Cloud management and monitoring: a systematic mapping study

Indonesian Journal of Electrical Engineering and Computer Science
Vol. 21, No. 3, March 2021, pp. 1648~1662
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v21.i3.pp1648-1662  1648
Journal homepage: http://ijeecs.iaescore.com
Cloud management and monitoring: a systematic mapping
study
Isaac Odun-Ayo1
, Toro-Abasi Williams2
, Jamaiah Yahaya3
1,2
Department of Computer and Information Sciences, Covenant University, Nigeria
3
Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
Article Info ABSTRACT
Article history:
Received May 12, 2020
Revised Sep 15, 2020
Accepted Nov 5, 2020
A key component of ensuring that services are available on the cloud at the
right time in the right manner is adequate cloud management. This makes it
possible to provide services that meets user demands. The purpose of this
research is to carry out a systematic study of management and monitoring on
the cloud. Three facets were applied in conducting the categorization. These
are the contribution, research, and topic facets. The purpose was to determine
the level of work so far carried out in the field of cloud management. This
enabled the creation of a pictorial representation of the research coverage. The
result of the study showed that there are no opinion research on cloud
management. Generally, articles on experience research, philosophical
research and metric are the lowest at 6.62%, 4.41% and 1.90% respectively,
while articles on models, solution research and evaluation research are the
highest with 52.38%, 46.32% and 31.62% respectively. The outcome of this
study will stimulate further research in the area cloud management and
systematic studies.
Keywords:
Cloud computing
Cloud management
Cloud monitoring
Service level agreements
Systematic mapping
This is an open access article under the CC BY-SA license.
Corresponding Author:
Isaac Odun-Ayo
Department of Computer and Information Sciences
Covenant University, Ota, Nigeria
Email: isaac.odun-ayo@covenantuniversity.edu.ng
1. INTRODUCTION
A unique aspect of the cloud is that the user can participate in the management of cloud activities
albeit in a very limited manner. In view of the massive infrastructure on the cloud, cloud computing lends itself
to various forms of management. Autonomic form allows activities to run with little or no human interaction.
Adaptive nature of the cloud allows flexibility in user operation. Service level agreements (SLA) monitoring
deals with ascertaining that tested metrics meet the required standards [1]. Autonomic communication services
has the capacity to start and end a requested process based on the network services and operating environment
[2].
Cloud computing fully lends itself to self-service on the part of the user and there are cloud-monitoring
tools for this purpose. They ensure a mutually beneficial operation; there are SLA’s that determine the nature
of contract between the CSP and the consumer. For a SaaS provider to guarantee smooth operations, there are
several critical quality of service (QoS) parameters that must be considered in a service requiring provisioning
such as response time [3]. There are other metrics that must be considered such as availability and uptime, list
of services and resources being offered by the CSP to the user [4].
On the cloud, monitoring has various levels which help to determine the status of the physical
infrastructure [5]. The issues of management on the cloud and its attend realization of satisfactory SLA is of
prime importance in cloud computing [6, 7]. Cloud monitoring and management is vital to offering data as it

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Cloud management and monitoring: a systematic mapping study (Isaac Odun-Ayo)
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relates to performance and availability of service on the cloud relevant to provisioning in real time in ensuring
that service demands are met [8]. It also supports the provisioning of virtualized resources and ensuring the
configuration process is automated. Autonomic computing is another aspect of cloud management that
provides efficient service level agreement (SLA) centred on a systems ability to automatically handle resources
and meeting requirements [9]. There are autonomic managers, analyzers, and reconfiguration mangers that
supports SLA, analyze monitoring data and generates reconfiguration actions [9]. In the traditional method,
allocation of resources are not scalable because the management is centralised making them unsuitable the
cloud environment [10]. It therefore becomes pertinent to develop systems that are not centralized and capable
of meeting the demands of cloud systems and applications. There is the possibility of showing that resources
are scalable in terms of the number cloud servers and the amount of applications to be placed on such cloud
servers, making it possible to optimize the numbers of servers to be deployed on a particular domain [10]. From
the foregoing, it is obvious that cloud management and monitoring is an area of cloud computing that is worth
studying. The essence of conducting this research is that there is still a need for more papers in the area of
cloud management and monitoring. Hence, the research is conducted to identify areas where papers are lacking
and make them available to prospective researches. A lot of papers have already been written, however it is
important to provide on overview and summary of such work. A systematic study helps in summarizing what
has been done in a field of study and also putting it in a pictorial form. The aim of the research is therefore to
carry out a study on monitoring and management on the cloud. This paper contributes to knowledge by
producing percentages and a visual map indicating the extent of work that has been done using indices such as
research and contribution in cloud management and monitoring. The rest of the paper is as follows: In Section
2, the related work is discussed. In Section 3, the materials and method is presented. The result and discussion
is presented in Section 4, while the paper is concluded in Section 5.
2. RELATED WORK
The papers in [11, 12] focuses primarily on guidelines for conducting a systematic literature review.
Several studies in the areas of systematic mapping studies were examined and lessons were drawn from such
studies. Such lessons offered insight to guide studies in the practice of designing systematic maps.
The work in [13] focuses on the requirement engineering process. The work dealt with identifying
software patterns during a software development activity. The paper examined parameters related to these
patterns and how they can be subsequently replicated in further research in this area of study.
The paper in [14] conducted a Cloud based IoT-enabled solid waste monitoring system for smart and
connected communities. In this paper, an intelligent solid waste monitoring system is developed using internet
of things (IoT) and cloud computing technologies. Waste containers are strategically situated within the
communities and the fill level of solid waste in each of the containers is detected using ultrasonic sensors. The
sensor data is transmitted to an IoT cloud platform, ThingSpeak, via a wireless fidelity (Wi-Fi) communication
link.
The work carried out in [15] focused on maps relating to concepts in Computer Science. The
contribution was the examinations of papers dealing with Computer Science concept maps. A review on the
subject was also depicted in terms of teaching and learning supports. To enhance the search backward
snowballing was employed, and major digital databases were used on the search string.
The paper in [16] examined the concept of composition, virtualization, orchestration and virtualization
using a systematic mapping study. Six features were considered in the classification process which are
development, virtualization, composition, rationalization and centralization. The paper centered on producing
the map using the contribution and research facets which examined method and tool, and validation and
solution research respectively.
The primary focus of the paper in [17] has to do with designs on the cloud and development models.
A review was done with unique features to examine extent of study in this field. The six features employed
were service development, designs, implementation, privacy, configuration and security. The protocol was
applied on the standard research and contribution categories. In the research category, experience, validation,
opinion and solution research were discussed, while tool, method and model was examined in the contribution
category.
The paper in [18] dealt with cloud-based testing. A review was done with the empirical aspect of this
software process. The classification process focused on non-functional and functional testing methods. The
methods were subjected to statistical analysis with results contributing to knowledge in this field. Sixty nine
(69) primary studies was used in the examination process of the proposed solution, extracted from major digital
databases.
In [19], a review of testing based on cloud mobile application was carried out. The systematic mapping
study provides result relating to testing of mobile cloud-based applications. The classification scheme used

