Project1- Feasibility Proposal
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Jun 10, 2024
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Uploaded by ColonelBravery14503
Feasibility Proposal
Created by Md Saifur R Akash
Sunday, June 2
nd
Cloud Computing
Cloud computing models vary in deployment and service types. There are three main types
of cloud deployment models: public, private, and hybrid clouds (Mell & Grance, 2011).
Public Cloud: Provided by third-party providers over the public internet. Examples include
AWS, Google Cloud, and Microsoft Azure. Suitable for scalability and cost-efficiency, ideal for
startups and applications with variable workloads.
Private Cloud: Dedicated environments for a single organization, offering enhanced security
and control. Best for sensitive data and critical applications where compliance and data
sovereignty are essential.
Hybrid Cloud: Combines public and private clouds, allowing data and applications to be
shared between them. Useful for balancing the need for flexibility and security, enabling
workloads to be shifted as needed.
Service models include:
Infrastructure as a Service (IaaS): Provides virtualized computing resources over the
internet. Suitable for organizations needing control over their infrastructure without
managing physical hardware.
Platform as a Service (PaaS): Offers hardware and software tools over the internet,
primarily for application development. Ideal for developers building software or
applications without managing underlying infrastructure.
Software as a Service (SaaS): Delivers software applications over the internet on a
subscription basis. Best for end-users requiring access to software without worrying about
infrastructure or platform management.
Benefits and Drawbacks of the Cloud
Cloud deployment models offer several benefits over on-premises models.
Benefits of cloud deployment models include scalability, cost-efficiency, accessibility, and
reduced maintenance burdens (Armbrust et al., 2010). Cloud services offer on-demand
resources, allowing organizations to scale up or down based on their needs, and reduce
capital expenditures on hardware and software with pay-as-you-go models. Additionally,
cloud services can be accessed from anywhere with an internet connection, promoting
remote work and collaboration, while cloud providers handle maintenance, updates, and
security, reducing the burden on in-house IT teams.
However, there are drawbacks to consider, such as potential security concerns, dependence
on internet connectivity, and reduced control over the infrastructure (Gartner, 2020). There
are risks related to data breaches and compliance with regulatory requirements, as well as
reliance on the provider's uptime guarantees and limited ability to perform custom
performance tuning compared to on-premises solutions.
Cloud Deployment Models
Adopting different cloud computing deployment models presents various risks and benefits.
Public Cloud Risks include data security and privacy concerns, potential for vendor lock-in,
and compliance challenges (Mell & Grance, 2011). Private Cloud Risks involve higher costs
due to dedicated resources and complex management and maintenance requirements.
Hybrid Cloud Risks consist of increased complexity in managing and integrating public and
private environments, and potential data transfer costs.
Public Cloud Benefits entail cost savings, scalability, flexibility, and reduced infrastructure
management (Armbrust et al., 2010). Private Cloud Benefits include enhanced security,
control, and compliance, tailored to specific organizational needs. Hybrid Cloud Benefits
offer flexibility to balance workload distribution, optimized cost and performance, and
improved disaster recovery options.
Considerations of Cloud Computing
Switching to a cloud model requires careful consideration of organizational and technical
issues.
Organizational issues include change management, cost management, and compliance and
legal issues (Gartner, 2020). Ensuring a smooth transition with proper training and support
for employees is crucial. Understanding and planning for the financial implications,
including hidden costs, and ensuring adherence to industry regulations and data
sovereignty laws are also essential.
Technical issues involve data migration strategies, ensuring compatibility and integration
with existing systems and applications, and implementing robust security measures to
protect data in transit and at rest (Madden, 2012).
Big Data vs. Structured Data
Big Data is characterized by high volume, variety, and velocity. This includes unstructured
data from various sources like social media, sensors, and logs. Structured Data, on the other
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hand, is organized in predefined formats like databases and spreadsheets, making it easier
to analyze using traditional methods (Madden, 2012).
Big Data Collection often occurs in real-time from diverse sources, requiring advanced tools
for storage and processing. Structured Data Collection typically involves manual entry or
transactional systems, stored in relational databases.
Big Data Storage uses distributed storage systems like Hadoop HDFS, NoSQL databases, and
cloud storage solutions. Structured Data Storage is managed in relational databases using
SQL-based systems like MySQL, Oracle, and SQL Server (Gartner, 2020).
Volume, Variety, and Velocity of Big Data
The scale of big data significantly affects data analysis methods.
Volume requires distributed computing and storage solutions, making traditional single-
server approaches inadequate. Variety necessitates flexible processing frameworks capable
of handling unstructured and semi-structured data. Velocity demands real-time processing
capabilities, which traditional batch processing methods struggle to meet (Madden, 2012).
Data Usability is impacted by the scale of data sets, necessitating advanced analytics tools,
machine learning algorithms, and scalable infrastructure to extract meaningful insights. The
complexity and sheer size of big data require sophisticated preprocessing and analysis
techniques (Armbrust et al., 2010).
References
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., ... & Zaharia, M.
(2010). A view of cloud computing. Communications of the ACM.
Gartner. (2020). Top 10 Trends in Data and Analytics for 2020. Madden, S. (2012). From databases to big data. IEEE Internet Computing.
Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. National Institute of
Standards and Technology.