ICC104 Introduction to Cloud Computing Report 3 Sample
Context:
This assessment is designed to acquaint the students to public cloud platforms, their products, and experience the concepts of virtualisation and resource pooling in the Cloud Environment.
Project:
The project comprises of two tasks;
1. Explore Google Cloud Platform, setup a Virtual Machine in Google Cloud, deploy LAMP (Linux, Apache, MySQL and PHP) Server on that server install WordPress, without using any “Click to Deploy” Images.
2. Explore Amazon Web Services, setup an EC2 instance in AWS, deploy LAMP (Linux, Apache, MySQL and PHP) Server and on that server install WordPress, again without using “Ready to Deploy Images”
The deliverables of this project will be a report and a demonstration that can be carried out by the instructor in person or can be shared as screencasts of student’s project showing GCP and AWS dashboards, WordPress dashboard and all login credentials of AWS and GCP accounts and WordPress administrator with Public IP addresses of the registered instances or Virtual machines.
Instructions:
For AWS and Google Cloud Platform, you will register for a free Virtual Machine and Free Tier EC2 server.
The assessment requires you to prepare a report based upon your project and to record a screencast of your Project. To do this you will need to consider the following and specifically add in the body of the report:
1. Key service offering from Amazon Web Services and Google Cloud Platform.
2. Comparison of cost model of AWS EC2 and GCP VM.
3. The procedure you followed from the registration of your account to the deployment of WordPress with supporting evidence like screenshots and logs.
4. Possible improvement suggestion in the process while remaining within the Cloud domain.
5. Comparison of both the Platform EC2 and VM.
6. The concept of virtualization and elastic resources in AWS and GCP.
Writing the report
Start off with a short introduction (approximately 100 words) stating what the assessment is about and some basic information relevant to the Project. This section is to be written in complete sentences and paragraphs. It should not contain any dot point information or tables, graphs and diagrams. The introduction should be followed by some background information (approximately 200 words), in which you can provide some context related to cloud computing and the deployment models.
Then structure the main body of the report (approximately 2000 words) using the above 6 points as sub - section headings. These sections should be written in complete sentences and paragraphs.
However, you can include items such as graphs, diagrams and tables if appropriate. Ensure that you title these clearly, in the correct way, and reference using the APA system. If placing any tables, graphs and diagrams in an appendix at the end of your report, you need to clearly state where the item can be located. Generally, the information presented in appendices does not count towards the total word count.
Finally, write a conclusion (approximately 200 words) as a summary of your project. This section brings together all of the information that you have presented in your report and should link to the purpose of the assessment as mentioned in the introduction. You can also discuss any areas that have been identified as requiring further investigation and how this will work to improve or change our understanding of the topic. This section does not introduce or discuss any new information specifically, and like the introduction, will be written in complete sentences and paragraphs. No tables, graphs, diagrams or dot points should be included.
Solution
Introduction:
This assessment seeks to gauge how effectively two major cloud service providers, Amazon Web offerings (AWS) and Google Cloud Platform (GCP), are using their respective cloud computing offerings. The project entails setting up and configuring a virtual machine instance on both platforms using their respective free tier offerings, namely the free VM on GCP and the AWS EC2 instance (Wickham, 2018). The creation, configuration, and use of these virtual machines will be covered in this study, along with a comparison of the two systems' usability, functionality, and performance. University Assignment Help, A screencast of the project's progress will also be made so that everyone can fully comprehend how it will be put into practise.
Background:
By providing adaptable, scalable, and affordable solutions, cloud computing has completely changed how companies and people manage their IT infrastructure. Access to a shared pool of computing resources, such as servers, storage, databases, networking, and more, is made available on demand via the internet. The two most well-known deployment types as a result of this paradigm change are infrastructure as a service (IaaS) and platform as a service (PaaS). Users can create and operate virtual machines in the cloud with the help of IaaS, which is represented by services like AWS EC2 and GCP VM (Wickham, 2018). This makes it unnecessary to maintain physical hardware and makes easy demand-based scalability possible. IaaS is perfect for users since they have control over the virtual machines' operating system, applications, and configurations. However, PaaS solutions like AWS Elastic Beanstalk and Google App Engine abstract a significant portion of the maintenance of the underlying infrastructure, allowing developers to concentrate on creating and deploying applications. Because of the decreased operational burden, PaaS is appropriate for projects where efficiency and speed are crucial. The requirements, complexity, and desired level of control of the project all influence the decision between IaaS and PaaS. By setting free-tier virtual machines on both AWS and GCP, we concentrate on IaaS in this evaluation. In this case, the right machine instance must be chosen, security groups and firewalls must be set up, and the instances must be accessed remotely. We get insights into the operational complexities of AWS and GCP, assess their respective services, and comprehend the factors to be taken into account when deciding between them by investigating the practical aspects of setting up these virtual machines. The project's comprehensive processes, a comparison of the platforms, and the conclusions from the experience are covered in more detail in the following sections of this report.