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features such as compatibility testing, securing testing, GUI testing and functional testing. These features were
used in the contribution categories to examine metric, framework, tool, model and method. In addition, Testing-
as-a-Service was done on topics in the contribution facet. The research facet focused on validation, evaluation
and solution research types.
The paper in [20] did a review in the software engineering domain. It dealt with the lessons that accrue
from software engineering systematic literature review process. Several works were examined in this domain
and the lessons that were learnt from the experience were systematically summarized in a map. Such lessons
would have useful applications to the practice of software engineering.
The paper in [21] also carried out a systematic literature review in the area of software engineering.
In this instance, the focus was on assessing the impact of such review using evidence-based process in
contributing to knowledge. Relevant materials were drawn from both journal and conference papers.
The work in [22] is a study on the software measurement process in software engineering. A review
was done based on measuring software quality model were discussed. The classification process considered
intervention, population, outcome and comparison. The software quality model was examined in terms of
ISO/IECSQuaRE and ISO/IEC 9126. The result indicated that the ISO SQuaRE was more suitable.
The work in [23] surveyed various monitoring tools. The paper conducted a comprehensive survey of
on the objectives and capabilities of tools for monitoring on the cloud. A taxonomy on the importance of the
monitoring tools was carried out including an analysis. It was concluded that cloud specific monitoring tools
are platform dependent and proprietary.
The work in [24] is focused on quality of service (QoS) as it relates to SLA on the cloud. It examine
how service compositions can be managed in terms of self-service resources. The work discussed the
properties, designs, structure in terms of service components required for managing runtime with a bid to
providing personalized services to meet SLA’s. An architecture relating to service components was defined for
constructing discovery of services, adapting to SLAs and creating QoS components to ensure that service
components are available for different functionalities. Clearly, there were no papers in the area of cloud
management based on systematic studies.
3. MATERIALS AND METHOD
3.1. The systematic mapping process
The systematic mapping study on cloud management and monitoring as shown in Figure 1, utilized
the steps provided in which served as guidelines [11, 12]. A systematic mapping study is repetitive in nature
meant to examine the extracted publications based on the objectives of study [25]. All the steps for carrying
out a systematic were utilized in creating a systematic map on cloud management and monitoring.
Figure 1. The process of systematic mapping [11]
3.2. Definition of research questions
The research questions are as follows:
RQN1: What aspects of cloud management and monitoring are considered and the number of papers
discussed in different areas ?
RQN2: Which form of articles constitute publications in this field and in particular what evaluation
and novelty do they constitute?
RQN3: What methods of research were used in the studies and what was the level of contributions?
3.3. Conduct of search for primary studies
Five (5) digital electronic libraries as shown in Table 1, were used because they have journals and
conference papers with high impact factor. The major libraries are ACM, IEEEXplore, Science Direct, Springer
and Scopus. The keyword used is based on the various aspects of cloud management associated with the title

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of this work. The starting point of the studies is examining relevant digital databases for the appropriate papers.
In addition, the backward snowballing process is adopted to refine the search [26]. In this particular study on
cloud management and monitoring, the string adopted for search on the digital databases is as follows:
(TITLE (“CLOUD management’’) OR TITLE (service level agreement “) OR TITLE (“SLA’’) AND (TITLE
(adaptive) or TITLE (monitoring) OR TITLE (autonomic) AND (KEY (CLOUD) OR KEY (SLA)
Table 1. Digital libraries
Electronic Databases URL
IEEE https://ieeexplore.ieee.org/Xplore/home.jsp
Springer https://link.springer.com/
Science Direct https://www.sciencedirect.com/
ACM https://dl.acm.org/
Scopus https://scopus.com.
3.4. Screening of papers for inclusion and exclusion
The inclusion and exclusion criteria as shown in Table 2, was employed to exempt topics not relevant
to cloud management, and papers that were not in conformity with the questions of the research. Abstracts that
mention only the main focus of this research without providing in-depth details were removed. This study did
not include presentation slides, summaries, tutorials, editorials, panel discussions and prefaces. Articles that
had this study as its primary focus with some additional secondary aspects of this paper were also considered.
The Appendix contains the list of primary studies.
Table 2. Exclusion and inclusion criteria
Inclusion Criteria Exclusion Criteria
Abstract explicitly mentions management and monitoring, as it
relates to clouds. Furthermore, such abstracts that relates to SLA
and autonomic.
The abstract does not relate to cloud computing. Furthermore,
there are no discussions related to management and monitoring
on the cloud.
3.5. Keywording of abstracts
Keywords from the various articles relating to cloud management was combined to ensure proper
understanding of types of research and contributions. The outcome of this was used to determine the set of
categories adopted in this study. In this study, three facets were adopted. The first facet focused on the topic,
which was derived from the keyword and the constituent parts of the title of this work, the types of contributions
were discussed in the second facet as related to this research, and the third facet involves research issues.
3.6. Research and contributions descriptions
This research facet used the approaches for research classification as enunciated in [27].
 Validation Research: The techniques used in the research are unique but not yet implemented. No
experiments are conducted.
 Evaluation Research: The techniques outlined had been implemented and evaluated. There are results
discussing the benefits or otherwise.
 Solution Proposal: The technique proposes a unique guidance to an issue. The value of such solution are also
discussed.
 Philosophical Papers: The research offers new ways to solve a problem by proffering concepts and
framework.
 Opinion Papers: Opinions are expressed not based on any method of research, but still provides valuable
insights.
 Experience papers: An author’s personal experience is provided. Such experience details how things can be
done.
These categories were considered appropriate for use in this study. This was used as part of the
classification scheme; hence articles used for this study were classified using the different research categories.
The aspect of contribution considered the topics listed [19]:
 Framework: A well-structured and detailed method, with wide scope and purpose, focusing on a number of
research questions or areas.
 Model: Provides an abstraction view of a topic and problems rather than a tangible and specific approach for
solving specific problem.
 Tool: Provides means of evaluating a concept using specific tool.

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 Evaluation: A technique used for empirically measuring the proposed solution(s).
 Metric: Provides guidelines for measuring particular phenomena.
 Method: Focuses on a more specific goal with a narrow research question or purpose.
3.7. Data extraction and mapping of studies
Data extraction was done using a Microsoft Excel table for the classification scheme categories. The
extent of publication in the contribution and research facets were extracted on different microsoft excel tables.
The overall frequency of publication was obtained from a combined chart for the topic, research and
contribution aspects. The analysis was centered around frequency depictions of publication for categories
within the scheme. The essence of this was to determine which aspects of management and monitoring on
cloud was emphasized more in the study. This enables the determination of gaps and it provided a means to
recommend more studies.
Bubble plots were created to present the frequencies of articles which is the map. The intersection of
the categories had a two x-y scatter plot with bubbles used to create the map. The bubble coordinates have
bubble sizes that correspond to the frequency of publication in that category. There are two quadrants due to
the fact that more than one facet was used. The different quadrants provided information relating to the topics,
research and contributions areas of the study. Hence, it becomes easy to visualize the two quadrants at the same
time. In addition, relevant statistics were added to the bubbles providing a quick overview of the study on cloud
management.
4. RESULTS AND DISCUSSION
4.1. Contribution and topic category
The main focus of analysis are the topics extracted form the keywords. During the classification
process, the extracted features for this study are:
 SLA monitoring
 Security
 Autonomous management
 Self-adaptive SLA
 Architectures
Table 3 depicts the primary studies selected in relation to the contribution and topics categories, while
Figure 2 indicates the topics’ percentage in the research category. The map of management and monitoring on
the cloud is shown in Figure 4. On the left quadrant of the x-axis of Figure 4, is the the contribution facet’s
results. The contribution facet dealt with the types of contribution in the papers included in this study. On Table
3, the result indicated that articles that out of 105 papers examined, only 1.9% discussed metric in relation to
cloud management. Also, tool had 17.14%, model had 52.38%, method had 13.33% and process had 15.24%.
Simulations. The left quadrant of Figure 4 indicates the dynamics of publications between the contribution and
topics facet. For example, model contributed 52.38% of the articles considered. The breakdown in relation to
the topic facet shows that 1.9% of model contributions were on simulation, 11.43% were on architectures,
8.57% were on self-adaptive SLA, and 2.86% were on autonomous management, 14.29% on security and
13.33% on SLA monitoring. Other aspects of the contribution category as it relates to topic is as shown in
Figure 4.
Table 3. Topic and contribution primary studies
Contribution
Facet
Topic
Metric Tool Model Method Process
SLA Monitoring 3, 11, 14, 15, 31, 34, 37, 39, 57,
61, 73, 81, 104, 124,
2, 7, 8, 6, 10, 123
Security 103 13, 19, 38, 56, 77, 80,
109, 119, 120, 121,
4, 5, 12, 26, 36, 45, 58, 59, 70, 78,
115, 117, 118, 134, 136
22, 35, 98,
99, 133
49
Autonomous
Management
17, 20, 127 27, 30, 46, 89, 92
Self-Adaptive SLA 28, 42, 101, 107, 132 18, 43, 44, 87, 88, 95, 100, 108,
129
50, 54, 97 60, 62, 83
Architecture 63 1, 9, 55, 66, 74, 106, 112, 116,
131, 122, 130, 135
114
Simulations 110, 128, 71, 65 64, 67, 68, 72,
75, 76, 91
Percentage 1.90% 17.14% 52.38% 13.33% 15.24%

