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Cloud Management Optimization

Authors:
Abstract Cloud computing is the current technology
paradigm that portends the greatest potentials to revolutionize
the way IT activities are conducted. Cloud computing
influences most known areas of activities in human endeavour.
The cloud provides easy to use applications that can be
accessed online at any place and time. Cloud computing also
allows organisations and enterprises to create and deploy own
applications. In addition, the cloud offers extendable storage
facilities, inclusive of processing capabilities. The cloud utilizes
various data centres with physical machines or servers for
storage and computing purposes. These data centres consume a
high amount of energy. The electricity utilization is high and it
continues to increase. The servers and cooling machines
consume a lot of energy, hence the need to manage this in an
optimal way. The study was executed by means of review of
some literature available on cloud management optimisation.
This study examines issues and developments of cloud
management optimisation and also present a recommendation
for future research. The result only 25% of the core papers
examined discussed the issue of cloud virtualization as it relates
to cloud management optimisation. This outcome will offer
insight for further work in cloud management optimisation.
Index TermsCloud Computing, Virtualization,
Optimization
I. INTRODUCTION
LOUD computing represents a model for enabling
ubiquitous, convenient, on-demand network access
to a shared pool of configurable computing resources (e.g.,
networks, servers, storage, applications, and services) that
can be rapidly provisioned and released with minimal
management effort or service provider interaction” [1].
Cloud computing is dynamically evolving, yet making
significant impact in diverse areas of life. More enterprises
are migrating to the cloud and new technological
developments are taking place. Cloud computing offers
several benefits to organizations, enterprises and small
businesses alike. Most enterprises can reduce spending by
leveraging on the infrastructure available on the cloud.
Cloud services can be categorized in three primary ways.
Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS)
and Infrastructure-as-a-Service (IaaS). SaaS offers the cloud
user customized applications for use.
Manuscript received April 05, 2018; revised May 10, 2018. This work
was supported in part by the Covenant University through the Centre for
Research, Innovation and Discovery (CUCRID).
I. Odun-Ayo is with the Department of Computer and Information
Sciences, Covenant University, Ota, Ogun State Nigeria.
(+2348028829456; Isaac.odun-ayo@covenantuniversity.edu.ng)
F. Agono is with the Department of Computer and Information
Sciences, Covenant University, Ota, Ogun State, Nigeria.
(+2348123688797; email: frank.agono@stu.cu.edu.ng)
Cloud service providers (CSPs) ensures the availability of
applications over the Internet for use by cloud consumers.
Cloud consumers need not bother about installing software
or even software licences. In PaaS, the CSP provides a
platform for each user to develop and deploy applications.
Each user exercises control over his applications and some
resources, while the CSP maintains total control over the
platform. In IaaS, the CSP provides the cloud user with
network, storage, memory and compute resources at a fee.
The core process here is virtualization. The user has access
to the CSP’s infrastructure and is able to utilize all the
software resources associated with the resource usage.
Cloud computing also offers four deployment types: the
private, public, community and hybrid clouds. The private
cloud is owned entirely by an organization, usually accessed
by internal staff only and making it more secure.
Infrastructure can be on-premise or offpremise. Public
clouds have infrastructure on a much larger scale usually
owned by major CSPs. Public cloud offers different kind of
services to users on a metered basis. However, they are
considered less secure. Community cloud is usually owned
by several organizations agreeing to use the same
infrastructure based on a shared common interest.
Community clouds are usually either managed by the
community itself or outsourced to a third party company.
Hybrid clouds form a combination of community, private
and the public cloud environments. They operate as unique
entities, sharing the same infrastructure, but managed as a
single unit. Hybrid clouds allow organizations retain core
activities in the private cloud, while less essential activities
are migrated to the public cloud.
Cloud management usually refers to software applications
and platforms useful for monitoring and managing
operations, services and data in the cloud [2]. Cloud
management tools and technologies ensure the optimal
performance of cloud services and resources. Cloud
management involves task such as performance monitoring,
security and compliance issues [2]. Cloud optimization
refers to making more efficient and effective use of
computing resources and other infrastructure on the cloud.
Excluding the cost and responsibilities of its users, cloud
computing allows businesses to begin on a smaller scale and
thrive gradually thus ensuring optimal development.
