Cloud Computing Question 1

This document provides a description of cloud architectures required for DTGOV to meet the Board's strategy. It discusses resource pooling architecture and elastic resource capacity architecture, along with their benefits and issues. The document also includes references for further reading.

Running head: CLOUD COMPUTING
Cloud Computing Question 1
Name of the Student
Name of the University
Author’s Note:

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Question 1
Description of Cloud Architectures required to employ for assisting DTGOV for meeting
the Board’s Strategy
1. Description of Cloud Architectures for DTGOV with Proper Reasons
DTGOV have planned to move to the Hybrid cloud strategy and for this reason they
need to select proper cloud architectures to be deployed. Two distinctive cloud architectures
that would be perfect for DTGOV are as follows:
i) Resource Pooling Architecture: The first and the foremost cloud architecture that is
suitable for DTGOV is resource pooling architecture (Jain & Paul, 2013). This particular
architecture is completely based on utilization of several resource pools, where identical
resources of information technology are being grouped as well as maintained by a specific
system, which can automatically ensure that synchronization is being maintained. A specific
resource pool is comprised of networks, memory, Central Processing Unit, storage and
servers like physical, virtual and both (Dinh et al., 2013). These resource pools eventually
enable the provide to properly delegate management or control over host resources and it is
being achieved by creation of a hierarchy of few resource pools in two models of nested
pools and parent and sibling pool.
Physical server pool is comprised of networked server, which has been installed with
OS as well as other required programs or applications, hence are ready for utilization
immediately (Botta et al., 2016). The virtual server pool is being configured with available
templates selected by cloud consumer while provisioning. Storage pool comprises of block
based and file based storage structure, which comprise of empty or filled device of cloud
storage. Network pool is comprised of various previously configured network connectivity
device and CPU pool is ready to be allocated towards virtual servers and can be broken down

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to individual processing core (Almorsy, Grundy & Müller, 2016). Since, DTGOV will be
implementing hybrid cloud in their business, it is highly recommended to them to use
resource pooling architecture so that they are able to reduce complexities for management of
resource pools to a high level. There are few dedicated pools that could be created for every
IT resource and the individual pool could be grouped into a large pool, where all individual
pools would become a sub pool (Hashem et al., 2015). In a nested pool model, the large pools
are sub divided to small pools, which group similar kinds of IT resources and could be
utilized for assigning resource pools for different groups or departments in similar cloud
based organization.
Parent sibling resource pool, on the other hand is being drawn from physically
categorized IT resources and these are isolated from one another for providing access to only
respective pool (Othman, Madani & Khan, 2013). There are some of the major as well as the
most significant mechanisms for implementation of resource pooling architecture in DTGOV,
which include resource cluster, logical network perimeter, audit monitor, pay as per use
monitoring, hypervisor, resource replication, remote administration systems, cloud usage
monitor and resource management system. DTGOV would be able to access to the cloud
storage devices and virtual servers.
ii) Elastic Resource Capacity Architecture: The second cloud architecture that is
suitable for DTGOV is elastic resource capacity architecture (Dinh et al., 2013). This
particular architecture is completely related to dynamic provisioning of virtual server with the
help of a system, which allocates as well as reclaims RAM and CPUs within immediate
response to the fluctuating processing requirement of all types of hosted information
technology resources. Resource pool is being utilized simply by scaling technologies, which
interact with VIM or hypervisor for retrieving and returning RAM or CPU resources at run
time (Botta et al., 2016). This run time processing of virtual server could be monitored so that

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so that extra processing power is being leveraged from resource pool through dynamic
allocation even before capacity thresholds are being fulfilled. Virtual server as well as the
hosted applications or information technology resources could be vertically scaled properly.
This particular cloud architecture could be designed with the purpose that hypervisor
as well as VIM for retrieving and returning RAM and CPU resources during run time
(Hashizume et al., 2013). This run time processing of virtual server could be monitored so
that additional processing power is being leveraged from resource pool through dynamic
allocation even before capacity thresholds are being fulfilled. Virtual server as well as the
hosted applications or IT resources could be vertically scaled. The cloud architecture could be
designed so that intelligent automation engine could send the scaling requests through VIM.
The virtual servers, which eventually participate within elastic resource allocation system
might need rebooting for dynamic resource allocations and taking effect (Chen et al., 2015).
Few significant and important mechanisms for elastic resource capacity architecture include
cloud usage monitoring, pay as per monitoring and resource replication. Since DTGOV will
be moving to a Microservies model, it would be quite beneficial for them to opt platform as a
service.
2. Description of Benefits and Issues that might result in deployment of the
Architectures
The major benefits and issues that might occur for DTGOV during the successful
implementation of resource pooling architecture and elastic resource capacity architecture are
as follows:
i) Resource Pooling Architecture: The most significant benefits of resource pooling
architecture are as follows:

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a) Grouping of Several Resources with Same Attributes: The first and the foremost
benefit of this particular architecture is to group or classify several resources with same
attributes after providing a quicker view of same storage objects (Jain & Paul, 2013). This
type of grouping helps in management of resources and hence DTGOV would not face any
type of issue.
b) Single Logical Unit for Provisioning: The resources would provide a single logical
unit for provisioning and thus resource pool management is much easier.
c) Configuring System for Automatic Provision Storage: The system should be
configured properly for automatic provision storage from distinctive resource pools without
intervention (Ali, Khan & Vasilakos, 2015).
d) Enabling Event Generation on Resource Pools: The event generation could be
enabled for resource pools so that as soon as a set of threshold is being crossed, the event
could be generated for alerting resource pool owner in DTGOV.
The issues of resource pooling architecture that DTGOV might get are as follows:
a) Issues related to Data Security: The major issues related to data security are the
first and the most significant challenges of resource pooling architecture (Othman, Madani &
Khan, 2013). Access management should be done for reduction of these issues.
b) Lack of Authentication: The authentication and authorization of data is absent in
this particular architecture and should be analysed in an effective manner.
ii) Elastic Resource Capacity Architecture: The most significant benefits of elastic
resource capacity architecture are as follows:

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a) On Demand Computing: This particular effort needs larger up front investments in
hardware, cooling, power, space and network bandwidth and hence on demand computing is
one of the major advantages in this architecture (Almorsy, Grundy & Müller, 2016).
b) Pay as per Use: It does not incur any kind of extra cost and hence only payment is
made the amount is used. Major opportunity for saving substantial IT investment capital is
not wasted on paying for idle resource.
c) Fault Tolerance: This is the third advantage of this architecture is fault tolerance,
hence DTGOV would be benefitted.
The issues of elastic resource capacity architecture that DTGOV might get are as
follows:
a) Issues related to Compatibility: The compatibility issues are the major issues for
this particular architecture and it should be eradicated on time (Ali, Khan & Vasilakos, 2015).
b) Issue with Demand Elasticity: This is the second issue with demand elasticity and it
can bring major complexities within DTGOV.

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References
Ali, M., Khan, S. U., & Vasilakos, A. V. (2015). Security in cloud computing: Opportunities
and challenges. Information sciences, 305, 357-383.
Almorsy, M., Grundy, J., & Müller, I. (2016). An analysis of the cloud computing security
problem. arXiv preprint arXiv:1609.01107.
Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing
and internet of things: a survey. Future generation computer systems, 56, 684-700.
Chen, X., Jiao, L., Li, W., & Fu, X. (2015). Efficient multi-user computation offloading for
mobile-edge cloud computing. IEEE/ACM Transactions on Networking, 24(5), 2795-
2808.
Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013). A survey of mobile cloud computing:
architecture, applications, and approaches. Wireless communications and mobile
computing, 13(18), 1587-1611.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The
rise of “big data” on cloud computing: Review and open research issues. Information
systems, 47, 98-115.
Hashizume, K., Rosado, D. G., Fernández-Medina, E., & Fernandez, E. B. (2013). An
analysis of security issues for cloud computing. Journal of internet services and
applications, 4(1), 5.
Jain, R., & Paul, S. (2013). Network virtualization and software defined networking for cloud
computing: a survey. IEEE Communications Magazine, 51(11), 24-31.
Othman, M., Madani, S. A., & Khan, S. U. (2013). A survey of mobile cloud computing
application models. IEEE communications surveys & tutorials, 16(1), 393-413.

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Wei, L., Zhu, H., Cao, Z., Dong, X., Jia, W., Chen, Y., & Vasilakos, A. V. (2014). Security
and privacy for storage and computation in cloud computing. Information
Sciences, 258, 371-386.