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Figure 2. Percentage of topics on contribution category
4.2. Topic and research type category
Table 4 depicts the topics and research category in relation to primary studies selected for this paper,
while Figure 3, indicates the research category’s percentage in terms of topics. On Table 4, the result shows
that out of 136 papers reviewed in the research facet, evaluation research had 31.62%. In addition, solution
research had 46.31%, validation research had 11.03%, experience had 6.62% and philosophical had 4.41%.
There was no result for opinion research. On the x-axis of the right quadrant of Figure 4, is the result of the
type of research carried out in the area of cloud management and monitoring issues. The right quadrant of
Figure 4 indicated the relationship between the topics and research type facet. From Figure 4, out of the 136
papers reviewed on cloud management, 46.32% of the papers were on solution research. The breakdown shows
that 5.15% of solution research was on simulation, 1.47% on architectures, 9.56% on self-adaptive SLAs,
2.21% on autonomous management, 14.71% on security, and 13.24% on SLA monitoring. Other aspects of the
research type category are in Figure 4.
Table 4 Topic and Research Primary Studies
Research
Facet
Topic
Evaluation Validation Solution Philosophical Experience Opinion
SLA Monitoring 2, 10, 11, 25, 31, 39,
81, 85, 96, 102, 104,
124
29, 125 3, 7, 8, 14, 15, 16, 21,
23, 24,32, 33, 34, 37, 57,
61, 73, 111, 123
6 90
Security 4, 36, 78, 80, 99,
117, 118, 119, 136
47, 48, 49,
51, 53, 56,
5, 12, 13, 19, 35, 38, 41,
45, 58, 59, 70, 77, 103,
109, 113, 115, 120, 121,
133, 134
94, 98 22, 26
Autonomous
Management
17, 89, 92, 127, 69 30, 46, 52, 20, 126 27
Self-Adaptive
SLA
18, 28, 42, 129, 132 43, 44, 101 50, 54, 60, 62, 79, 82,
83, 87, 88, 95, 100, 107,
108
97
Architecture 1, 66, 106, 112, 116,
122, 135,
55, 74 9, 131
Simulations 40, 75, 76, 91, 93,
105,
130 71, 72, 84, 86, 110, 114,
128,
63, 64, 65,
67, 68,
Percentage 31.62% 11.03% 46.32% 4.41% 6.62% 0%

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Figure 3. Percentage of Topics on Research Category
4.3. Findings
The focus of the cloud management and monitoring systematic mapping study on is thematic analysis,
classification, and also identifying the publication areas. From the analysis, gaps were identified through
graphing; this indicated which topic areas has a shortage of articles. Conversely, the map showed the areas that
were sufficiently examined in the primary studies. In producing the systematic map and showing the
frequencies, the category of assessment was at the highest level.
The left and right quadrant of the systematic map in Figure 4, is a two x-y scatter plot with bubbles at
the intersection of the topic and contribution facets, and the topics and research facet respectively. It can been
seen from the map that there were more publications on tool as it relates to security (9.52%), more publications
on model in terms of security (14.29%), more articles on method as it relates to security (4.76%) and more
papers on process as it relates to simulation (6.67%). Similarly, on the right quadrant it can be seen that more
papers discussed SLA monitoring as it relates to evaluation research, more articles discussed security in terms
of solution and evaluation research with 6.62% and 14.71% respectively. Furthermore, more papers on
simulation with respect to experience research (3.68%) were recorded. At a glance, it was depicted that there
were generally more articles on cloud monitoring as it relates to security.
On the other hand, to the best of the authors’ knowledge, there were no publications in the area of
simulation, self-adaptive SLA, autonomous management and SLA monitoring on tool as a contribution. In
addition, there were no publications that focused on process in terms of architectures and lack of papers on
autonomous management in the area of method. On the right quadrant, there were no publications on
philosophical research in terms of simulations and architectures on cloud management. There were no articles
on experience research in the area of architectures and self-adaptive SLA. Interestingly, there were no opinions
on cloud management. Generally, articles on validation, philosophical and evaluation research were the lowest.
5. CONCLUSION
Cloud computing has continued to evolve in different topic areas. This evolution has led to volumes
of publications and articles providing insight into various aspects of the cloud. Despite the quantity of
publications, several areas still have shortage of articles. A classification scheme was used to extract data in
the area of cloud management and monitoring. Based on the categories produced in the classification, a
systematic map was created using a two x-y scatter plot with bubbles. This visual representation allows
researchers to observe frequencies of publications with an indication of areas where there is shortage of
publications. This outcome provides vast opportunities for further research. This research paper will certainly
contribute to broadening the frontiers of knowledge in cloud computing. This is because it uncovered gaps in
the area of monitoring and management on the cloud that had not been explored by many researchers.

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ACKNOWLEDGEMENT
We acknowledge the support and sponsorship provided by Covenant University through the Centre
for Research, Innovation, and Discovery (CUCRID).
APPENDIX - LIST OF PRIMARY STUDIES
1. Abrahao, B., Almeida, V., Almeida, J., Alex, Z., Beyer, D., Safai, F., “Self-adaptive SLA-driven capacity management
for internet services, IEEE Symposium Record on Network Operations and Management Symposium, art. no. 1687584,
pp. 557-568, 2006.
2. Aghera, P., Chaudhary, S., Kumar, V., “An approach to build multi-tenant SaaS application with and SLA,”
Proceedings - International Conference on Communication Systems and Network Technologies, CSNT 2012, art. no.
6200688, pp. 658-661, 2012.
3. Al Falasi, A., Serhani, M.A., Dssouli, R., “A model for multi-levels SLA monitoring in federated cloud environment,”
Proceedings - IEEE 10th International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and IEEE
10th International Conference on Autonomic and Trusted Computing, ATC 2013, art. no. 06726231, pp. 363-370,
2013.
4. Al-Ghuwairi, A.-R., Salah, Z., Alsarhan, A., Al Qudah, S., Al Qahmous, G., Baarah, A., Al-Oqaily, A., “Monitoring
and modelling service level agreement of multiple virtual machines in cloud computing,” International Journal of
Business Information Systems, vol. 27, no. 4, pp. 538-553, 2018.
5. Al-Shammari, S., Al-Yasiri, A., “MonSLAR: A middleware for monitoring SLA for RESTFUL services in cloud
computing,” 2015 IEEE 9th International Symposium on the Maintenance and Evolution of Service-Oriented Systems
and Cloud-Based Environments, MESOCA 2015 - Proceedings, art. no. 7328126, pp. 46-50, 2015.
6. Alsulaiman, L.A., Alturki, R., “Monitoring multimedia quality of service in public cloud service level agreements,”
Proceedings of 2012 International Conference on Multimedia Computing and Systems, ICMCS 2012, art. no.
6320195, pp. 605-609, 2012.
7. Anithakumari, S., Chandrasekaran, K., “Negotiation and monitoring of service level agreements in cloud computing
services,” Advances in Intelligent Systems and Computing, vol. 469, pp. 651-659, 2017.
8. Anithakumari, S., Chandrasekaran, K., “Monitoring and Management of Service Level Agreements in Cloud
Computing,” Proceedings - 2015 International Conference on Cloud and Autonomic Computing, ICCAC 2015, art.
no. 7312156, pp. 204-207, 2015.
9. Anithakumari, S., Chandra Sekaran, K., “Autonomic SLA Management in Cloud Computing Services,”
Communications in Computer and Information Science, 420 CCIS, pp. 151-159, 2014.
10. Ardagna, D., Trubian, M., Zhang, L., “SLA based resource allocation policies in autonomic environments,” Journal
of Parallel and Distributed Computing, vol. 67, no. 3, pp. 259-270, 2007.
11. Balis, B., Slota, R., Kitowski, J., Bubak, M., “On-line monitoring of service-level agreements in the grid,” Lecture
Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics), 7156 LNCS (PART 2), pp. 76-85, 2012.
12. Bertolino, A., Calabrò, A., De Angelis, G., “Adaptive SLA monitoring of service choreographies enacted on the
cloud,” c2013 IEEE 7th International Symposium on the Maintenance and Evolution of Service-Oriented and Cloud-
Based Systems, MESOCA 2013, art. no. 6632741, pp. 92-101, 2013.
13. Bertolino, A., Calabrò, A., De Angelis, G., “A generative approach for the adaptive monitoring of SLA in service
choreographies,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics), 7977 LNCS, pp. 408-415, 2013.
14. Bertolino, A., De Angelis, G., Sabetta, A., Elbaum, S., “Scaling up SLA monitoring in pervasive environments,”
ESSPE '07 - International Workshop on Engineering of Software Services for Pervasive Environments - In conjunction
with the 6th ESEC/FSE Joint Meeting, pp. 65-68, 2007.
15. Binu, V., Gangadhar, N.D., “A cloud computing service level agreement framework with negotiation and secure
monitoring,” 2014 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2014, art. no.
7015474, 2015.
16. Blatchley, C.C., “Effects of cosmic rays on SLA wear monitoring Technical Program for MFPT 2012,” The
Prognostics and Health Management Solutions Conference - PHM: Driving Efficient Operations and Maintenance,
p.6, 2012.
17. Bobelin, L., Bousquet, A., Briffaut, J., “An autonomic Cloud management system for enforcing security and assurance
properties,” CLHS 2015 - Proceedings of the 2015 Workshop on Changing Landscapes in HPC Security, Part of
HPDC 2015, art. no. 2752500, pp. 1-8, 2015.
18. Bonvin, N., Papaioannou, T.G., Aberer, K., “Autonomic SLA-driven provisioning for cloud applications,”
Proceedings - 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2011, art.
no. 5948634, pp. 434-443, 2011.
19. Brandic, I., Music, D., Leitner, P., Dustdar, S., “VieSLAF framework: Enabling adaptive and versatile sla-
management,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics), 5745 LNCS, pp. 60-73, 2009.
20. Breskovic, I., Maurer, M., Emeakaroha, V.C., Brandic, I., Dustdar, S., “Cost-efficient utilization of public SLA
templates in autonomic cloud markets,” Proceedings - 2011 4th IEEE International Conference on Utility and Cloud
Computing, UCC 2011, art. no. 6123502, pp. 229-236, 2011.