Flexible pricing models allows cloud users to predict the
cost of usage beforehand. The processor, storage and
network resources utilises the pay-per-use payment model.
Users get access to resources, uses the resources for as long
as they want and then relinquish them afterwards [2].
Projections have revealed that by ensuring up-to-date
multiplexing of the resources and usage statistics of large
organisations, cloud computing amounts to about 5-7
percentage decrease in the cost of electricity, network
Cloud Management Optimization Issues and
Developments
Isaac Odun-Ayo, Member, IAENG, and Frank Agono
“C
Proceedings of the World Congress on Engineering and Computer Science 2018 Vol I
WCECS 2018, October 23-25, 2018, San Francisco, USA
ISBN: 978-988-14048-1-7
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)
WCECS 2018
bandwidth, operations, software and hardware requirements
[3]. Cloud computing also provides energyefficiency
through elimination of redundancies and efficient resource
management by CSP.
The aim of this paper is to discuss, cloud management
and optimization. The paper will examine what management
and optimization entails. It also highlights trends in the
industry. The remaining part of the paper is as follows.
Section 2 deals with related work. Section 3 examines the
concept of cloud management and optimization. Section 4 of
the paper presents current trends in this field. Section 5
concludes the paper and recommends further research.
II. RELATED WORK
In [4], Autonomic Management of Cloud Service Centres
with Availability Guarantees is presented. In the cloud
environment, changes occur beyond the control of the cloud
service providers. This is resolved by autonomic solutions in
terms of performance and energy trade-offs. The paper
focuses on solution in terms of availability. In [3], A
Business-Driven Cloud Optimization Architecture is
proposed. Several issues in optimization were discussed and
framework for addressing the challenges were examined.
The framework enhances different optimization concepts. In
[5], Cost optimization of virtual infrastructure in dynamic
multi-cloud scenarios is proposed. Pricing schemes and
other benefits makes the cloud attractive. Cloud brokers
have also made it easy to decide on suitable providers. The
paper employs brokerage to ensure optimum deployment of
servers. In [6], Energy Efficient Resource Management in
Virtualized Cloud Data Centres is presented. The paper
proposed an efficient energy utilization in cloud data
centres. The outcome shows that energy is saved without
jeopardising QoS. In [7], Application Centric Cloud
Management is proposed. The infrastructure approach is
often used in cloud optimization. The paper proposes an
application centred approach for optimization and for
ensuring that the user application can utilize resources from
different providers. In [8], Efficient Resource Management
for Cloud Computing Environments is presented. The focus
is on the optimal utilization of resources in data centres.
Various techniques were presented to improve overall
performance. In [9], the concept of revenue management is
proposed to allow optimum benefit in the provision of cloud
services. In [10], IaaS Cloud Architecture: From Virtualized
Datacenters to Federated Cloud Infrastructures is presented.
The architecture presented discusses the relevance of the
cloud operating system. It is expected to manage physical
and virtual resources and ensure scalability among cloud
providers. In [11], a novel agent-based autonomous and
service composition framework for cost optimization of
resource provisioning in cloud computing is proposed. The
main aim is to reduce the cost of virtual machine utilization
while ensuring customer satisfaction. A framework is
proposed for request processing and provision of resources.
In [12], Risk perception and risk management in
cloud computing: Results from a case study of Swiss
companies is proposed. The paper explores the risks
involved in cloud utilization in present times. An analysis
was carried out on Swiss companies on the cloud and the
result was that of risk awareness before adoption. In [13],
Resource usage optimization in Mobile Cloud Computing is
proposed. The focus is on resource demand in mobile cloud
computing. A framework was proposed and evaluated with
good results in terms of resource demand. In [14], Revenue
management for Cloud computing providers: Decision
models for service admission control under non-
probabilistic uncertainty is presented. The focus is to ensure
that services are available to the consumers while at the
same time cloud providers maximise profit.
III. CLOUD MANAGEMENT AND OPTIMIZATION
A. Goals of Management and Optimization
Cloud management means exercising administrative
rights over cloud architectures [15]. A well-actualised
management plan enables cloud users to exercise control
over the scalable and dynamic environment on the cloud.
The following are some of the goals of management and
optimization [15].
