Multi-tenancy in Cloud-native Architecture: A Systematic Mapping Study

Abstract

Cloud-native architectures has become an essential part of the cloud computing paradigm with the capacity of improved horizontal and vertical scalability, automation, usability and multi-tenancy. However, there are parts that are yet to be fully discovered like multi-tenancy. Multi-tenancy an essential part of the cloud computing, has not been fully. The purpose of this study is to survey existing research on multi-tenancy in cloud-native architecture in order to identify useful trends, opportunity, challenges and finally the needs for further researches. A systematic mapping method was used to systematically compare, classify, analyse, evaluate and appraise existing works of literature on multi-tenancy in cloud-native. We started from over 921 potentially relevant peer reviewed publications. We applied a selection procedure resulting in 64 peer reviewed publications over the last six years between 2015 to 2022 and the selected studies were classified through the characterisation framework. The review shows the emerging challenges and trending concepts in multi-tenancy within cloud native architecture, but also discusses the improvement in multi-tenancy while considering cloud native architecture in the recent years.

Full text

Multi-tenancy in Cloud-native Architecture: A Systematic Mapping
Study
DANIEL OLABANJI, TINEKE FITCH, OLUMUYIWA MATTHEW
University of Portsmouth, Portsmouth,
UNITED KINGDOM
Abstract: - Cloud-native architectures has become an essential part of the cloud computing paradigm with the
capacity of improved horizontal and vertical scalability, automation, usability and multi-tenancy. However,
there are parts that are yet to be fully discovered like multi-tenancy. Multi-tenancy an essential part of the cloud
computing, has not been fully. The purpose of this study is to survey existing research on multi-tenancy in
cloud-native architecture in order to identify useful trends, opportunity, challenges and finally the needs for
further researches. A systematic mapping method was used to systematically compare, classify, analyse,
evaluate and appraise existing works of literature on multi-tenancy in cloud-native. We started from over 921
potentially relevant peer reviewed publications. We applied a selection procedure resulting in 64 peer reviewed
publications over the last six years between 2015 to 2022 and the selected studies were classified through the
characterisation framework. The review shows the emerging challenges and trending concepts in multi-tenancy
within cloud native architecture, but also discusses the improvement in multi-tenancy while considering cloud
native architecture in the recent years.
Key-words: Cloud-Native, Multi-tenancy, Isolation, Cloud Computing, Systematic Mapping study.
Received: March 26, 2022. Revised: January 2, 2023. Accepted: February 5, 2023. Published: March 7, 2023.
1 Introduction
Cloud computing has several essential characteristics
that make it more robust and attractive to several
users. The technology enables the pay-per-use
business model and moves local storage to cloud-
based storage for average internet users and almost
every commercial entity, [1]. Cloud computing is
classified into private, public, hybrid, and
community deployment models. These deployment
model classifications are based on the infrastructure's
ownership, management, and operation. Similarly
cloud computing is also classified by service models
namely: Software as a Service (SaaS), Platform as a
Service (PaaS) and Infrastructure as a Service (IaaS)
[2], and each service model has different
management and control that makes each unique.
The SaaS delivers applications that are accessible to
different users online, although, the user does not
manage or control the underlying cloud
infrastructure. While in the PaaS, the user has access
and controls their data, the application, and the
application development lifecycle, without control
over the infrastructure. Whereas in the IaaS the user
owns and manages the applications, data, operating
system and application runtime. The cloud service
model is thus, the classification of the services that a
service provider can offer in a public cloud. This
study focused mainly on the IaaS model and private
cloud deployment model, where both multi-tenancy
and virtualisation are implemented.
Multi-tenancy is an architectural tactic to increase
cost-efficiency by sharing the available resources
maximally among several users, [3]. According to
[4], there are three methods for achieving multi-
tenancy in cloud computing: using a database,
virtualisation, and physical separation. Of the three
options, virtualisation is the most used options in
achieving multi-tenancy in cloud computing.
Virtualisation is achieved in cloud computing
through the implementation of either virtual
machines or containers [5]. In a virtual machine
(VM) setup, multi-tenancy is achieved through the
use of a hypervisor, which allows service providers,
developers, engineers and designers to make a single
instance of an application, hardware, middleware,
and database to be shared between several entities by
isolating each tenant from the others [5]. However,
this comes with several limitations such as; resource
management, scalability and lack of automation and
to solve these issues another virtualisation
technology was introduced.
The industry developed container virtualisation to
provide on-demand scalability, optimal resource
usage, fault tolerance, and automation. Container
virtualisation has been seen as an alternative to VMs
in the IaaS cloud computing development model,
and it is becoming a vital part [7]. Containers are
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lightweight virtualisation that makes use of fewer
resources and less time to provide scalable, portable
and interoperable applications in cloud computing
[6]. The container virtualisation, Microservice,
DevOps and other improvements in cloud computing
architecture have made the term 'cloud-native'
popular in industries and academic.