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21. Brogi, A., Carrasco, J., Cubo, J., D'Andria, F., Ibrahim, A., Pimentel, E., Soldani, J., “SeaClouds: Seamless adaptive
multi-cloud management of service-based applications,” CIBSE 2014: Proceedings of the 17th Ibero-American
Conference Software Engineering, pp. 95-108, 2014.
22. Bruneo, D., Fritz, T., Keidar-Barner, S., Leitner, P., Longo, F., Marquezan, C., Metzger, A., Pohl, K., Puliafito, A.,
Raz, D., Roth, A., Salant, E., Segall, I., Villari, M., Wolfsthal, Y., Woods, C., “CloudWave: Where adaptive cloud
management meets DevOps,” Proceedings - International Symposium on Computers and Communications,
Workshops, art. no. 6912638, 2014,
23. Bustos-Jimenez, J., Ramiro, V., Lalanne, F., Barros, T., “Adkintun: SLA monitoring of isp broadband offerings,”
Proceedings - 27th International Conference on Advanced Information Networking and Applications Workshops,
WAINA 2013, art. no. 6550599, pp. 1445-1449, 2013.
24. Casola, V., De Benedictis, A., Rak, M., “Security monitoring in the cloud: An SLA-based approach,” Proceedings -
10th International Conference on Availability, Reliability and Security, ARES 2015, art. no. 7299988, pp. 749-755.,
2015
25. Celaya, J., Sakellariou, R., “An adaptive policy to minimize energy and SLA violations of parallel jobs on the cloud,”
Proceedings - 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, UCC 2014, art. no.
7027541, pp. 507-508, 2014.
26. Cheng, X., Shi, Y., Li, Q, “A multi-tenant oriented performance monitoring, detecting and scheduling architecture
based on SLA,” 2009 Joint Conferences on Pervasive Computing, JCPC 2009, art. no. 5420114, pp. 599-604., 2009
27. Chhetri, M.B., Vo, Q.B., Kowalczyk, R., “CL-SLAM: Cross-layer SLA monitoring framework for cloud service-
based applications,” Proceedings - 9th IEEE/ACM International Conference on Utility and Cloud Computing, UCC
2016, pp. 30-36, 2016.
28. Chhetri, M.B., Vo, Q.B., Kowalczyk, R., “Adaptive AutoSLAM - Policy-based orchestration of SLA establishment,”
Proceedings - 2014 IEEE International Conference on Services Computing, SCC 2014, art. no. 6930569, pp. 472-
479, 2014.
29. Cicotti, G., Coppolino, L., D’Antonio, S., Romano, L., “Runtime model checking for sla compliance monitoring and
qos prediction,” Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, vol. 6,
no. 2, pp. 4-20, 2015.
30. Clark, K.P., Warnier, M., Brazier, F.M.T., “Self-adaptive service level agreement monitoring in cloud environments,”
Multiagent and Grid Systems, vol. 9, no. 2, pp. 135-155, 2013.
31. Clark, K.P., Warnier, M.E., Brazier, F.M.T., Quillinan, T.B., “Secure monitoring of service level agreements,” ARES
2010 - 5th International Conference on Availability, Reliability, and Security, art. no. 5438056, pp. 454-461, 2010.
32. Coppolino, L., De Mari, D., Romano, L., Vianello, V., “SLA compliance monitoring through semantic processing,”
Proceedings - IEEE/ACM International Workshop on Grid Computing, art. no. 5697975, pp. 252-258, 2010.
33. Dastjerdi, A.V., Tabatabaei, S.G.H., Buyya, R., “A dependency-aware ontology-based approach for deploying service
level agreement monitoring services in Cloud Software,” Practice and Experience, vol. 42, no. 4, pp. 501-518, 2012.
34. De Turck, F., Vanhastel, S., Backx, P., Duysburgh, B., Demeester, P., “Design of a generic architecture for service
management and monitoring of service level agreements through distributed intelligent agents,” IEEE Intelligent
Network Workshop, Proceedings, pp. 50-57, 2001.
35. Dekeris, B., Narbutaite, L., Adomkus, T., “A new Adaptive Fair Queueing (AFQ) scheduler for support SLA,”
Proceedings of the International Conference on Information Technology Interfaces, ITI, art. no. 4283839, pp. 597-
602, 2007.
36. Ding, J., Zhao, Z., “Towards autonomic SLA management: A review,” 2012 International Conference on Systems
and Informatics, ICSAI 2012, art. no. 6223574, pp. 2552-2555, 2012.
37. Dong, W., “SLA monitoring based on semantic web,” Communications in Computer and Information Science, vol.
34, pp. 21-29, 2009.
38. Emeakaroh, V.C., Brandic, I., Netto, M.A.S, Derose, C.A.F., “Application-level monitoring and sla violation detection
for Multi-tenant cloud services,” Emerging Research in Cloud Distributed Computing Systems, pp. 157-186, 2015.
39. Emeakaroha, V.C., Ferreto, T.C., Netto, M.A.S., Brandic, I., De Rose, C.A.F., “CASViD: Application level
monitoring for SLA violation detection in clouds,” Proceedings - International Computer Software and Applications
Conference, art. no. 6340204, pp. 499-508, 2012.
40. Emeakaroha, V.C., Netto, M.A.S., Calheiros, R.N., Brandic, I., Buyya, R., De Rose, C.A.F., “Towards autonomic
detection of SLA violations in Cloud infrastructures,” Future Generation Computer Systems, vol. 28, no. 7, pp. 1017-
1029, 2012.
41. Engel, R., Chen, B., Rajamoni, S., Ludwig, H., Keller, A., Mohamed, M., Tata, S., “Domain-independent monitoring
and visualization of SLA metrics in multi-provider environments: (Short Paper),” Lecture Notes in Computer Science
(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10573 LNCS, pp.
628-638, 2017.
42. Fakhfakh, K., Chaari, T., Tazi, S., Drira, K., Jmaiel, M., “A comprehensive ontology-based approach for SLA
obligations monitoring,” Proceedings - The 2nd International Conference on Advanced Engineering Computing and
Applications in Sciences, ADVCOMP 2008, art. no. 4641021, pp. 217-222, 2008.
43. Farrell, A.D.H., Sergot, M.J., Salle, M., Bartoiini, C., Trastour, D., Christodoulou, A., “Performance monitoring of
service-level agreements for utility computing using the Event Calculus,” Proceedings - First IEEE International
Workshop on Electronic Contracting, WEC 2004, pp. 17-24, 2004.