1) Self Service Capability: The introduction of cloud
management, grant users the ability for self-service. Thus,
eliminating the conventional process involved in the
provisioning of IT resources. Users have the ability to
access the several available cloud types, users can monitor
current cloud processes, establish new processes, monitor
usage and adjust cost of resource allocation. Using the
reporting capabilities, users can track budgetary allocations
and make adjustments where necessary so as to reduce
operating costs.
2) Workflow Automation: Cloud management enables
the automation of workflow. Automation enables
organisations transform their business strategies and policies
into actions and steps required to create and handle cloud
instances with minimal human assistance or interference.
Asides functioning in creating, placing and adjusting of
intensive cloud processes, automating organisational
workflow assists organisations attain desired compliance
and report requirements. For instance, cloud management
tools have the feature that sends information to a manager in
the event of an employee trying to transmit the files and
activities of a private cloud to a public cloud, which can lead
to violation of the company’s security and compliance
regulations or policies thus attracting potential sanctions
from regulatory bodies.
3) Analysis of workload: Cloud management allows
the analysis of ongoing user experiences and cloud
workload. An organisation using a private cloud can
ascertain the functionality of its cloud infrastructure and
offer basic activities such as balancing organisational
workload and capacity planning. In public cloud
environments, server downtime measurements help enforce
adherence to the public cloud provider service level
agreement. By employing the use of measurements criteria,
companies get to choose the period it desires to use cloud
service providers or when it is necessary to migrate the
workload from public to private clouds. Public cloud service
providers deploy complex tools to match the requirements
of the services provided.
Proceedings of the World Congress on Engineering and Computer Science 2018 Vol I
WCECS 2018, October 23-25, 2018, San Francisco, USA
ISBN: 978-988-14048-1-7
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)
WCECS 2018
B. Strategies for Cloud Optimization
Cloud management and optimization is about improving
efficiency in the utilization of infrastructure. The variety of
services cloud computing offers continually represents an
integral part of an organisations Information Technology
infrastructure, hence management teams in organizations
realise the need for cloud optimization [16]. Organisations
have found that it is more satisfactory not only to simply
implement cloud services, but the need for proper and close
monitoring and evaluation of resources to ensure optimal
performance and productivity. Discussed below are some
strategies for cloud optimization [16]:
1) Governance Strategy: Cloud governance is closely
related to cloud optimization. Cloud governance strategy
determines the methodology by which an organisation
evaluates and maintains its cloud solution using already
stated guidelines. Cloud governance can effectively reduce
inefficient occurrences of cloud usage, therefore ensuring
the responsible utilization of resources.
2) Investing in Cloud Analytics: Organization must
have a method for determining the manner, the place and the
people using its cloud services. This helps to ensure that the
cloud services remain at its peak performance.
3) Dynamic Uptime, Scaling and Scheduling:
Majority of the systems used in cloud platform comprises a
large number of workstations, which functions only within
the regular period for business activities on a daily basis.
Back end systems are often times idle, other than running
periodical batch functions. Therefore, systems should be
analysed and organized by uptime needs such as 24/7 and
weekdays only, as well as regular batch function. System
can be scaled based on load or other metrics that are
identified to ensure that they meet needs. System uptime
should be automated based on categorization.
4) Leveraging Purchase Commitments: Major cloud
service providers give out huge discount offers for reaching
certain commitments over a period of time. Using the
various spending models can reduce cost and increase return
on investment. It is therefore necessary to identify uptime
schedules to take advantage of an appropriate model.
5) Lift and Shift Management: Many cloud
infrastructure start out as lift and shift projects, where
existing on-premise infrastructure is mapped to the cloud on
an as-is basis. This approach can result in oversized and
inefficient system. Therefore, each system instance and
feature should be sized according to true performance needs.
Utilizing reserved instances and turning off unused instances
have proven to be worthy steps in significantly reducing
costs.
6) Instance Sizing: It is essential to choose one
instance that matches an enterprises initial requirement. In
the event that further usage and monitoring indicates the
need to change the requirements, then adjustment of size can
be made on demand.
7) Auto Scale: Using the cloud is embracing a
dynamic environment that changes with demand. Auto scale
is an important and basic cloud feature that allows cloud
users to define their minimum and maximum instance pools
as well as fundamental scaling metrics such as CPU
utilization rate.