It is noteworthy that the term cloud-native was early
mentioned at the early stage of cloud computing to
means applications developed solely for cloud
computing [8]. However, as more ideas and
innovation emerged, the term cloud-native gained a
more comprehensive meaning and popularity from
the year 2015 [9] to mean cloud architecture that
uses microservice and containers virtualisation to
provide a scalable application further details in
Section 2.1. However, this improvement comes with
issues of implementing multi-tenancy specifically
the sharing of available resources in cloud-native
architecture environment. The understanding of
performance bottlenecks in multi-tenancy based
cloud-native environment is critical in achieving
performance improvements in cloud native
adoptability, application level fairness and resource
management, [7].
Kubernetes which is the main container
orchestration system lacks sufficient multi-tenant
supports by design, [10]. Furthermore, the
inadequate support for multi-tenancy during the
development of kubernetes orchestrator brings the
lack of guaranty secure isolation between tenant,
[11], diminish the benefits of cloud computing and
makes is difficult to adopt. In order to provide detail
understanding of multi-tenancy in cloud-native
architecture and trends, this study conducted a
systematic mapping study which intended to
identify, evaluate and summarise the findings about
multi-tenancy in cloud-native architecture. Which
will help to provide more insight on the existing
academic work on cloud-native architecture, its
challenges and improvement. Sixty-four peer-
reviewed publications were methodically selected
years ranging from 2015 to 2022 in which cloud-
native architecture and multi-tenancy are mentioned
and discussed in their topic, keywords and metadata
from different online academic database.
The remainder of this paper is structured as
follows: Section 2 describes the background and
related research. Section 3 explains the research
methodology, research question and screening;
Section 4 provides a classification scheme, followed
by the mapping; Section 5 discusses findings, and
Section 6 provides the review's conclusions.
2 Multi-tenancy
Multi-tenancy is a computing architectural concept
concerned with information sharing among multiple
users referred to as tenants. In case cloud computing,
improvement of resource utilisation and service
availability in cloud computing are based on multi-
tenancy, [12], [13]. [12], explained that multi-
tenancy is a concept that enables sharing the same
service instance, scaling up and down the resources
allocated among different tenants. Both
characteristics improve resource utilisation, cost and
service availability. In addition, apart from the
capacity of multi-tenancy to share resources as a
strategy in cloud computing, it also enables service
providers to maximise resource utilisation and, thus,
reduce the servicing costs per tenant, [14]. In the
database perspective, multi-tenancy as a principle
where a single instance of the DBMS runs on a
server, serving multiple clients (tenants), [15]. The
multi-tenancy in database systems supports several
separate and distinct groups of users, the users are
referred to as tenants.
Multi-tenancy can thus be defined as an architectural
concept that makes resource sharing possible and
enforce isolation between tenants. These tenants
could be applications, users, physical or virtual
infrastructure or systems. Furthermore, as [16],
observed, multi-tenancy is an essential property of
cloud computing that optimises resource utilisation
by allowing multiple consumers and multiple
workloads to share computing and network
infrastructure using virtualisation technology. Multi-
tenancy in cloud computing has its challenges: the
lack of filtration of the inside part of the servers
because both the client and the attackers are in the
same server [13], access control and resource
allocation. Multi-tenancy enables computation
instances from different tenants running on the same
physical server in an IaaS service model [17]. Those
identified challenges affecting multi-tenancy were
transferred into the cloud-native architecture.
2.1 Cloud-native Architecture (CNA)
Cloud-Native Computing Foundation (CNCF), the
sole convener of the architecture, defines cloud-
native as a set of technologies that empower
organisations to build and run scalable applications
in modern, dynamic environments such as public,
private, and hybrid clouds. The approach is
exemplified by "containers, service meshes,
microservices, immutable infrastructure, and
declarative APIs". CNCF further claims that "these
techniques enable loosely coupled systems that are
resilient, manageable, and observable combined with
robust automation, that allow engineers to make
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high-impact changes frequently and predictably with
minimal toil" , [18]. In [19], it is observed that cloud-
native technologies are used to develop applications
built with services packaged in containers deployed
as microservices and managed on elastic
infrastructures through agile DevOps processes and
continuous delivery workflows.
To further understand CNA and containers, there is a
need to briefly discuss about ‘Microservice’.
Microservice architecture divides applications into
smaller self-contained components, called
microservice and the microservice serves specific
business functions and communicates via lightweight
language-agnostic APIs, [20]. Microservice
architecture necessitates a virtualisation technique
that can provide a better level of isolation,
scalability, deployment, updating and elastic
resources; these can be achieved using a different
virtualisation technique than the virtual machine.
These needs produce the success of the containers
virtualisation mechanism which are now widely used
in cloud computing environment and configuration.
Containers virtualise the operating system and spin
up multiple containers within milliseconds, whereas,
virtual machine are based on running software on
physical hardware to simulate physical computer and
different operating systems need to be installed on
virtual devices, [6]. However, as discuss above the
CNA consist of the container virtualisation and
microservice architecture and need to be study to
shows the gaps and add to knowledge in cloud-
native architecture.