1657
44. Haase, P., Mathäß, T., Schmidt, M., Eberhart, A., Walther, U., “Semantic technologies for enterprise cloud
management,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics), 6497 LNCS (PART 2), pp. 98-113, 2010.
45. Haberland, B., Derakhshan, F., Grob-Lipski, H., Klotsche, R., Rehm, W., Schefczik, P., Soellner, M., “Radio base
stations in the cloud,” Bell Labs Technical Journal, vol. 18, no. 1, pp. 129-152, 2013.
46. Hahanov, V., “Cyber physical computing for IoT-driven services,” Cyber Physical Computing for IoT-driven Services,
pp. 1-279, 2018.
47. Hahanov, V., Mishchenko, O., Litvinova, E., Chumachenko, S., “Big Data Driven Smart Cyber University,”
Proceedings - 2016 IEEE World Congress on Services, SERVICES 2016, art. no. 7557412, pp. 134-141, 2016.
48. Hahm, J., Lim, S.B., Li, G., Cho, H., Shin, S.C., Yeom, J., Lee, J., “Function and performance test of open source
cloudstack platform for HPC service,” Journal of Next Generation Information Technology, vol. 5, no. 2, pp. 56-66,
2014.
49. Hajjem, L., Abdallah, S.B., “Resources management in cloud computing,” 2016 6th International Workshop on
Computer Science and Engineering, WCSE 2016, pp. 599-602, 2016.
50. Han, F., See, K., Feng, Y., Yu, X., Yi, X., “Online SoC estimation for Li-ion batteries: A survey explore the distributed
secure cloud management to battery packs,” Proceedings of the 2017 12th IEEE Conference on Industrial Electronics
and Applications, ICIEA 2017, 2018-February, pp. 1838-1843, 2018.
51. Han, H., Kim, S., Jung, H., Yeom, H.Y., Yoon, C., Park, J., Lee, Y. A, “RESTful approach to the management of
cloud infrastructure,” CLOUD 2009 - 2009 IEEE International Conference on Cloud Computing, art. no. 5284212,
pp. 139-142, 2009.
52. Hanawa, T., Sato, M., “D-cloud: Software testing environment for dependable distributed systems using cloud
computing technology,” Cloud Technology: Concepts, Methodologies, Tools, and Applications, vol. 4, pp. 2307-2322,
2014.
53. Hanawa, T., Sato, M., “D-cloud: Software testing environment for dependable distributed systems using cloud
computing technology,” Software Testing in the Cloud: Perspectives on an Emerging Discipline, pp. 340-355, 2012.
54. Hanawa, T., Koizumi, H., Banzai, T., Sato, M., Miura, S., Ishii, T., Takamizawa, H., “Customizing virtual machine
with fault injector by integrating with specc device model for a software testing environment D-cloud,” Proceedings
- 16th IEEE Pacific Rim International Symposium on Dependable Computing, PRDC 2010, art. no. 5703226, pp. 47-
54, 2010.
55. Hanawa, T., Banzai, T., Koizumi, H., Kanbayashi, R., Imada, T., Sato, M., “Large-scale software testing environment
using cloud computing technology for dependable parallel and distributed systems,” ICSTW 2010 - 3rd International
Conference on Software Testing, Verification, and Validation Workshops, art. no. 5463684, pp. 428-433, 2010.
56. Hamze, M., Mbarek, N., Togni, O., “Autonomic Brokerage Service for an End-to-End Cloud Networking Service
Level Agreement,” Proceedings - IEEE 3rd Symposium on Network Cloud Computing and Applications, NCCA 2014,
art. no. 06786763, pp. 54-61, 2014.
57. Hamze, M., Mbarek, N., Togni, O. “Self-establishing a service level agreement within autonomic cloud networking
environment,” IEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management
in a Software Defined World, art. no. 6838336, 2014.
58. Hanif, M., Yoon, H., Jang, S., Lee, C. “An adaptive SLA-based data flow mechanism for stream processing engines,”
International Conference on Information and Communication Technology Convergence: ICT Convergence
Technologies Leading the Fourth Industrial Revolution, ICTC 2017, 2017-December, pp. 81-86, 2017.
59. Hasan, Racz, P., Stiller, B., “Monitoring of SLA compliances for hosted streaming services,” 2009 IFIP/IEEE
International Symposium on Integrated Network Management, IM 2009, art. no. 5188817, pp. 251-258, 2009.
60. He, H., Ma, Z., Chen, H., Yeh, C.Y., Shao, W., “An aspect-oriented approach to SLA-driven monitoring multi-tenant
cloud application,” IEEE International Conference on Cloud Computing, CLOUD, art. no. 6973824, pp. 857-864,
2014.
61. Herssens, C., Faulkner, S., Jureta, I.J., “Context-driven autonomic adaptation of SLA,” Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5364
LNCS, pp. 362-377, 2008.
62. Hussain, S., Gustavsson, R., Saleem, A., Nordstrom, L., “SLA conceptual framework for coordinating and monitoring
information flow in Smart Grid,” 2014 IEEE PES Innovative Smart Grid Technologies Conference, ISGT 2014, art.
no. 6816470, 2014.
63. Hussain, S., Gustavsson, R., Saleem, A., Nordstrom, L. A., “SLA conceptual framework for monitoring QoS in smart
grid,” 2013 IEEE Grenoble Conference PowerTech, POWERTECH 2013, art. no. 6652467, 2013.
64. Hussain, S., Gustavsson, R., Saleem, A., Nordstrom, L., “Service level agreement: Coordination and monitoring of
actors in Smart Grid,” IEEE PES Innovative Smart Grid Technologies Conference Europe, art. no. 6465862, 2012.
65. Hussain, W., Hussain, F.K., Hussain, O.K., “Maintaining trust in cloud computing through SLA monitoring,” Lecture
Bioinformatics), no. 8836, pp. 690-697, 2014.
66. Hussin, M., Muhammed, A., Abd Hamid, N.A.W., “Adaptive multi-tenancy policy for enhancing service level
agreement through resource allocation in cloud computing,” International Journal of Computer Networks and
Communications, vol. 8, no. 4, pp. 59-69, 2016.
67. Kaaniche, N., Mohamed, M., Laurent, M., Ludwig, H., “Security SLA based monitoring in clouds,” Proceedings -
2017 IEEE 1st International Conference on Edge Computing, EDGE 2017, art. no. 8029261, pp. 90-97, 2017.