8) Disposing Instance: Unused capacity should be
terminated based on predefined metrics and rules such as
CPU utilization less than 10%. Using start/stop option for
automatically capturing instances image and volume
snapshots are helpful in performing recovery.
Reserved Capacity: Proper estimation of the planning
and demand capacity can improve efficiency. Leveraging
reserved and spot instances capacity also offer lower rates.
9) Cheaper Storage and Compute Resources: It is
possible to have cheaper computer resources with spot
instances or by deploying in a cheaper geographic region
based on services availability. It is essential to transmit data
from costly disk volumes to cloud storage. Using archive
service like ‘Glacier’ can significantly decrease storage cost.
10) Management and optimization Tools: It is
imperative to choose and implement devices that generate
transparency and also helps an enterprise arrive at
appropriate utilization of resources.
C. Optimization Architecture
In [3], a cloud optimization architecture is proposed. The
proposed architecture is structured in layers. There are 3
optimization layers based on the 3 service models of cloud
computing architecture, that is, the SaaS, PaaS and IaaS. In
[3] is a figure that shows each layers different optimization
goals, sensors and actuators. Optimizing each layer
represents the layers’ interest to either maximise profit or
increase user engagement and satisfaction. The methods of
measuring optimization (sensors) or drive optimization
(actuators) have been limited in type and number due to
abstraction caused by virtualization and by SaaS concept.
1) IaaS Owners
a. Goals: the value of cost and revenue depends on
the number of resources and price per resources.
Ensuring cost reduction and revenue increment
requires the full utilization of resources and
maximising the resources by multiplexing. Recent
reports have suggested that only about 10% of the
entire IT resources are presently in use, it has also
been proven hypothetically that resource utilization
metrics can be improved up to a factor of 9. During
actual process, only a factor of 5 7 is realistic.
Service request initiated by the layer represents
each VMs performance characteristics: memory,
processing capability, and storage. Attaining
optimal usage means that the management layer is
required to ensure that the fewest number of
hardware units are used to cater for all the
requirements.
b. Constraint: Capacity constraints involving the IaaS
owners include memory, storage, network and
processors, which may change due to failures or
equipment additions.
c. Sensors: The IaaS layer enjoys access to all
hardware including the operating system and
hypervisors. Sensors are tasked with measuring the
usage capacity, the availability and location of each
resource.
d. Actuators: The allocation of the VMs and storage
represents the main actuators that satisfies the
Proceedings of the World Congress on Engineering and Computer Science 2018 Vol I
WCECS 2018, October 23-25, 2018, San Francisco, USA
ISBN: 978-988-14048-1-7
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)
WCECS 2018
performance requirement and improve the layers’
usage. In addition, activating or deactivating the
VMs can increase usage without hampering the
requirements output.
2) PaaS Owners
a. Goal. Revenue is realised from providing hosting
services. Cost comprises the VMs resources, the
amount of disk spaces used and the cost of using
licences from third party companies, database
services, and penalty it is incurs when SLA
violation occurs. This layer’s goal is to reach the
maximum amount of applications hosted, and
reducing the requirements needed and penalty it
has to pay.
b. Constraint: SLA contracts with SaaS owner can
be treated as hard constraints in place of cost.
Delays in increasing the resources the CSP has
can occur, and this may constrain these resources
in the short term.
c. Sensors: The layer monitors its own VMs. The
layer is restricted from accessing the hardware
resources counter or virtualization hypervisors.
The layer also has to monitor the resources of its
collection of licences and a pay-per-use license
of third-party software applications.
d. Actuators: This layer achieves its performance
goals by acting on several handlers such as the
type, number, size of VMs and also the
allocation of containers to these VMs.
3) SaaS Owners
a. Goals: This layer is operation is centred on
subscriptions. The cost of subscription is based on
the revenue, which is dependent on the total
amount of users and also dependent on the
performance. Associated cost is comprised of
payment for resources to PaaS, this cost also can be
by subscription.
b. Constraints: Some SLA with applications could be
treated as hard constraints.
c. Sensor: Each application maintains its optimal
quality of service by ensuring it monitors the
number of users over the amount of transactions.