3 Research Methodology
In this study, systematic mapping study method was
adopted to ascertain the trends, research areas and
challenges of multi-tenancy in CNA. A Systematic
Mapping Study (SMS) is useful in determining the
structure of the study in a research area where there
is a lack of high-quality primary papers. Systematic
mapping as an approach designed to give an
overview of a research area through classification
and quantification of contributions in the categories
arising [21]. A systematic mapping study helps to
achieves a broad review of primary studies in a
specific topic area and to identify the available
evidence and research gaps.
This study follows the guidelines for systematic
mapping in software engineering which provides
insight through a rigorous and methodical approach
to searching, classifying literature and extracting
evidence with analytical evaluation [21]. Based on
the above definition and description of the SMS, it is
a suitable method for the topic area. There are other
literature review methods, but they lack the
systematic analysis method in SMS. Table 1 presents
the step-by-step process taken to achieve the
research outcome and present the contribution in a
methodical way.
Table 1. Systemic Mapping process
3.1 Research Questions
The following research questions were answered in
this study and they are methodically formulated to
guide the study.
[RQ1]: What does the term 'multi-tenancy' in cloud-
native architecture mean?
[RQ2]: What are the existing trends in cloud-native
multi-tenancy?
[RQ3]: What are the foreseeable challenges of multi-
tenancy in cloud-native architecture?
3.2 Conducting the Primary Search
Publications and papers were extracted from relevant
electronic databases through the use of search terms
and keywords. The selection of search terms were
drew on the keyword identification guidance of [21].
This is done by grouping keywords and synonyms to
formulate a search string that covered a larger space,
unbiased and avoid incompleteness. Similar relative
terms were used to construct the search string.
Research papers such as [9] have considered key
'cloud-native' search words. Surprisingly, several
other papers use 'containers' to represent CNA.
Although there are other virtualisation techniques in
cloud computing, container technology is the
virtualisation method used in the CNA, hence,
including 'container' in the search string. Also, some
of the literature could be under-represented without
the addition of the word. The search string used to
initiate this study was:
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("Cloud native" OR "cloud-native” OR
"container" OR "native cloud") AND ("multi-
tenant" OR "multi-tenancy”)
This study considered the PICO (Population,
Intervention, Comparison and Outcome) keyword
formulation search approach. Table 2 shows the
online databases used and the 921 papers generated
from the online database.
Table 2. Papers considered from the initial search
3.3 Literature Screening
The screening mechanism was applied for
scrutinising the selected papers with inclusion and
exclusion criteria. The design and methodological
guidelines from [21] were used to screen the
literature. Table 3 shows both the inclusion and the
exclusion criteria that were applied.
Table 3. Literature screening criteria
The literature screening criteria helped remove the
papers that did not focus on cloud-native and multi-
tenancy and those that were not related to the
inclusion criteria. Likewise, a specific year gap was
selected as the target years. The reason for these
target years has been discussed earlier in this
publication. Based on the criteria in Table 3 and
publications indexed in multiple databases resulting
in duplication, 867 publications were removed.
Which bring the remaining categorises 64
publications comprised only peer-reviewed
publications. The selected papers can be found in the
Table 4 with classifications. Also, publication in the
table was not sorted to avoid any bias. The second
and third authors contributed to this work by
providing a quality performance control to each of
the section in the research methodology.
4 Keywording and Topic Classification
4.1 Topic Classification
This process create a classification scheme for clear
understanding and straightforward design of the
systematic mapping. The selected papers are
classified into two main categories:
 Topic-independent classification
 Topic-specific classification
4.1.1 Topic-independent Classification
Topic-independent classification classifies the papers
based on the research approach and not by topic or
keyword. According to [21], adopting an existing
classification scheme in a systematic mapping study
is advisable. This study drew on the classification
proposed by [84], with a few adjustments to their
categories. For instance, [9] suggested that
philosophical papers are rare in software
engineering. Thus, this study replaced philosophical
category with the survey category to provide a
clearer classification. Details of this classification of
the selected papers can be found in Table 4.
Sources
No.
Oldest
New
Accessed
IEEE
64
2015
2022
4.9.2022
ScienceDirect
360
2015
2022
7.9.2022
ACM
159
2015
2022
7.9.2022
SpringerLink
338
2015
2022
7.9.2022
Total
921
Inclusion
 Journals or conferences that are
peer-reviewed and researching
multi-tenancy and cloud-native
or cloud-native architecture.