 ISSN: 2502-4752
1658
68. Kailasam, S., Gnanasambandam, N., Dharanipragada, J., Sharma, N. “Optimizing service level agreements for
autonomic cloud bursting schedulers, Proceedings of the International Conference on Parallel Processing Workshops,
art. no. 5599085, pp. 285-294, 2010.
69. Kaminski, H., Perry, M. “A framework for autonomic SLA creation,” Proceedings of the IADIS International
Conference on e-Society 2008, 2008, pp. 379-386.
70. Katsikogiannis, G., Mitropoulos, S., Douligeris, C., “Optimizing SLA-driven adaptive routing,” Proceedings - IEEE
Symposium on Computers and Communications, 2016-August, art. no. 7543807, 2016, pp. 627-632.
71. Katsikogiannis, G., Mitropoulos, S., Douligeris, C., “Policy-based QoS management for SLA-driven adaptive
routing,” Journal of Communications and Networks, vol. 15, no. 3, art. no. 6559356, pp. 301-311, 2013.
72. Kertesz, A., Kecskemeti, G., Brandic, I. “An interoperable and self-adaptive approach for SLA-based service
virtualization in heterogeneous cloud environments,” Future Generation Computer Systems, vol. 32, no. 1, pp. 54-68,
2014.
73. Kertesz, A., Kecskemeti, G., Oriol, M., Kotcauer, P., Acs, S., Rodríguez, M., Mercè, O., Marosi, A.C., Marco, J.,
Franch, X., “Enhancing federated cloud management with an integrated service monitoring approach,” Journal of
Grid Computing, vol. 11, no. 4, pp. 699-720, 2013.
74. Kertész, A., Kecskeméti, G., Brandic, I., “Autonomic SLA-Aware service virtualization for distributed systems,
Proceedings - 19th International Euromicro Conference on Parallel, Distributed, and Network-Based Processing,
PDP 2011, art. no. 5739040, pp. 503-510, 2011.
75. Kosiński, J., Nawrocki, P., Radziszowski, D., Zieliński, K., Zieliński, S., Przybylski, G., Wnȩk, P., “SLA monitoring
and management framework for telecommunication services, Proceedings - 4th International Conference on
Networking and Services, ICNS 2008, art. no. 4476554, pp. 170-175, 2008.
76. Labidi, T., Mtibaa, A., Gaaloul, W., Tata, S., Gargouri, F., “Cloud SLA modeling and monitoring,” Proceedings -
2017 IEEE 14th International Conference on Services Computing, SCC 2017, art. no. 8035003, pp. 338-345, 2017.
77. Li, X., Du, J. “Adaptive and attribute-based trust model for service-level agreement guarantee in cloud computing,”
IET Information Security, vol. 7, no. 1, pp. 39-50, 2013.
78. Liong, Y.-L., Li, M., “Problems/issues in 3G QoS service level agreement monitoring,” Proceedings of the
International Conference on Wireless Networks, ICWN'04, 1, 2004, pp. 219-226.
79. Liu, H., Gu, W., Zhang, J., Zhao, W., Chen, Y., Chen, X., Zhang, H., Wang, L., “Optical service level agreement
based on service plane of adaptive multi-service optical network,” Proceedings of SPIE - The International Society
for Optical Engineering, no. 7137, art. no. 71370S, 2008,
80. Liu, X., Xu, F., “Cloud service monitoring system based on SLA,” Proceedings - 12th International Symposium on
Distributed Computing and Applications to Business, Engineering and Science, DCABES 2013, art. no. 6636434, pp.
137-141, 2013.
81. Maarouf, A., El Hamlaoui, M., Marzouk, A., Haqiq, A., “Combining multi-agent systems and MDE approach for
monitoring SLA violations in the cloud computing,” Proceedings of 2015 International Conference on Cloud
Computing Technologies and Applications, CloudTech 2015, art. no. 7336975, 2015.
82. Maarouf, A., Hamlaoui, M.E., Haqiq, A., “MDE approach for the establishment of a service level agreements
monitoring by trusted third party in the cloud computing,” International Journal of Grid and High Performance
Computing, vol. 7, no. 4, pp. 1-20, 2015.
83. Maurer, M., Brandic, I., Sakellariou, R., “Self-adaptive and resource-efficient SLA enactment for cloud computing
infrastructures,” Proceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012, art. no.
6253527, pp. 368-375, 2012.
84. Maurer, M., Brandic, I., Sakellariou, R., “Simulating autonomic SLA enactment in clouds using case based reasoning,”
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics), no. 6481 LNCS, pp. 25-36, 2010.
85. Meng, S., Liu, L., “Enhanced monitoring-as-a-service for effective cloud management,” IEEE Transactions on
Computers, vol. 62, no. 9, art. no. 6231620, pp. 1705-1720, 2013.
86. Mohamed, M., Anya, O., Sakairi, T., Tata, S., Mandagere, N., Ludwig, H., “The rSLA framework: Monitoring and
enforcement of service level agreements for cloud services,” Proceedings - 2016 IEEE International Conference on
Services Computing, SCC 2016, art. no. 7557507, pp. 625-632, 2016.
87. Molina-Jimenez, C., Shrivastava, S., Crowcroft, J., Gevros, P., “On the monitoring of contractual service level
agreements,” Proceedings - First IEEE International Workshop on Electronic Contracting, WEC 2004, 2004, pp. 1-
8.
88. Moustafa, S., Elgazzar, K., Martin, P., Elsayed, M., “SLAM: SLA monitoring framework for federated cloud
services,” Proceedings - 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing, UCC 2015,
art. no. 7431465, pp. 506-511, 2015.
89. Nafarieh, A., Sivakumar, S., Robertson, W., Phillips, W., “Enhanced adaptive SLA-aware algorithms for provisioning
shared mesh optical networks,” Procedia Computer Science, vol. 19, pp. 494-502, 2013.
90. Nadgowda, S., Suneja, S., Bila, N., Isci, C., “Voyager: Complete container state migration,” Proceedings -
International Conference on Distributed Computing Systems, art. no. 7980161, pp. 2137-2142, 2017.
91. Nadgowda, S., Jayachandran, P., Verma, A., “I2Map: Cloud disaster recovery based on image-instance mapping,”
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics), no. 8275 LNCS, pp. 204-225, 2013.

1659
92. Nagalakshmi, A., Joglekar, S., “Collective intelligence applications - Algorithms and visualization,” Proceedings -
2nd International Conference on Emerging Applications of Information Technology, EAIT 2011, art. no. 5734910,
2011, pp. 25-30.
93. Naik, P., Shaw, D.K., Vutukuru, M. “NFVPerf: Online performance monitoring and bottleneck detection for NFV,”
2016 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2016, art. no.
7919491, 2017, pp. 154-160.
94. Namjoshi, J., Wadia, Y., Gaonkar, R., “Portable autoscaler for managing multi-cloud elasticity,” Proceedings - 2013
International Conference on Cloud and Ubiquitous Computing and Emerging Technologies, CUBE 2013, art. no.
6701474, pp. 48-51, 2013.
95. Nassar, R., Simoni, N., “Cloud management architecture INNGN/NGS context: QoS-awareness, location-awareness
and service personalization,” CLOSER 2011 - Proceedings of the 1st International Conference on Cloud Computing
and Services Science, 2011, pp. 629-635.
96. Nenadic, I., Kobal, D., Palata, D., “About the telco cloud management architectures,” 2016 39th International
Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2016 -
Proceedings, art. no. 7522203, 2016, pp. 545-552.
97. Nguyen, A.Q., Tantar, A.-A., Bouvry, P., Talbi, E.-G., “Design of an energy efficiency model and architecture for
cloud management using prediction models,” 2013 International Conference on Soft Computing and Pattern
Recognition, SoCPaR 2013, art. no. 7054153, 2013, pp. 333-336.
98. Nicoletti, L., Margheri, A., Lombardi, F., Sassone, V., Schiavo, F.P., “Cross-cloud management of sensitive data via
blockchain: A payslip calculation use case,” CEUR Workshop Proceedings, no. 2058, 2018.
99. Nayak, D., Martha, V.S., Threm, D., Ramaswamy, S., Prince, S., Fatimberger, G., “Adaptive scheduling in the cloud
- SLA for Hadoop job scheduling,” Proceedings of the 2015 Science and Information Conference, SAI 2015, art. no.
7237240, pp. 832-837, 2015.
100. Nemati, H., Singhvi, A., Kara, N., El Barachi, M., “Adaptive SLA-based elasticity management algorithms for a
virtualized IP multimedia subsystem,” 2014 IEEE Globecom Workshops, GC Wkshps 2014, art. no. 7063377, pp. 7-
11, 2014.
101. Nobakht, B., De Gouw, S., De Boer, F.S., “Formal verification of service level agreements through distributed
monitoring,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics), no. 9306, pp. 125-140, 2015.
102. Parkin, S.E., Morgan, G., “Toward reusable SLA monitoring capabilities,” Software - Practice and Experience, vol.
42, no. 3, pp. 261-280, 2012.
103. Paschke, A., Bichler, M., Dietrich, J., “ContractLog: An approach to rule based monitoring and execution of service
level agreements,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence
and Lecture Notes in Bioinformatics), no. 3791 LNCS, pp. 209-217, 2005.
104. Paul, M., Das, A. “An Innovative SLA-Based service monitoring framework in cloud, Journal of Information and
Knowledge Management. Article in Press, 2018.
105. Petcu, D., Panica, S., Irimie, B., Macariu, G., “On security SLA-based monitoring as a service,” Lecture Notes of the
Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 169, pp.
326-336, 2016.
106. Petcu, D., “A taxonomy for SLA-based monitoring of cloud security,” Proceedings - International Computer Software
and Applications Conference, art. no. 6899283, pp. 640-641, 2014.
107. Petcu, D., Crǎciun, C., “Towards a security SLA-based cloud monitoring service, CLOSER 2014 - Proceedings of the
4th International Conference on Cloud Computing and Services Science, 2014, pp. 598-603.
108. Petcu, D. “SLA-based cloud security monitoring: Challenges, barriers, models and methods,” Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
no. 8805 (Part 1), pp. 359-370, 2014.
109. Qiang, W., Xu, G., Sun, G., Zhu, T., Jin, H., “CloudController: A writable and heterogeneous-adaptive virtual machine
introspection for cloud management,” Proceedings - 16th IEEE International Conference on Trust, Security and
Privacy in Computing and Communications, 11th IEEE International Conference on Big Data Science and
Engineering and 14th IEEE International Conference on Embedded Software and Systems,
Trustcom/BigDataSE/ICESS 2017, art. no. 8029438, pp. 177-184, 2017.
110. Raouyane, B., Errais, M., Bellafkih, M., Ranc, D., “SLA management & monitoring based-eTOM and WS-composite
for IMS networks,” 2011 4th IFIP International Conference on New Technologies, Mobility and Security, NTMS
2011 - Proceedings, art. no. 5721150, 2011.
111. Rea, L., Mammi, E., “Italian QoS monitoring network: Impact on SLA control,” Proceedings of 2012 15th
International Telecommunications Network Strategy and Planning Symposium, NETWORKS 2012, art. no. 6381666,
2012.
112. Repp, N., Miede, A., Niemann, M., Steinmetz, R., “WS-Re2Policy: A policy language for distributed SLA monitoring
and enforcement,” Proc. - The 3rd Int. Conf. Systems and Networks Communications, ICSNC 2008 - Includes I-
CENTRIC 2008: Int. Conf. Advances in Human-Oriented and Personalized Mechanisms, Technologies, and Services,
art. no. 4693680, pp. 256-261, 2008.
113. Rios, E., Mallouli, W., Rak, M., Casola, V., Ortiz, A.M., “SLA-Driven Monitoring of multi-cloud application
components using the MUSA framework,” Proceedings - 2016 IEEE 36th International Conference on Distributed
Computing Systems Workshops, ICDCSW 2016, art. no. 7756209, pp. 55-60, 2016.