QoS includes response time for each user request
and throughput of aggregated functions across
multiple requests.
d. Actuators: Applications restrict the amount of
requests it sends for resources from PaaS in order
to reduce its cost. Applications should control the
amount of instances, and deployment of the
application.
D. Optimization Scenario
In cloud computing, optimization is a series of continuous
activities, activated by deploying new applications,
adjusting the workload characteristics, hardware crash and
software components and maintenance activities.
a. Scene One: A burst of load for an application. The
application must identify its requirement for extra
capacity or better load balancing by streamlining its
utilization in the new situation. Likewise, the PaaS layer
must locate an optimal choice to provide it with
resources it already possesses or request from the IaaS
layer, which must be deployed and allocated additional
VMs in an optimal manner.
b. Scene Two: The instant of a node failure rapidly
reduces the availability of resources at all layers. The
IaaS must identify the failure and act to replace the lost
node by deploying new VMs and possibly copying the
data at its current state. The PaaS layer may react then
by deploying replica container in these VMs and
finally, SaaS layer will deploy replica software.
E. Cloud Load Balancing
Cloud load balancing (CLB) is an essential process for
management and optimization on the cloud. This comprises
activities such as the distribution of jobs to be done and the
distribution of resources required for processing. CLB gives
companies the ability to accurately manage applications by
sharing resources between several services or clients.
IV CLOUD COMPUTING IN AFRICA
The emergence and adoption of cloud computing is still in
its early stage in most African countries. This section
examines the adoption and use of cloud computing in four
African countries. The selected countries represent each of
the four regions, with Nigeria representing the West, Kenya
representing the East, South Africa representing the South of
Africa and Tunisia representing the North. South Africa
currently has the highest activity in terms of cloud
utilization, with demand arising from the private sector. It
also possesses an indigenous cloud provider company, the
Internet Solutions [18]. The other countries however have
recorded certain level of highs and lows in the cloud
computing sphere [18].
A. Cloud Adoption and Management in Nigeria
There exist several companies offering cloud services and
solutions in the Nigerian market. A number of which
include Amazon, Google, EMC, Cisco, HP, IBM, Microsoft,
Sales Force and SAP [18]. The cloud computing industry in
Nigeria is in early growth stages, much of its demands is on
the IaaS. Mobile operators such Airtel, Glo and MTN have
introduced several mobile cloud offerings targeted at the
SME market [18]. Two American-based cloud providers,
IBM and Sproxil have implemented solutions that enhanced
the fight against drug counterfeiting. Consumers are able to
ascertain the authenticity of drugs using mobile phones. By
simply scratching off a code from a shiny seal on the pack
of the drug and texting same to a designated number,
consumers can verify if the particular drug is original and
can also report the venue where the drug was purchased
from, in cases where the drug is found not to be genuine. A
survey conducted in 2011, predicts that by the year 2020, a
large percentage of users will exchange information online,
access software and interact with applications using only
their smartphones, utilizing remote servers powered by
cloud computing instead of having to install applications on
their personal computers [20].
Proceedings of the World Congress on Engineering and Computer Science 2018 Vol I
WCECS 2018, October 23-25, 2018, San Francisco, USA
ISBN: 978-988-14048-1-7
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)
WCECS 2018
B. Cloud Adoption and Management in Kenya
Market competition and supply is emerging between local
and international organizations in Kenya. Companies like
Kenya Data Network (KDN), Safaricom Ltd and MTN are
ahead in the delivery of the IaaS services [21]. Providers of
PaaS services in Kenya offer services such as server
provision, storage and backup systems, which is
championed by the partnership between Safaricom and
Seven Seas Company. The entry of this partnership into the
cloud market has significantly changed the nature of events,
by encouraging potentials users to explore local clouds as a
workable alternative to foreign clouds [22]. [23] states the
following as the obstacles facing cloud computing adoption
in Kenya: security, infrastructure, internet coverage, lack of
standardization, compliance and unavailability of hardware
and software.
C. Cloud Adoption and Management in South Africa
Various companies are beginning to announce the
benefits of cloud computing in South Africa. More
companies have deployed some form of cloud computing in
a bid to reduce operational costs [23]. Most recently, Google
was rated as the most reliable cloud vendor in the country.