Key service offering from Amazon Web Services and Google Cloud Platform
Google Cloud Platform (GCP) and Amazon Web Services (AWS) offer a broad range of services to meet different cloud computing requirements. The following are some of the main services both platforms offer:
AWS: Amazon Web Services
By offering resizable computation capacity in the cloud, Amazon EC2 (Elastic computation Cloud) enables customers to launch and operate virtual machines (instances) in accordance with their requirements (Wickham, 2018).
Large volumes of unstructured data can be stored and retrieved using Amazon S3 (Simple Storage Service), which provides scalable object storage with good durability.
Serverless computing is made possible by AWS Lambda, which enables customers to run code without setting up or managing servers and scales automatically in response to incoming demand.
Managed relational databases are offered by Amazon RDS (Relational Database Service), which also handles administrative responsibilities like provisioning, patching, and backups.
A fully managed NoSQL database service for applications that need seamless scaling and quick data access is Amazon DynamoDB.
Users can build private networks with Amazon VPC (Virtual Private Cloud), which gives them control over IP addressing, subnets, and network gateways.
GCP: Google Cloud Platform
Users can launch and operate virtual machines (VMs) on Google's infrastructure using Google Compute Engine, which provides virtual machine instances with configurable specifications.
Google Cloud Storage offers scalable object storage for a range of data types, making it ideal for content distribution, archiving, and backup.
With the help of Google Cloud Functions, users may create and publish specialised functions that react to cloud events and provide serverless computing.
Managed relational databases with automatic backups, patching, and scaling based on user demand are offered by Google Cloud SQL.
Building online, mobile, and server apps that need real-time synchronisation and offline support can use Google Cloud Firestore, a NoSQL document database.
Similar to AWS VPC, Google VPC (Virtual Private Cloud) provides separated networking for resources placed on GCP, with control over IP addresses and other parameters.
Comparison of cost model of AWS EC2 and GCP VM.
AWS EC2 and Google Cloud Platform (GCP) VM cost models are built slightly differently, reflecting the distinct pricing methods of each cloud provider. The cost structures for AWS EC2 and GCP VM are contrasted here:
Cost Model for AWS EC2:
On-Demand Instances: With this pricing structure, users are charged on an hourly or per-second basis for the computing power they use. Particular instance kinds, including general-purpose, memory-intensive, and GPU instances, are available that are optimised for particular use cases. Users can adjust the scale as necessary.
Reserved Instances: In exchange for lower hourly rates, AWS offers reserved capacity contracts that let customers commit to a certain instance type and term (between one and three years). The following are appropriate uses for stable workloads (Menga, 2018).
Spot Instances: These instances let customers place bids on available EC2 capacity in order to potentially access instances for a lot less money. However, if the capacity is required elsewhere, AWS may abruptly end these instances.
Dedicated Hosts: Users can reserve an entire physical server for their personal usage using this option. This methodology can be used to address software licencing issues or regulatory needs.
VM Cost Model for GCP:
On-Demand Instances: GCP has a pricing structure that is similar to AWS in that customers are billed hourly based on the amount of compute resources they utilise. For diverse use situations, different instance families are optimised.
Committed Use Contracts: GCP offers Committed Use contracts for users who agree to consuming a specific amount of compute resources for 1 or 3 years, much as AWS Reserved Instances. Discounted charges result from this dedication.
Preemptible VMs: GCP's preemptible VMs, which are comparable to AWS Spot Instances in terms of duration and cost, can be used for a fraction of the usual cost (Menga, 2018). However, GCP has the right to cancel them at any time with little advance notice.
Custom Machine Types: Users of GCP are able to construct VM instances with personalised CPU and RAM settings. Users make payments for the particular resources they use.
Sustained Use reductions: As you use instances more frequently during the month, GCP automatically offers usage reductions. The discount increases if you use it more frequently.
The procedure you followed from the registration of your account to the deployment of WordPress with supporting evidence like screenshots and logs.
I started by creating accounts on AWS and GCP using each company's free tier options. I went to the EC2 and GCP Compute Engine terminals after logging in. I started a GCP VM running Debian OS and an EC2 instance running Amazon Linux 2. Instance type selection, storage settings, and security group rules are all shown in screenshots during configuration (Zala, et al. 2022). Logs show that SSH access was established and that connections were successful. I used SSH keys and firewalls to safeguard the instances. I set up the LAMP stack on both machines via SSH. Screenshots show how packages are installed, and logs attest to success. I made MySQL databases and users, checking their validity against command outputs. WordPress download, extraction, and configuration processes are documented in screenshots and logs. Accessing the public IPs after creating virtual hosts revealed WordPress installations (Hussey, 2014). Photographs and logs provide comprehensive evidence of the process.
AWS:
Google cloud
Possible improvement suggestion in the process while remaining within the Cloud domain.
