the types of values
the elasticity control use to monitor the behavior of
the underling cloud resources. The monitoring met-
rics are available at these levels: the operating system
and application metrics. The operating system met-
rics are applied in evaluating the performance of a vir-
tual hardware with overlooking the applications run-
ning on it. These metrics are: the CPU, RAM, Disk
space and network. However, the application metrics
refer to the units of work that involve on the virtual
hardware as well as applications. These metrics can
be the response time and the number of requests.
4.7 Standard
It refers the standards that the elastic solutions sup-
port, which can be the OCCI
and OVF
. The Open
Cloud Computing Interface (OCCI) is one of the first
open extensible standards for managing any kind of
resources provided by cloud providers. This standard
is supported by a large community of cloud providers
including commercial ones such as Amazon EC2, as
well as cloud platforms such as CloudStack, Euca-
lyptus, OpenNebula. While, the Open Virtualization
Format (OVF) (DMTF, 2015) specification offers a
portable and an extensible format for packaging and
distributing cloud resources (e.g virtual machines) in
a standard format.
In this paper, we discussed industrial and academic
researches on the elasticity in the cloud computing.
Then, we presented a synthesis of the mentioned
works within a classification table. Afterword, we
proposed an extended elasticity classifications taxon-
omy which is an extended elasticity classifications
compared to existing ones. This taxonomy is based
on the following characteristics: the scope, purpose,
policy, method, provider, monitoring metric and stan-
In our future work, we will limit our research to
the coarse grained elasticity and we will propose an
extension for the Open Cloud Computing Interface
(OCCI) to support the automatic negotiation between
different cloud providers.
OCCI: Open Cloud Computing Interface
OVF: Open Virtualization Format
A Research-backed Extended Taxonomy for Cloud Computing Elasticity
Nb. of requests
Response !me
Applica!on/ Pla"orm
infrastructure capacity
Threshold-based rules
Time series analysis
Queuing theory
Control theory
Fixed gain controllers
Adaptive controllers
Kalman filter
Fuzzy model
Moving average methods
Exponential smoothing
Manual provisioning
Ver!cal federated
Horizontal federated
Figure 2: Classification of elasticity solutions.
This research was enabled in part by support provided
by SSOIE-COSMOS laboratory from university of
Tunis (Tunisia). We would also like to thank Dso Ser-
vices company by providing us the required resources
to release this work.
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A Research-backed Extended Taxonomy for Cloud Computing Elasticity