Google cloud services include but not limited to, web
hosting, application development, configuration and data
backup, e-commerce, customer relationship management
systems (CRM) and email hosting/archiving [24].
Estimations show that in 2013, South Africa had over
twelve million cell phones connected to the internet [25].
This figure translates to meaning that every Internet
connected mobile phone can access cloud services, ensuring
availability and accessibility of the cloud services.
D. Cloud Adoption and Management in Tunisia
The increasing number of scientific conferences,
workshops, business and technology events associated with
cloud computing in Tunisia indicates the growing interest
and awareness of the technology. These growing interests
translates to increased investment possibilities [22]. Foreign
companies such as Microsoft, HP and Oracle have shown
rapid interest in the emergence of cloud technology in
Tunisia. Universities have begun to encourage research in
topic related to cloud computing [22].
V. ANALYSIS AND DISCUSSION
A. Cloud Optimization Strategies
Strategies for cloud optimization has over time gathered
some reasonable amount of attention from cloud
researchers. Presented in Table 1 are the works of different
core authors concerning cloud optimization strategies. [26]
highlighted two divisions of cloud optimization strategies.
The divisions were based on the most used network resource
in a particular cloud computing environment. These
divisions are computing intensive and data intensive
resources. Scheduling strategy for computing intensive tasks
involves moving data to the high-performance computer and
for tasks that are data intensive. The scheduling strategy
should reduce the flow of data movement and hence
decreasing its required transmission time. In [27], the author
highlighted that the strategy for allocation of resources is a
vital condition in cloud computing, as it ensures optimal use
of scarce resources. In addition, the strategy for allocation of
resources should be dependent on application
characteristics, hence the need to avoid providing more
resources than is necessary and preventing low usage of the
provided resources and the service level agreements. [28]
mentioned that a time-based optimization strategy would
employ a more direct and aggressive dynamic provisioning
utilization in a bid to reduce time required for execution. In
[31] the authors stated that the resource allocation strategy
should be responsive to changes in resource management
policies and current network traffic situation. In this short
analysis, only 33% of this core authors discussed issues
relating cloud optimization strategies.
B. Cloud Virtualization
Virtualization constitutes a unique aspect of cloud
management and optimization. An emerging interesting
field of study, virtualisation-as-a-Service was highlighted in
[33]. It was concluded by defining virtualization as the
activity of substituting a physical resource with virtual
(logical) resources, thereby reducing the amount of space
required to store the resources. [34] mentioned that
virtualization provides the platform and possibility for
shareable and available-on-demand infrastructure. [28]
opined that virtualisation enables the abstraction of
computer resources thus enabling a single physical machine
serve as multiple virtual machines. [30] contributed by
stating that the current available virtualization technology
and methodology offers the possibility to perform live
migration i.e. migrating one virtual machine from one host
to another without hampering its functions. Despite these
contributions, only 25% of the core papers examined
discussed the issue of cloud virtualization.
TABLE I
COMPARATIVE ANALYSIS OF CORE CLOUD MANAGEMENT
OPTIMIZATION AREAS
References
Optimization
Strategies
L. Guo, et al. (2012)
x
T. Nadu and V. P. Anuradha, (2014).
x
R. Buyya, et al. (2011).
x
N. M. Calcavecchia, et al. (2012).
A. A. M., et al. (2011).
x
B. Simmons, et al. (2010).
M. Litoiu, et al. (2010).
V. R. Roopali Goel. 2012.
L. Luo, et al. (2012).
M. Paul and G. Sanyal. (2011).
V. Nallur, et al. (2009).
L. Y. Xiao, et al. (2013).
Proceedings of the World Congress on Engineering and Computer Science 2018 Vol I
WCECS 2018, October 23-25, 2018, San Francisco, USA
ISBN: 978-988-14048-1-7
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)
WCECS 2018
C. Cloud Architecture
There exist several challenges to ensuring cloud
optimization and hence the need for an architecture tackle
such challenges [35]. The proposed architecture provides
support for self-maintenance using automation of the tasks
involved in the different stages of cloud optimization:
monitoring, analysis and prediction, planning and execution.