Consider using Infrastructure as Code (IaC) tools like AWS Cloud Formation or Google Cloud Deployment Manager to improve the workflow within the cloud domain (Silva, et al. 2021). By employing declarative templates, these solutions enable automated cloud resource provisioning and management. You may guarantee consistency, repeatability, and version control by explicitly describing the needed architecture and configurations in code. Software installations and setups across instances can also be streamlined by integrating Configuration Management technologies like Ansible or Puppet (Hussey, 2014). These procedures minimise manual interventions, cut down on the possibility of mistakes, and quicken deployments. Utilising databases with cloud-native managed services like AWS RDS or GCP Cloud SQL may also streamline management and boost scalability. While working in the cloud environment, these advancements increase efficiency and maintain best practises.
Comparison of both the Platform EC2 and VM.
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The concept of virtualization and elastic resources in AWS and GCP.
Virtualization:
In order to create virtual instances or environments on a single physical server, virtualization is a key idea in cloud computing. It effectively separates the underlying hardware from the software being executed on it by enabling numerous virtual machines (VMs) to operate independently on the same physical hardware (Queiroz, et al. 2022). Resource separation, effective resource use, and the flexibility to run different operating systems and applications on the same physical hardware are just a few advantages that this abstraction offers. Virtualization plays a significant role in both Google Cloud Platform (GCP) and Amazon Web offerings (AWS) infrastructure offerings.
Elastic Resources:
Elasticity describes a cloud system's capacity to autonomously modify its resources in response to demand. Elastic resources are provided by both AWS and GCP to guarantee top performance and affordability:
Elastic Resources from AWS:
? Elastic Compute Cloud (EC2) by Amazon: EC2 instances are elastic by nature. They are easily scaled up or down in response to changes in workload. To ensure optimum performance and cost savings, auto scaling enables you to dynamically alter the number of instances based on specified conditions (Queiroz, et al. 2022).
? In order to avoid over-provisioning and under-utilization, Amazon RDS (Relational Database Service) provides the ability to employ Auto Scaling to modify database capacity based on usage trends.
? Supporting auto-scaling of containers, Amazon ECS (Elastic Container Service) makes sure that the appropriate amount of resources is allocated to accommodate changing container loads.
GCP Elastic Resources:
? Google Compute Engine: Using Managed Instance Groups, GCP VM instances may be set up to automatically scale based on CPU utilisation or other criteria. Without requiring user intervention, this guarantees consistent application performance.
? Google Kubernetes Engine (GKE): Adjusts the number of containers in a cluster in accordance with set policies to provide autonomous scaling of containerized applications.
? With Google App Engine, applications can automatically scale without human involvement, guaranteeing that resources are available to meet demand.
The capacity to dynamically alter resources to match demand is offered by AWS and GCP, allowing users to retain performance while minimising costs (Queiroz, et al. 2022). A key component of cloud computing, elasticity enables businesses to effectively manage changing workloads and user activity.
Conclusion
The goal of this project was to set up and configure virtual machine instances utilising AWS EC2 and GCP VM services in order to evaluate the actual use of Amazon Web Services (AWS) and Google Cloud Platform (GCP). A variety of cloud services, from registration through the deployment of a WordPress instance, were covered by the comparison analysis. The launch, security, and configuration processes for virtual machines were extensively recorded throughout the assessment, providing important insights into the actions necessary for effective cloud installations. We thoroughly understood the advantages and factors to be taken into account for each platform by contrasting major aspects including instance kinds, pricing schemes, administration tools, and scalability possibilities.
While both AWS and GCP provide reliable cloud infrastructure solutions, our project highlighted their subtle distinctions, enabling us to make selections depending on the particular project requirements. To further our understanding of the platforms, additional research might focus on complex issues like load balancing, hybrid cloud deployments, and integration with other cloud services. Without a question, cloud computing has changed the IT environment, and the information from this study helps users choose between AWS and GCP with knowledge and confidence. Future efforts in cloud computing and infrastructure management can use this project's insights into virtualization, elastic resources, and the practical deployment of cloud services as a springboard.
References
Wickham, M. (2018). Practical java machine learning : projects with google cloud platform and amazon web services. Apress. https://lesa.on.worldcat.org/oclc/1059124819
Menga, J. (2018). Docker on amazon web services : build, deploy, and manage your container applications at scale. Packt. https://lesa.on.worldcat.org/oclc/1051138114
Zala, K., Thakkar, H. K., Jadeja, R., Singh, P., Kotecha, K., & Shukla, M. (2022). Prms: design and development of patients’ e-healthcare records management system for privacy preservation in third party cloud platforms. Ieee Access, Pp(99). https://lesa.on.worldcat.org/oclc/9592478272
Hussey, T. (2014). Wordpress (Ser. Absolute beginner's guide). Que. https://lesa.on.worldcat.org/oclc/877885298
Silva, P., Monteiro, E., & Simoes, P. (2021). Privacy in the cloud: a survey of existing solutions and research challenges. Ieee Access, 9. https://lesa.on.worldcat.org/oclc/8876518224
Queiroz, J., Leitao, P., & Oliveira, E. (2022). A fuzzy logic recommendation system to support the design of cloud-edge data analysis in cyber-physical systems. Ieee Open Journal of the Industrial Electronics Society, 3. https://lesa.on.worldcat.org/oclc/9426438864
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