 Studies published between year
2015 to 2021
Exclusion
● Studies that did not relate to
cloud computing
● Studies that were not presented
in English
● Studies that were not accessible
in full text
● Studies that were non-peer-
reviewed
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Table 4. Included and categorised publications
Study
No
Title
Year
Type
Validation
Evaluation
Solution
Survey
Option
Experience
S1
Evaluating the Effect of Multi-
tenancy Patterns in
Containerized Cloud-hosted
content management
system[22]
2018
Conference
X
X
X
X
S2
Scone: Secure Linux
Containers with intel SGX[23]
2016
Conference
X
X
X
X
S3
Leveraging Kernel security
Mechanisms to improve
container security: a
survey[24]
2019
Conference
X
X
S4
KubeSphere: An Approach to
Multi-Tenant Fair Scheduling
for Kubernetes Clusters[25]
2019
Conference
X
X
X
X
S5
KubeSphere: An Approach to
Multi-Tenant Fair Scheduling
for Kubernetes Clusters [26]
2018
Conference
X
X
S6
A Case for Performance-
Aware Deployment of
Containers [27]
2019
Journal
X
X
X
S7
Design and Implementation of
Multi-tenant Vehicle
Monitoring Architecture
Based on Microservices and
Spark Streaming [28]
2020
Conference
X
X
X
S8
Preemptive and low latency
Datacenter Scheduling via
lightweight Containers [29]
2019
Journal
X
X
X
S9
Multi-tenant utility computing
with compute containers. [30]
2015
Conference
X
X
X
S10
Studying the Applicability of
Intrusion Detection to Multi-
tenant Container
Environments [31]
2019
Conference
X
X
X
S11
Using Attack Injection to
Evaluate Intrusion Detection
Effectiveness in Container-
based Systems [32]
2020
Conference
X
X
X
S12
A Study on the Security
Implications of Information
Leakages in Container Clouds
[33]
2018
Journal
X
X
S13
ContainerLeaks: Emerging
Security threats of Information
Leakages in Container Clouds
[34]
2017
Conference
X
X
X
X
S14
Houdini's Escape: Breaking
the resource rein of linux
control groups [35]
2019
Conference
X
X
X
S15
Towards a Taxonomy of
Microservices Architectures
[36]
2017
Conference
X
X
S16
Singularity: Simple,secure
containers for compute-driven
workloads [37]
2019
Conference
X
S17
Software-defined object
storage in multi-tenant
environments [38]
2019
Journal
X
X
X
S18
SCoPe: A Decision System for
Large Scale Container
Provisioning Management
[39]
2016
Conference
X
X
X
S19
An Improved Kubernetes
Scheduling Algorithm for
Deep Learning Platform [40]
2020
Conference
X
X
X
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Table 4. Included and categorised publications
Study
No
Title
Year
Type
Validation
Evaluation
Solution
Survey
Option
Experience
S20
PARES: Packet Rewriting on SDN-
Enabled Edge Switches for Network
Virtualization in Multi-Tenant
Cloud Data Centers [41]
2017
Conference
X
X
X
X
S21
Energy efficiency comparison of
hypervisors [42]
2019
Journal
X
X
S22
A framework for black-box SLO
tuning of multi-tenant applications
in Kubernetes [43]
2019
Conference
X
X
X
X
X
S23
Securing Cloud Containers Using
Quantum Networking Channels [44]
2016
Conference
X
X
X
X
X
S24
Native Cloud Applications:Why
Monolithic Virtualization Is Not
Their Foundation [45]
2017
Conference
X
X
S25
Customizing Multi-Tenant SaaS by
Microservices: A Reference
Architecture[46]
2019
Conference
X
X
S26
Thread-level resource consumption
control of tenant custom code in a
shared JVM for multi-tenant SaaS
[47]
2021
Journal
X
X
S27
Evaluation of Virtualization and
traffic filtering methods for
container networks [48]
2019
Journal
X
X
S28
The Nas Benchmark Kernels for
Single and Multi-tenant cloud
Instances with LXC/K [49]
2018
Conference
X
X
X
S29
Reprint:Legiot: A Lightweight Edge
Gataway for the Internet of Things
[50]
2019
Journal
X
X
X
X
S30
Using Microservices for Non-
intrusive Customization of Multi-
tenant Saas [51]
2019
Conference
X
X
X
X
S31
Service-oriented Multi-tenancy (SO-
MT): Enabling Multi-tenancy for
Existing Service Composition
Engines with Docker [52]
2016
Conference
X
X
X
X
S32
An Open Sharing PatternDesign of
Massive Power Big Data [53]
2019
Conference
X
X
X
X
S33
Docker Cluster Management for the
Cloud - Survey Results and Own
Solution [54]
2016
Journal
X
X
S34
Profiling distribution systems in
lightweight virtualized
environments with logs and resource
metrics [55]
2018
Conference
X
X
S35
A Machine Learning Model for
Detection of Docker-based APP
Overbooking on Kubernetes [56]
2021
Conference
X
X
X
S36
SynAPTIC: Secure And Persistent
connecTIvity for Containers [57]
2017
Conference
X
X
X
X
S37
Scheduling dynamic workloads in
multi-tenant scientific workflow as a
service platforms [58]
2018
Journal
X
X
X
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Table 4. Included and categorised publications
Study
No
Title
Year
Type
Validation
Evaluation
Solution
Survey
Option
Experience
S38
Right Scaling for Right Pricing: A Case
Study on Total Cost of Ownership
Measurement for Cloud Migration [59]
2019
Conference
X
S39
Containers and Virtual Machines at scale:
A Comparative study [60]
2016
Conference
X
X
X
X
S40
Building a multi-tenant cloud service
from legacy code with Docker containers
[61]
2015
Journal
X
X
S41
Cloud Native Databases: An Application
Perspective [62]
2017
Conference
X
X
X
S42
Towards Vulnerability Assessment as a
Service in OpenStack Clouds [63]
2016
Conference
X
X
X
X
S43
SWITCH-ing from multi-tenant to event-
driven video conferencing services [64]
2017
Workshop
X
X
X
S44
Towards a container-based architecture
for multi-tenant SaaS applications [3]
2016
Workshop
X
X
S45
Performance overhead of container
orchestration frameworks for
management of multi-tenant database
deployments [65]
2019
Conference
X
X
X
S46
Network Virtualization: Proof of Concept
for Remote Management of Multi-Tenant
Infrastructure [66]
2020
Conference
X
X
X
S47
Challenges for Building a Cloud Native
Scalable and Trustable Multi-tenant AIoT
Platform [67]
2020
Conference
X
X
X
X
S48
A Latency-driven Availability
Assessment for Multi-Tenant Service
Chains [68]
2022
Journal
X
X
X
S49
A Multi-Tenant Framework for Cloud
Container Services [10]
2021
Journal
X
X
X
S50
A Secure Container Placement Strategy
Using Deep Reinforcement Learning in
Cloud [69]
2022
Conference
X
X
X
S51
Advocating isolation of resources among
multi-tenants by containerization in IaaS
cloud model [70]
2017
Conference
X
X
X
S52
Container-Based Service Chaining: A
Performance Perspective [71]
2016
Conference
X
X
X
X
S53
Containers Resource Allocation in
Dynamic Cloud Environments [72]
2021
Conference
X
X
X
S54
Enhancing Proportional IO Sharing on
Containerized Big Data File Systems [73]
2021
Journal
X
X
X
X
S55
Framework for Analysing a Policy-driven
Multi-Tenant Kubernetes Environment
[74]
2021
Conference
X
X
X
S56
Improving the Security of Microservice
Systems by Detecting and Tolerating
Intrusions [75]
2020
Workshop
X
S57
Migrating Monoliths to Microservices-
based Customizable Multi-tenant Cloud-
native Apps [76]
2021
Journal
X
X
X
X
S58
Feasibility of container orchestration for
adaptive performance isolation in multi-
tenant SaaS applications [77]
2020
Conference
X
X
X
X
X
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Table 4. Included and categorised publications
4.1.2 Topic-specific Classification
This research did not achieve topic-specific
classification through the IEE, ISO/IEC Swebok
classification [85], so the researchers generated their
topic-specific classification. In agreement with [21],
who stated that most mapping studies design their
classification scheme. For this purpose, developing a
bespoke topic-specific classification was decided.
Authors' keywords from our selected papers were
extracted first, as most papers have keywords. For
those without keywords, such as [3], [65], and [23],
the approach of [21], was followed, which involved
an adaptive reading of the abstract in picking the
keywords, and when there is no abstract, reading the
introduction and conclusion to provide valuable
keywords for the study. The first reviewer extracted
the data by considering the inclusive and exclusive
criteria, and co-authors checked the outcome from
the extracted data.
4.1.2.1 Steps used in Developing Topic-Specific
Classification:
1. Extracted the keywords from each selected
paper
i. Identified those without keywords
ii. Adaptively read the abstract, introduction,
and conclusion to generate keywords for
those without
2. Grouped the keywords cohesively
3. Aggregated the grouped keywords
4. Grouped the papers into each aggregated
keyword.
4.2 Data Extraction and Mapping
The data extraction and mapping process is based on
the above classification techniques to develop the
keyword classification scheme. The data mapping is
also divided into the same two types: topic-
independent and topic-specific.
Study No
Title
Year
Type
Validation
Evaluation
Solution
Survey
Option
Experience
S59
Deep customization of multi-tenant
SaaS using intrusive microservices
[78]
2018
Conference
X
X
S60
EdgeNet: A Multi-Tenant and Multi-
Provider Edge Cloud [79]
2021
Workshop
X
X
S61
LogStore: A Cloud-Native and Multi-
Tenant Log Database [80]
2021
Conference
X
X
X
S62
Multi-Tenant Machine Learning
Platform Based on Kubernetes [81]
2020
Conference
X
X
X
S63
Performance Evaluation of Container-
Level Anomaly-Based Intrusion
Detection Systems for Multi-Tenant
Applications Using Machine Learning
Algorithms [82]
2021
Conference
X
X
X
X
S64
Reinforcement Learning for Resource
Management in Multi-tenant
Serverless Platforms [83]
2022
Conference
X
X
X
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Data mapping simplifies the mapping system and
answers the research questions
4.2.1 Topic-Independent Data Extraction and
Mapping
In this type of data mapping, the information
considered is not closely related to the publications'
keywords or content; instead, it includes other
factors, such as the year of publication, the venue
type, and the research approach.