[32] presented an extension of the three-layered cloud
computing architecture using strategy-trees. Each layer of
the cloud architecture act as a manager representing the
perspective of the provider, and utilising strategy-trees to
implement feedback loops to attain desired objectives over a
set period of time. It is not surprising that 75% of the papers
reviewed focus extensively on optimization architecture
because of its importance in management optimization.
VI. CONCLUSION
Cloud computing is evolving both in terms of technology
and utilization. Cloud computing provides applications and
also allow users deploy their own applications. Huge
infrastructure is available to provide compute resource and
storage for cloud users. Management and optimization is
critical if cloud providers and users are to obtain maximum
satisfaction from cloud activities. There are strategies for
cloud management and optimization including technology
tools and processes.
ACKNOWLEDGMENTS
We acknowledge the support and sponsorship provided
by Covenant University through the Centre for Research,
Innovation and Discovery (CUCRID).
REFERENCES
[1] P. Mell, T. Grance, “The NIST Definition of Cloud Computing”,
NIST Special Publication 800-145, 2011.
[2] ‘Definition of Cloud Management’, Retrieved from:
www.webopedia.com/term/c/cloud_management.html
[3] M. Litoiu, J. Ng, G. Iszlai, “A Business-Driven Cloud Optimization
Architecture”, SAC’10, March 22‐26, 2010, Sierre, Switzerland.
ACM 978‐1‐60558‐638‐0/10/03.
[4] B. Addis, D. Ardagna and B. Panicucci, L. Zhang, “Autonomic
Management of Cloud Service Centers with Availability Guarantees”,
IEEE International Conference on Cloud Computing, 2010. DOI
10.1109/CLOUD.2010.19.
[5] J. L. Lucas-Simarro, R. Moreno-Vozmediano, R. S. Montero and I.
M. Llorente. “Cost optimization of virtual infrastructures in dynamic
multi-cloud scenarios”, Concurrency and Computation: Practice and
Experience Concurrency Computat.: Pract. Exper. Published Online
in Wiley Online Library (Wileyonlinelibrary.Com), 2012. Doi:
10.1002/Cpe.2972
[6] A. Beloglazov and R. Buyya. “Energy Efficient Resource
Management in Virtualized Cloud Data Centers”, 10th IEEE/ACM
International Conference on Cluster, Cloud and Grid Computing,
2010. DOI 10.1109/CCGRID.2010.46.
[7] S. Khatua, A. Ghosh, N Mukherjee, “Application Centric Cloud
Management”, Access on 24 May 2016
[8] [8] A. J. Younge, G. V. Laszewski, L. Wang, S. Lopez-Alarcon, W.
Carithers, “Efficient Resource Management for Cloud Computing
Environments”.
[9] T. Pueschel, D. Neumann. “Management of Cloud Infastructures:
Policy-Based Revenue Optimization”, ICIS 2009 Proceedings. Paper
178.
[10] R. Moreno-Vozmediano, R. S. Montero, and I. M. Llorente, “IaaS
Cloud Architecture: From Virtualized Datacenters to Federated Cloud
Infrastructures”, Published by the IEEE Computer Society, 2012.
[11] A. Singh, D. Juneja, M. Malhotra, “A novel agent based autonomous
and service composition framework for cost optimization of resource
provisioning in cloud computing”, Journal of King Saud University
Computer and Information Sciences, 1928, 2017.
[12] N. Brender1, I. Markov, “Risk perception and risk management in
cloud computing: Results from a case study of Swiss companies”,
International Journal of Information Management, 726-733, 2013.
[13] P. Nawrocki, W. Reszelewski, “Resource usage optimization in
Mobile Cloud Computing”, Computer Communications. pp 1-12,
2017
[14] T. Püschela, G. Schryenb, D. Hristovab, D. Neumanna, “Revenue
management for Cloud computing providers: Decision models for
service admission control under non-probabilistic uncertainty”,
European Journal of Operational Research, 637 647, 2015.
[15] M. Rouse. Cloud Load Balancing’, TechTarget, ComputerWorld
Publication, 2015
[16] D. Lucky Cloud optimization: 3 Ways to improve the efficiency of
your cloud usage’, Datapipe Publication, 2015.
[17] Equinix ‘Hybrid Cloud Optimization’, Retrieved from:
www.equinix.com, 2016
[18] F. Odufuwa. Nigerian cloud report (unpublished), 2013.