 ISSN: 2502-4752
1660
114. Rubach, P., Sobolewski, M., “Dynamic SLA negotiation in autonomic federated environments,” Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
5872 LNCS, pp. 248-258, 2009.
115. Rubach, P., Sobolewski, M., “Autonomic SLA management in federated computing environments,” Proceedings of
the International Conference on Parallel Processing Workshops, art. no. 5366097, pp. 314-321, 2009.
116. Sagbo, K.A.R., Houngue, Y.P.E., Damiani, E., “SLA negotiation and monitoring from simulation data,” Proceedings
- 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016, art. no.
7907553, pp. 766-772, 2017.
117. Samreen, F., Blair, G.S., Rowe, M. “Adaptive decision making in multi-cloud management,” Proceedings of the 2nd
International Workshop on Cross-Cloud Systems, CrossCloud Brokers 2014 - Held in conjunction with the 15th
ACM/IFIP/USENIX International Middleware Conference, Middleware 2014, art. no. 4, 2014.
118. Schoonjans, A., Van Landuyt, D., Lagaisse, B., Joosen, W., “On the suitability of black-box performance monitoring
for SLA-driven cloud provisioning scenarios,” Proceedings of the 14th Workshop on Adaptive and Reflective
Middleware, ARM 2015 - Collocated with ACM/IFIP/USENIX Middleware 2015, art. no. 6, 2015.
119. Shin, S.-C., Shin, S.-Y., Han, S.-Y., “Network performance monitoring system for SLA: Implementation and
practices,” 6th International Conference on Advanced Communication Technology: Broadband Convergence Network
Infrastructure, vol. 2, pp. 661-664, 2004.
120. Słota, R., Nikolow, D., Młocek, P., Kitowski, J., “Semantic-based SLA monitoring of storage resources,” Lecture
Bioinformatics), 7204 LNCS (PART 2), pp. 232-241, 2012.
121. Sommers, J., Barford, P., Duffield, N., Ron, A., “Accurate and efficient SLA compliance monitoring,” ACM
SIGCOMM 2007: Conference on Computer Communications, pp. 109-120, 2007.
122. Sommers, J., Barford, P., Duffield, N., Ron, A., “A framework for multi-objective SLA compliance monitoring,”
Proceedings - IEEE INFOCOM, art. no. 4215880, pp. 2446-2450, 2007.
123. Son, S., Kang, D.-J., Huh, S.P., Kim, W.-Y., Choi, W., “Adaptive trade-off strategy for bargaining-based multi-
objective SLA establishment under varying cloud workload,” Journal of Supercomputing, vol. 72, no. 4, pp. 1597-
1622, 2016.
124. Son, S., Sim, K.M., “An adaptive tradeoff algorithm for multi-issue SLA negotiation,” Communications in Computer
and Information Science, 121 CCIS, pp. 32-41, 2010.
125. Spillner, J., Hoyer, J., “SLA-driven service marketplace monitoring with grand slam,” ICSOFT 2009 - 4th
International Conference on Software and Data Technologies, Proceedings, no. 2, pp. 71-74, 2009.
126. Suresh, S., Sakthivel, S., “SLA guaranteed contention-aware adaptive resource management with dynamic scheduling
policies VMM of virtualized clusters for enterprise cloud computing,” International Journal of Applied Engineering
Research, vol. 9, no 24, pp. 26079-26098, 2014.
127. Ta, X., Mao, G., “Online end-to-end quality of service monitoring for service level agreement management,”
International Journal of Communication Systems, vol. 2, no. 4, pp. 383-404.
128. Taher, Y., Haque, R., Nguyen, D.K., Finance, B., “PAEAN4CLOUD a framework for monitoring and managing the
SLA violation of cloud service-based applications,” CLOSER 2014 - Proceedings of the 4th International Conference
on Cloud Computing and Services Science, 2014, pp. 361-371.
129. Tan, W., Sun, Y., Li, L., Tang, A. “Multivariate quality control chart for monitoring SLA of workflow applications,”
Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design,
CSCWD 2014, art. no. 6839905, 2014, pp. 667-671.
130. Teshome, A., Rilling, L., Morin, C., “Including security monitoring in cloud service level agreements,” Proceedings
of the IEEE Symposium on Reliable Distributed Systems, art. no. 7794346, pp. 209-210, 2016.
131. Toueir, A., Broisin, J., Sibilla, M., “A goal-oriented approach for adaptive SLA monitoring: A cloud provider case
study,” 2nd IEEE Latin American Conference on Cloud Computing and Communications, LatinCloud 2013, art. no.
6842223, pp. 53-58, 2013.
132. Tunc, C., Fargo, F., Al-Nashif, Y., Hariri, S., Hughes, J., “Autonomic resilient cloud management (ARCM) design
and evaluation,” Proceedings - 2014 International Conference on Cloud and Autonomic Computing, ICCAC 2014,
art. no. 7024043, pp. 44-49, 2015.
133. Ullah, K.W., Ahmed, A.S., “Demo paper: Automatic provisioning, deploy and monitoring of virtual machines based
on security service level agreement in the cloud,” Proceedings - 14th IEEE/ACM International Symposium on Cluster,
Cloud, and Grid Computing, CCGrid 2014, art. no. 6846490, 2014, pp. 536-537.
134. Xu, C.-Z., Rao, J., Bu, X., “URL: A unified reinforcement learning approach for autonomic cloud management,”
Journal of Parallel and Distributed Computing, vol. 72, no. 2, pp. 95-105, 2012.
135. Yin, Y., Zhou, J., Zhang, C., Chen, D., “Adaptive SLA mechanism for service sharing in virtual environments,”
Kybernetes, vol. 45, no. 7, pp. 1036-1051, 2016.
136. Zhang, Y., Liu, H., Lu, Y., Deng, B., “SLA-driven state monitoring for cloud services,” Proceedings - 2013 IEEE
International Conference on High Performance Computing and Communications, HPCC 2013 and 2013 IEEE
International Conference on Embedded and Ubiquitous Computing, EUC 2013, art. no. 6831950, pp. 428-433, 2014.
REFERENCES
[1] Clark, K. P., Warnier, M., and Brazie, F. M., “Self-Adaptive Service level Agreement Monitoring in cloud
Environments,” Multiagent and Grid Systems, vol. 9, no. 2, pp. 135-155, 2013.