4.2.1.1 Year of Publication
Firstly, the collated literature was separated into the
publication years (see Figure 1). In 2015, just two
publications considered multi-tenancy in CNA,
while in the following year, there was an increase in
the number, which remained steady until the year
2019. In 2019, there was another and more
significant rise, possibly because there was more
awareness of multi-tenancy needs and capabilities in
cloud-native orchestration software, such as
Kubernetes. The efforts put into such research
through the CNCF, and other secondary foundations
and organisations, focus on multi-tenancy in cloud-
native computing. Furthermore, security and
isolation have become critical in cloud-native
research for 5G, IoT, and artificial intelligence
technologies. Research into those technologies has
increased since 2019.
Fig. 1: Year of publication
(Note that the apparent significant drop in numbers
after 2019 was due very probably to the impact of
the Covid-19 global pandemic in 2020 and 2022)
4.2.1.2 Research Format of publications
The publication format is another criterion that can
map the selected literature into different categories.
Figure 2 below shows the contribution based on the
format type in which the papers are published. The
primary format of this study was conference papers,
which stand at 74% of the total selected literature,
while 22% were articles in journals, and only 4%
were workshops.
Category
Description
Validatio
n
Validations are done for
techniques that might be novel or
have not yet been implemented in
practice. These techniques are
validated using experiments, i.e.,
work done in the lab.
Evaluatio
n
Techniques are implemented in
practice, and the method is
evaluated. It shows how the
process is executed (solution
implementation) in practice and
the consequences of the
implementation evaluation.
Solution
A solution to a problem is
proposed. The solution can be
either novel or a significant
extension of an existing
technique. The potential benefits
and the applicability of the
solution are shown by a small
example or a good argumentation
line.
Survey
A survey reviews other primary
or secondary studies relating to a
specific research question to
integrate/synthesise evidence
associated with a particular
research question.
Opinion
These papers express personal
opinions on whether a specific
technique is good or bad or how
things should be done. They do
not rely on related work and
research methodologies.
Experienc
e
Experience papers explain what
and how something has been
done in practice. It is related to
the personal experience of the
author.
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Fig. 2: Research Format
4.2.1.3 Research Approach
The papers were grouped based on the contribution
type (as noted in Table 4 above): experience,
validation, evaluation, solution, survey, and opinion.
Figure 3, below, visualises the categories and clearly
shows that the minor contribution is the 'survey' type
of research approach; more survey research is
needed in this field and other research approaches.
Fig. 3: Research categories
4.2.2 Topic-specific Data Extraction
This extraction answers the systematic mapping
research questions and focuses on the selected
papers' keywords and content. These are directly
keyword-related extractions, the correlation of the
research topics and research approaches.
Fig. 4: Keywords extraction and frequency
4.2.2.1 Keyword Extraction and Frequency
Keyword extraction categorises the selected papers
based on their topics. Another category was
developed that mapped the selected papers into a
group either because they had a similar research
topic or similar keywords. This yielded 16 groups, as
shown in Figure 4.
4.2.2.2 Main Research Topic
The keywords of the already collated and selected
publications were further regrouped. Figure 5
visualises the main research topics from the selected
papers. It could be observed that the majority of the
research topic focused on architecture and less on
multi-tenancy or security.
Fig. 5: Keywords grouping extraction and frequency
4.2.2.3 Correlation between Studies based on the
Extracted Keywords
The correlation between studies and extracted
keywords was done by correlating them to
investigate linearly related or correlated. Figure 6
visualises the correlation between the keywords and
the topics. It should be noted that none of the
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considered studies included topics concerning cloud
quality. In addition, this keyword categorisation may
not be detailed enough for use in some cases and
detailed and specific analyses, such as cost-
effectiveness, energy usage, forensics, or
cybersecurity.
Fig. 6: Further keywords extraction and correlation
Figure 7 shows the mapping of the research
approach to the research topic and the specific area
to which the study contributes. Both figures show
what has been done and can still be improved.
Fig. 7: Mapping the research approach to the
research topics
4.3 Study Quality
The quality of the study is based on the selection of
the contributing papers. The selected research
papers, including journal articles and conference and
workshop papers, were accessed from reputable
databases and were peer-reviewed. Books, keynotes
and magazines were not used in this research
because they are often based on the writers' views or
past reviews. This does not suggest that those
materials do not contribute to research, but they did
not meet the inclusion criteria here.
4.4 Threats of Validity
Combining cloud-native architecture and multi-
tenancy raises validity threats, common in systematic
mapping. The threats that might have affected this
research include theoretical validity, which might
have been evident in the study during data selection
and extraction. This was reduced by allowing the
second researcher to review the extraction, but, given
human judgment, this kind of threat cannot be
eliminated, [21]. Another threat is interpretive
validity, which relates to the conclusion, and the
value of the information provided. Data and
information provided in the research interpretation
may be biased because the study focuses on multi-
tenancy in cloud-native architecture rather than other
cloud computing properties.
The study used easy-to-read guidelines, suggested in
[21], which help reduce personal and technical bias
about the content and the systematic mapping data.