[19] M. MILLER, Cloud Computing: Web-Based Applications that
Change the way you work and collaborate online US: Que Publishing,
2009.
[20] K. JACKSON, D. PHILPOTT, "GovCloud: Cloud Computing for the
Business of Government," Florida: Government Training Inc., 2011.
[21] M. Mureithi, Kenya cloud economy: The state of play of a nascent
market (unpublished), 2013.
[22] Gillwald and Mpho, “The Cloud over Africa: Research ICT Africa,”
2012.
[23] R. K. Grewal and K. P. Pushpendra, "A Rule-based Approach for
Effective Resource Provisioning in Hybrid Cloud Environment,"
International Journal of Computer Science and Informatics, vol. 1, no.
4, pp. 2231 - 5292, 2012.
[24] C. Hinde and J.-P. Van Belle, "Cloud Computing in South African
SMMEs: Risks and Rewards for Playing at Altitude," International
Journal of Computer Science and Electrical Engineering, vol. 1, no. 1,
pp. 1 - 10, 2012.
[25] M. Grobler and Z. Dlamini, "Global Cyber Trends a South African
Reality," in IST-Africa, Durban, South Africa, 19 - 21 May 2010.
[26] L. Guo, S. Zhao, S. Shen, and C. Jiang, “Task scheduling
optimization in cloud computing based on heuristic Algorithm,” J.
Networks, vol. 7, no. 3, pp. 547553, 2012.
[27] T. Nadu and V. P. Anuradha, “Computing,” no. 978, 2014.
[28] R. Buyya, S. K. Garg, and R. N. Calheiros, “SLA-oriented resource
provisioning for cloud computing: Challenges, architecture, and
solutions,” Proc. - 2011 Int. Conf. Cloud Serv. Comput. CSC 2011,
no. Figure 1, pp. 110, 2011.
[29] N. M. Calcavecchia, O. Biran, E. Hadad, and Y. Moatti, “VM
placement strategies for cloud scenarios,” Proc. - 2012 IEEE 5th Int.
Conf. Cloud Comput. CLOUD 2012, pp. 852859, 2012.
[30] A. A. M., P. K., and W. a., “A framework for resource allocation
strategies in cloud computing environment,” Proc. - Int. Comput.
Softw. Appl. Conf., pp. 261266, 2011.
[31] B. Simmons, M. Litoiu, D. Ionescu, and G. Iszlai, “Towards a cloud
optimization architecture using strategy-trees,” Proc. 9th Int. Inf.
Telecommun. Technol. Symp. (I2TS 2010), pp. 1315, 2010.
[32] M. Litoiu, M. Woodside, J. Wong, J. Ng, and G. Iszlai, “A business
driven cloud optimization architecture,” Proc. 2010 ACM Symp.
Appl. Comput. - SAC ’10, no. May 2014, p. 380, 2010.
[33] V. R. Roopali Goel, “Cloud Computing and Service Oriented
Architecture,” Int. J. Recent Technol. Eng., vol. 1, no. 1, p. 3, 2012.
[34] L. Luo, W. Wu, D. Di, and F. Zhang, “A resource scheduling
algorithm of cloud computing based on energy efficient optimization
methods,” IEEE Green Comput. Conf., vol. (2012) Jun, no. July 2007,
pp. 16, 2012.
[35] M. Paul and G. Sanyal, “Survey and analysis of optimal scheduling
strategies in cloud environment,” 2011 World Congr. Inf. Commun.
Technol., pp. 789792, 2011.
[36] V. Nallur, R. Bahsoon, and X. Yao, “Self-Optimizing Architecture for
Ensuring Quality Attributes in the Cloud.pdf,” pp. 281–284, 2009.
[37] L. Y. Xiao, Z. Wang, R. Wang, and H. N. Wang, “Architecture and
Key Technologies of Cloud Computing,” Adv. Mater. Res., vol. 756
759, no. 200802860031, pp. 19531956, 2013.
Proceedings of the World Congress on Engineering and Computer Science 2018 Vol I
WCECS 2018, October 23-25, 2018, San Francisco, USA
ISBN: 978-988-14048-1-7
ISSN: 2078-0958 (Print); ISSN: 2078-0966 (Online)
WCECS 2018
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