1661
[2] Samimi F.A., McKinley P.K., Sadjadi S.M. “Mobile Service Clouds: A Self-Managing Infrastructure for Autonomic
Mobile Computing Services,” In: Keller A., Martin-Flatin JP. (eds) Self-Managed Networks, Systems, and Services.
SelfMan 2006. Lecture Notes in Computer Science, vol 3996. Springer, Berlin, Heidelberg, 2006.
https://doi.org/10.1007/11767886_10.
[3] Wu, L., Garg, S. K., and Buyya, R., “SLA-based admission control for a Software-as-a-Service provider in Cloud
computing environments,” Journal of Computer and System Sciences, vol. 78, no. 5, pp. 1280-1299, 2012.
[4] Chandana, O.R., Divya, C., Drunda, B.V., and Rajeshwari, B.V., “A study on service level agreement management
technique in the cloud,” International Journal of Advance Research in Computer Science and Management Studies,
vol 5, no. 5 May, 2017.
[5] Alhamazami, K., et al, “An Overview of the Commercial Cloud Monitoring Tools: Research Dimensions, Design
Issues, and State-of-the-Art,” Computing, vol. 97, no. 4, pp. 357-377, 2015.
[6] Odun-Ayo I., Odede B., Ahuja R. “Cloud Applications Management – Issues and Developments,” In: Gervasi O. et
al. (eds) Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer
Science, vol 10963. Springer, Cham, 2018. https://doi.org/10.1007/978-3-319-95171-3_54.
[7] I. Odun-Ayo, O. Ajayi and N. Omoregbe, "Cloud Service Level Agreements – Issues and Development," 2017
International Conference on Next Generation Computing and Information Systems (ICNGCIS), Jammu, 2017, pp. 1-
6, doi: 10.1109/ICNGCIS.2017.18.
[8] Xu, C., Rao, J., and Bu, x., “URL: A Unified reinforcement learning approach for autonomic cloud management,”
Journal of Parallel and Distributed Computing, vol. 72, no. 2, pp. 95-105, 2012.
[9] Mohamed, M, Belaïd, D., and Tata, S., “Extending OCCI for autonomic management in the cloud,” The Journal of
Systems and Software journal, vol. 122, pp.416-429, 2016.
[10] Barshan, M., Moens, H., Latré, S., Volckaert, B., and Turck, F., “Algorithms for network-aware application
component placement for cloud resource allocation,” Journal of Communications and Networks, vol. 19, no. 5, pp.
493-508, 2017.
[11] Petersen, K., Feldt, R., Mujtaba S., and Matisson, M., “Systematic Mapping Studies In Software Engineering,”
EASE'08 Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering,
Italy ,June 26 - 27, 2008, pp. 68-77
[12] Petersen, K., Vakkalanka, S., and Kuzniarz, L., “Guidelines for conducting systematic mapping studies in software
engineering: An update, Information and Software Technology,” Information and Software Technology, vol. 64, pp.
1-18, 2015.
[13] J. L. Barros-Justo, A. L. Cravero-Leal, F. B. Benitti, and R. Capilla-Sevilla, “Systematic mapping protocol: the
impact of using software patterns during requirements engineering activities in real-world settings,” Cornell
University Library, arXiv:1701.05747v1 [cs.SE], 2017.
[14] Atayero, A.A., Williams, R., Badejo, J.A., Popoola, S.I. “Cloud based IoT-enabled solid waste monitoring system
for smart and connected communities” International Journal of Civil Engineering and Technology, 10 (2), pp. 2308-
2315, 2019
[15] V. Santos, E. F. Souza, K. R. Felizardo, and N. L. Vijaykumar, “Analyzing the use of concept maps in computer
science: A systematic mapping study,” Informatics in Education, vol. 16, no 2, pp. 257–288, 2017.
doi:10.15388/infedu.2017.13.
[16] Odun-Ayo I., Geteloma V., Eweoya I., Ahuja R., “Virtualization, Containerization, Composition, and Orchestration
of Cloud Computing Services,” In: Misra S. et al. (eds) Computational Science and Its Applications – ICCSA 2019.
ICCSA 2019. Lecture Notes in Computer Science, vol 11622. Springer, Cham, 2019. https://doi.org/10.1007/978-3-
030-24305-0_30
[17] I. Odun-Ayo, R. Goddy-Worlu, V. Samuel, and V. Geteloma, “Cloud Design and Deployment Models: A Systematic
Mapping Study,” BMC Reseaerch Notes, vol. 12, no. 1, p. 436, 2019.
[18] Ahmad, P. Brereton, and P. Andras, “A systematic mapping study of empirical studies on software cloud testing
methods,” 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C),
Prague, 2017, pp. 555-562.
[19] B.I. Ya'u, et al, “A systematic mapping study on cloud-based mobile application testing,” Journal of Information
and Communication Technology, vol. 18, no. 4, pp. 485-527, 2019
[20] P. Brereton, B. A. Kitchenham, D. Budgen, M. Turner, and M. Khalil, “Lessons from applying the systematic
literature review process within the software engineering domain,” Journal of Systems and Software, vol. 80, no. 4,
pp. 571-583, 2007.
[21] Kitchenham, B., and Characters, S. (2007) “Guidelines for performing systematic literature review in software
engineering,” Ver. 2.3 EBSE Technical Report, vol. 5, 2007.
[22] T. Wahyuningrum, and K. Mustofa, “A systematic mapping review of software quality measurement: research trends,
model, and method,” International Journal of Electrical and Computer Engineering, vol. 7, no. 5, pp. 2847-2854,
2017.
[23] Fatema, K., Emeakaroha, V.C., Hearly, P.D., Morrison, J.P., and Lynn, T., “A survey of cloud monitoring tools:
Taxonomy, capabilities, and objectives,” Journal of Parallel and Distributed Computing, vol. 74, no. 10, pp. 2918-
2933, 2014.
[24] Aubonnet, T., Henrio, L., Kessal, S., Kulankhina, O., Lemoine, F., Eric, M., Ruz, C., and Simoni, N., “Management
of service composition based on self-controlled components,” Journal of Internet Services and Applications, vol. 6,
no. 1, pp. 1-17, 2015.
[25] Muhammed A.B., and Muhammed, A.C., “A systematic mapping study of software architectures for cloud based
systems,” Ph.D. dissertation, IT University of Copenhagen, 2014.

 ISSN: 2502-4752
1662
[26] Wohlin, C. “Guidelines for snowballing in systematic literature studies and a replication in software engineering,”
In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering (EASE
'14). Association for Computing Machinery, New York, NY, USA, Article 38, pp. 1–10, 2014.
DOI:https://doi.org/10.1145/2601248.2601268.
[27] Wieringa, R., Maiden, M.A.M., Mead N.R., and Rolland, C., (2006) “Requirement engineering paper classification
and evaluation criteria. A proposal and a discussion,” Requirement Engineering, vol. 11, pp. 102-107, 2006.
BIOGRAPHIES OF AUTHORS
Isaac A. Odun-Ayo was born in Ilesha, Nigeria in 1962. He received the B.S, M.S and Ph.D
degrees in Computer Science from the University of Benin, Benin City, Nigeria. Between 2010
and 2013 he was a faculty and Director Information and Communication Technology at the
National Defence College, Abuja, Nigeria. He joined the faculty of Covenant University, Ota,
Nigeria as a Senior Lecturer in October 2016. He is the author of one book and more than 40
journal and conference articles on Cloud Computing. His research interest include cloud
computing, human resource management, e-governance and software engineering. Dr. Odun-Ayo
is a recipient of the National Productivity Order of Merit Award, Nigeria for his contribution to
computing. He is a member of the Nigeria Computer Society (NCS), Computer Professionals of
Nigeria (CPN), International Association of Engineers (IAENG), Institute of Electrical and
Electronics Engineers (IEEE) and Member Information Science Institute (ISI).
Toro-Abasi Williams is a research assistant and a postgraduate student at the Department of
Computer and Information Sciences, Covenant University, Nigeria. He takes tutorials for several
courses at the post-graduate level. He has a passion for academics and research in computer
science. Williams has some publications in cloud computing His research interests include cloud
computing, mobile computing, artificial intelligence and software engineering.
Jamaiah Yahaya is Associate Professor at Faculty of Information Science and Technology
(FTSM), The National University of Malaysia (UKM) since July, 2011. Prior that she worked as
a senior lecturer in School of Computing, Northern University of Malaysia (UUM) and a system
analyst at University of Science Malaysia. Her bachelor degree was BSc in Computer Science and
Mathematics from University of Wisconsin-La Crosse, USA (1986), MSc in Information System
from University of Leeds, UK (1998), and PhDin Computer Science from The National University
of Malaysia (UKM) (2007). Her PhD thesis was the development of software certification model
and later, she continued her PhD research as a post-doctoral fellow in UKM (2008). Currently she
is the head of PhD program in FTSM, UKM. Her research interests are software quality, software
development and management, and software assessment and impact. She is an active researcher
with more than 100 publications in international journals and proceedings for the last 5 years.

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