Descriptive validity could be less evident because it
does not consider detailed observations and the
studies' objectives. In addition, readers need to
understand that it is possible to be biased in the
research scope or focus here because the study
targeted specific properties in the mapping. This may
provide a bias in the general mapping of the study.
This study focuses more on the security issues
related to multi-tenancy in cloud-native architecture
rather than the cost, energy usage and computer
hardware usage.
5 Discussion
5.1 Research Question 1: What Does the
Term Multi-Tenancy in Cloud-Native
Architecture (CNA) Mean?
Cloud-native technology considers the isolation from
the physical system, kernel-based orchestration,
software development methodology (DevOps), and
micro-servicing architecture. Many early academic
papers such as (S40), (S39), and (S44) discussed
how to migrate from the legacy cloud computing
characteristics to the cloud-native paradigm.
However, less of the research shows the challenges
of the cloud-native paradigm when considering
isolation
and sharing. Multi-tenancy in cloud-native
architecture was explained in this research as an
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architecture that enables sharing the same service
instance among different tenants, which is done by
sharing hardware, instances, clusters, namespace,
pods or microservices between different tenants. The
research indicates that the contributions focus of
multi-tenancy in cloud-native architecture falls
mainly into the research approach categories'
experiment' and 'solution'. (S1), (S2), (S4), (S6),
(S7), and several other study papers, as indicated in
Table 7 below, covered the experiment and
solutions. In [9] the author indicated that the
contribution type of their selected papers are mostly
solution and experience papers. In agreement, [86],
also show the same in their review of the Cloud
container technologies review, which shows fewer
opinion or survey types of contribution. To further
understand multi-tenancy in cloud-native
architecture, there is a need to understand the
differences between multi-tenancy in cloud-native
architecture and software orientation architecture.
Both isolation and sharing in CNA can be addressed
based on different forms of role, permission, and
access, which are different in other software multi-
tenancy. Table 6 shows the differences between
software-oriented and cloud-native architecture, and
these differences introduce the ideas about the
classification of tenants more than the 'owner' and
'tenant' relationship.
Table 6. Sharing mode and Isolation in cloud
computing
According to [70], the container provides better
isolation than the virtual machine. The virtual
machine isolation is operating-system-based, while
containers provide isolation at every instance of
virtualisation, such as the process level, the file
system level, the network level and the inter-process
communication level. Multi-tenancy in CNA also
provides enhanced sharing capability based on the
provided isolation. Containers enable significant
resource savings by isolating the application process
while sharing part of the operating system such as
the kernel, libraries and other processes concurrently
running on the machine because it was built on top
of namespaces (S15) and (S35).
5.2 Research Question 2: What are the
Existing Trends in Cloud-Native Multi-
Tenancy?
CNA properties, such as elasticity, auto-scaling,
horizontal and vertical scaling and automation, and
including multi-tenancy, bring several trending
characteristics and technologies to empirical
research. For this reason, this research question
considered cloud-native multi-tenancy with other
trending concepts. Table 6 identifies some of the
trending concepts found in the research. Readers
should note that these were not necessarily the only
trending considerations at the research time. Table 7
serves to identify a few of the critical examples
related to this research and the combination of both
cloud-native and multi-tenant architecture.
5.3 Research Question 3: What are the
Foreseeable Challenges of Multi-Tenancy in
Cloud-Native Architecture?
The challenges of multi-tenancy in CNA are
inherited from the SOA multi-tenancy issues and
some additional challenges. Challenges such as the
incomplete implementation of system resource
isolation mechanisms in the Linux kernel posed
security concerns for multiple container sharing in an
operating system kernel (S12), (S35). Listed below
are some of the challenges of multi-tenancy in CNA.
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Table 7. Trending concepts
Trending concepts
Definition
Hard multi-tenancy
Hard multi-tenancy means that multiple tenants in the same cluster
should not have access to anything from other tenants [87]. This concept
is extensively discussed in Kubernetes, a container orchestrator. Hard
multi-tenancy can be explained as the outright isolation of one tenant
from the other, and the method considers any tenant in a given cluster as
a potential malicious tenant.
Soft multi-tenancy
This is another concept popularly known in the Kubernetes multi-tenant
consideration, where users are not considered actively malicious since
they are within the same organisation. However, as soon as they leave
the organisation, they become a potential risk that brings security threats
to the cluster members. It is worthy of note that soft multi-tenancy
mainly focus on the preventive mechanism and not the defensive [87].
Stateless isolation
Isolation is one of the basic requirements to be met when customising
multi-tenant SaaS (S32) in cloud-native architecture. This concept
became essential due to the stateless protocol in cloud computing, in
which a client request is dealt with by the server with the previous
configuration and provides automatic scalability, which reduces resource
usage. This creates the need for a specific type of isolation that will
make commands sent from a given tenant to the cloud computing server
without affecting another tenant or instance.
