The document discusses performance evaluation of different cloud computing architectures and deployment models. It begins by defining cloud architecture and deployment models, including public, private and hybrid clouds as well as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It then discusses defining test scenarios, identifying architectures and models to evaluate, and preparing a report on the performance evaluation methodology, test results and analysis. The document also provides a literature review on previous research related to evaluating cloud platforms, characteristics of cloud deployment models, components of cloud architecture, and algorithms for handling constraints. It concludes by identifying research gaps in evaluating specific deployment models, a lack of real-world evaluations, limited research

1. PERFORMANCE EVALUATION OF DIFFERENT CLOUD COMPUTING
ARCHITECTURES AND DEPLOYMENT MODELS
KALASH SHANDILYA
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2. CLOUD ARCHITECHTURE
Cloud computing architecture refers to the overall design and structure of a cloud
computing environment, including the components, services, and interactions that
make up the system. It involves the design and integration of various computing
resources such as servers, storage, networking, and software, as well as the
deployment and management of these resources to provide services to users over
the internet.
The design of a cloud computing architecture depends on several factors, including
the type of cloud deployment model, the specific needs and requirements of the
organization, and the desired level of scalability, flexibility, and reliability.

3. CLOUD DEPLOYMENT MODEL
The deployment of a cloud computing environment requires careful planning and
consideration of factors such as security, compliance, and performance, to ensure
that the environment meets the organization's needs and requirements.
It involves the deployment of computing resources such as virtual machines,
storage, and networking, as well as the installation and configuration of software
and applications on the cloud infrastructure.

4. REQUIREMENT ANALYSIS
• Identify the different cloud computing architectures and deployment models: Identify the different cloud computing
architectures and deployment models that need to be evaluated. This can include public, private, and hybrid cloud
architectures, as well as different deployment models such as Infrastructure as a Service (IaaS), Platform as a
Service (PaaS), and Software as a Service (SaaS).
• Define the test scenarios: Define the test scenarios that will be used to evaluate the different cloud computing
architectures and deployment models. This can include scenarios such as peak load testing, stress testing, and
failover testing.
• Prepare the report: Prepare a comprehensive report that documents the performance evaluation methodology, test
scenarios, performance metrics, results, and analysis. This report can be used as a reference for future performance
evaluations and to inform decision-making around cloud computing architecture and deployment models.

5. LITERATURE REVIEW
1. Quang Duan et. al. [2017]
• Evaluation of the performance of Cloud computing platform services may need to handle some special issues, including
performance metrics and benchmarks appropriate for PaaS. To address such needs, Ataş and Gungor [2] developed a
framework for evaluating PaaS performance and proposed a set of benchmark algorithms that help determine the most
appropriate PaaS provider based on different resource needs and application requirements.
• Commercial PaaS services such as Cloud Foundry, Heroku, and OpenShift, were tested in [2] and the obtained results were
analyzed by the authors using two evaluation methods: the Analytical Hierarchy Process (AHP) and Logic Scoring of Preference
(LSP).
2. Dinesh Kumar, Saini Krishan Kumaran, Punit Gupta et. al. [2022]
• there are five essential characteristics of cloud, “on-demand self-service, broad network access, resource
pooling, rapid elasticity, and measured service” . All cloud deployment models and services must have these
characteristics. There are two basic types of cloud deployment models: private and public. By mixing these two
types, there emerge two more variants: hybrid and multicloud.
• Some examples of well-known public cloud providers are Microsoft Azure, Amazon Web Services (AWS), and
Google Cloud.
• HP Data Centers, Microsoft, Elastra-private cloud, and Ubuntu are the example of a private cloud.

6. CONTD…..
3. Archana Srivastava et. al. [2014]
• The two most significant components of cloud computing architecture are known as the front end and the back end.
• The front end is the part seen by the client, i.e. the computer user. This includes the client’s network and applications used to access the cloud
via a user interface such as a web browser. The back end of the cloud computing architecture is the ‘cloud’ itself, comprising various computers,
servers and data storage devices.
• Database as a service (DBaaS) - some cloud platforms offer options for using a database as a service, without physically launching a virtual
machine instance for the database. In this configuration, application owners do not have to install and maintain the database on their own.
Instead, the database service provider takes responsibility for installing and maintaining the database, and application owners pay according to
their usage. For example, Amazon Web Services
4. LaValle et. al. [1998]
• The tree version of Rapidly-Exploring Random Graph (RRG) is the Rapidly exploring Random Trees-Star (RRT), which is used to handle
differential constraints, keeping the RRG’s asymptotic optimal property intact; the bad connections are removed by using the RRT, which helps
to enhance the solution and substantially reduce its cost.
• Though the rapid exploring random graph, RRT promises to deliver completeness and has an overall positive performance, where it doesn’t
consider the result quality. It is also worth noting that, the RRT algorithm is not asymptotically optimal. Hence, the RRG methodology is
introduced to enable optimality in an asymptotic environment.

7. Research gap
• Limited research on specific deployment models: Although there is extensive research on cloud computing architectures and
deployment models, there is limited research on specific deployment models such as multi-cloud and edge computing.
• Lack of real-world evaluation: Most of the existing research on cloud computing architectures and deployment models is
based on simulation and modeling. There is a need for more real-world evaluation of these architectures and deployment
models to validate the results of simulation-based studies.
• Limited research on specific performance metrics: Although there are several performance metrics that can be used to
evaluate cloud computing architectures and deployment models, there is limited research on specific metrics such as energy
efficiency, security, and privacy.
• Lack of standardized performance evaluation methodologies: There is a lack of standardized performance evaluation
methodologies for cloud computing architectures and deployment models. This can make it difficult to compare the
performance of different architectures and deployment models.