Lodger
Cloud-native architecture has stretched the multi-tenancy of cloud
computing to a depth that brings into reality a different kind of tenant,
called the 'lodger'. Cloud-native architecture has become the latest
upgrade that cloud computing service providers and cloud computing
users consider due to its ability to improve productivity through
scalability and automation. In an IaaS model of cloud computing, the
service provider can host an instant either in containers or virtual
machines resold to the reseller (partner) customers, which involves a
bilateral, multi-round negotiation [88]. Furthermore, the customer
(tenant of tenant) of the reseller can be addressed as a 'lodger' if
considered from the perspective of the real estate industry from which
the term 'multi-tenant' was initially borrowed. Lodgers pose a security
threat in architecture.
Migration
Migration into the cloud-native architecture has had limited research [9].
(S38) described the migration of existing legacy software and associated
customers with perpetual licences and the adoption of cloud-native
Software by new customers with no existing economic relationship with
the service provider. Recent research is working on this area as multi-
tenancy affects how instances can be moved in a data centre (S9), (S7)
and (S45).
5.3.1 Isolation
Isolation is a significant dimension in cloud security
issues, requiring a vertical solution from the SaaS
layer down to physical infrastructure to develop
physical-like boundaries among tenants, instead of
the virtual limitations currently applied (S12).
According to (S12) incomplete implementation of
isolation in containers is another major challenge in
container-based virtualisation. The more inadequate
isolation makes it more susceptible to access into the
bare-metal host system from the containers than
from the hypervisor in a virtual machine (S43),
(S32). Kubernetes, one of the orchestration software
packages, cannot guarantee secured isolation
between tenants; it offers features that may be
sufficient for specific use cases with multi-tenancy in
mind. Nevertheless, isolation is achievable in
container technology through Namespace as the
implementor and Cgroups as the Control (S19),
(S23). It needs a more complex configuration of
policies such as the pod security policy and network
policy, improved scheduling policy/algorithms
(S19), and the use of namespaces to provide a clear
boundary between nodes.
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5.3.2 Malicious User
Multi-tenancy security is complex because both the
malicious user and the actual user are on the same
server. This is why avoidance of security is possible
in multi-tenancy, as it is not designed to infiltrate the
inside part of servers, and is ability is limited to the
boundaries of the network layer [13]. In agreement,
(S10) described the imminent threat around the
container-based cloud deployments in a multi-tenant
environment as a threat posed by an instance where a
malicious or attacker container and non-malicious
container reside in the same host operating system
and are running on the same container engine. Cloud
security deployment could be based on how strongly
the container engine can implement identity
management.
5.3.3 Migration
In cloud computing, migration can be described as
moving from a legacy system to cloud computing or
moving from one deployment model to another in a
CNA. It can also be considered as the movement of
part of the data in the cloud computing paradigm,
called portability, through the moving of pods,
clusters, containers, and users. Based on isolation
and multi-tenancy, migration becomes a challenge.
Moving different parts of the prominent structure
takes time and resources (S9). However, migration
should be a first-class notion in the system, having
the same stature as scalability, consistency, fault-
tolerance, and functionality. Nonetheless, migration
will not be easily achieved if multi-tenancy is not
solved in the CNA implementation's architecture
stage. Those challenges are being solved by
modification and customisation. Organisations using
CNA provide their own do-it-yourself (DIY)
approaches, such as the Alibaba virtual cluster,
which implements multi-tenancy in a containerised
orchestration application, Kubernetes. Alibaba
virtual cluster achieves this by implementing
complex customisation and the application's
adjustments, including interface, API, and design
parameters. According to the [18], Kubernetes is not
multi-tenancy-enabled at default and to achieve a
multi-tenancy containerised cloud architecture,
multiple configurations and customisation are
needed, and so is the use of tools such as
namespaces, network policies, resource quotas and
isolation systems, such as the sandbox and sole
tenant nodes provided and being improved.
6 Conclusion and Future Research
Multi-tenancy is a key characteristic of software-
oriented architecture, but according to the review it
is not thoroughly considered in the cloud-native
architecture which seems that the multi-tenancy in
cloud-native seem to be different to concept in
software-oriented architecture because of the
differences in style, component, and principle of
cloud native architecture. This review may not be
able to justify the differences based on the scope of
this research, further research work in this area is
needed. Our discussion shows some the trending
concepts in multi-tenancy within cloud-native
architecture that brings about improvement to the
cloud computing paradigm in general and also
challenges that are introduce. We identify
challenges such as migration, malicious user and
isolation issues which has some interest but not
maturity of research area is quite low. Overall, based
on analysis and classification, further research is
needed to provide a detailed understanding of multi-
tenancy in cloud-native architecture through
experiments and case studies. The experiment-based
findings will make cloud computing security,
isolation and migration in cloud-native architecture
less complicated. Secondly, future research should
investigate other cloud computing properties such as
interoperability and portability in cloud-native
architecture. Which will remove the gap in
knowledge about the adoption of cloud-native
architecture and technology and increase the
adoption of cloud computing and cloud-native-based
technology in small, medium and large-scale
enterprises.
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