In the domain of cloud computing, there are numerous projects emerging continuously in recent years. Talk with our technical experts to discuss all relevant ideas related to your thesis interest in cloud stay in touch with phdprime.com. We offer projects that utilize different cloud services and are capable to assist you to obtain realistic expertise in cloud computing:
- Cloud-based Online Learning Platform
- Goal: An online learning environment has to be constructed in such a manner that permits the users to access programs, carry out puzzles and monitor their advancement.
- Significant Services: AWS S3, RDS, EC2, CloudFront, Lambda, Cognito.
- Characteristics:
- Employing AWS Cognito for user authentication and management.
- Make use of CloudFront for CDN delivery and content storage in S3.
- With the help of Lambda, perform dynamic content generation and backend logic.
- Utilize RDS for relational database management.
- IoT-based Smart Agriculture System
- Goal: In order to track and handle farming situations remotely, aim to construct an IoT framework.
- Significant Services: AWS IoT Core, Lambda, SNS, S3, Dynamo.
- Characteristics:
- Through the utilization of AWS IoT Core, gather data from sensors such as soil dampness, temperature, humidity.
- This project enables data processing and saving in DynamoDB.
- By means of SNS, it creates warnings and alarms.
- Employing S3 for storage purposes, visualize previous data and patterns, and utilize a conventional dashboard.
- Real-time Collaborative Document Editing
- Goal: It is approachable to execute a web-related application that facilitates numerous users to cooperate on documents in an actual-time.
- Significant Services: AppSync, Cognito, AWS Amplify, DynamoDB.
- Characteristics:
- Employ AppSync and Amplify for user authentication and actual-time cooperation.
- In DynamoDB, save document variations.
- This study enables comparing resolution and version control.
- Serverless E-commerce Website
- Goal: Encompassing shopping cart, product list, and verification processes, develop an entirely serverless e-commerce blog or website.
- Significant Services: API Gateway, S3, Cognito, AWS Lambda, DynamoDB, CloudFront.
- Characteristics:
- In S3, product catalog has to be saved, and delivered through CloudFront.
- Through the utilization of Cognito, it facilitates user authentication.
- By means of Lambda functions that are created by API Gateway, business logic has to be managed.
- Employing DynamoDB for cart and order management.
- Healthcare Management System
- Goal: Specifically, for patient logs, appointment planning, and telemedicine, construct a cloud-related healthcare management model.
- Significant Services: AWS RDS, Elastic Beanstalk, EC2, SNS, S3.
- Characteristics:
- In RDS, this project facilitates safe storage of patient logs.
- Through the utilization of SNS, it supports appointment planning and remembrances.
- Typically, it facilitates file storage for medical logs in S3.
- Utilizing Elastic Beanstalk, implement application for scalability.
- Big Data Analytics Platform
- Goal: As a means to carry out big data analytics on extensive datasets for visualization and perceptions, it is appreciable to develop a suitable environment.
- Significant Services: S3, QuickSight, AWS EMR, Lambda, Athena.
- Characteristics:
- Generally, this study enables data incorporation and storage in S3.
- By employing Athena and EMR, it facilitates data processing and exploration.
- Through utilizing QuickSight, enables visualization and documenting.
- Employ Lambda for automatic data processes.
- Cloud-based Disaster Recovery Solution
- Goal: Specifically, for significant applications and data, it is appreciable to execute a disaster recovery approach.
- Significant Services: AWS RDS, CloudFormation, Backup, S3.
- Characteristics:
- Through the utilization of AWS Backup, this project facilitates autonomous backup of databases and file frameworks.
- By means of versioning, save backups in S3.
- As a means to computerize architecture recovery, employ CloudFormation.
- It enables consistent evaluation and authentication of retrieval processes.
- Blockchain Application on AWS
- Goal: For safe and clear dealings, focus on constructing a blockchain application.
- Significant Services: Amazon Managed Blockchain, DynamoDB, Lambda, API Gateway.
- Characteristics:
- By employing Amazon Managed Blockchain, configure a blockchain network.
- This study facilitates the creation of smart contracts and backend logic by means of Lambda function.
- Mainly, in DynamoDB, save transactional data.
- Through API Gateway, it reveals API endpoints.
- Personal Finance Management App
- Goal: A cloud-related application has to be developed in such a manner for handling finances, budgeting, and cost monitoring.
- Significant Services: AWS DynamoDB, Cognito, Amplify, Lambda.
- Characteristics:
- With the help of Cognito, this project facilitates user authentication.
- It assists financial data storage and management in DynamoDB.
- Through the utilization of Lambda, it supports budgeting and cost monitoring logic.
- By means of AWS Amplify, it enables a receptive web interface.
- Machine Learning Model Deployment
- Goal: For predictive analytics, aim to implement and handle a machine learning framework.
- Significant Services: AWS S3, API Gateway, SageMaker, Lambda.
- Characteristics:
- Through employing SageMaker, instruct and implement a machine learning system.
- This study enables saving training data in S3.
- By utilizing Lambda and API Gateway, develop an interpretation endpoint.
- It enables computerizing retraining and framework upgrades.
How to write Performance analysis results in cloud computing?
The process of writing performance analysis outcomes is considered as challenging as well as interesting. We suggest a formatted technique that assists you to report the outcomes of your performance analysis in an efficient manner:
- Introduction
- Goal: The objective of the performance analysis has to be mentioned in a concise manner.
- Range: Encompassing what factors of performance are being accessed such as scalability, cost effectiveness, delay, throughput, aim to explain the range of your analysis.
- Methodology
- Environment Setup: Generally, involving information of the cloud supplier, instance kinds, arrangements, and any certain scenarios or enhancements implemented, it is appreciable to define the cloud platform employed for the analysis.
- Tools and Metrics: The tools and parameters employed for the performance analysis have to be mentioned. For instance, you might utilize JMeter for load testing, CloudWatch for tracking, and certain parameters such as memory usage, network latency, CPU utilization, and response time.
- Workload Description: The kind of workload or implementations examined, involving their features and why they were selected for this analysis has to be described in an explicit manner.
- Experimental Design
- Test Settings: It is appreciable to explain the various evaluation settings or arrangements assessed. Typically, differing the number of instances, various instance kinds, various areas, etc., could be encompassed.
- Data Gathering: Encompassing how long every evaluation was executed and in what way data was gathered and saved, summarize the data gathering procedure.
- Baseline and Comparisons: Specifically, for comparison purposes, explain the model. Aim to define other models or any benchmarks employed for comparison.
- Results
- Graphs and Tables: Through the utilization of charts, graphs, and tables, focus on depicting the outcomes. It is advisable to make sure that every table/graph contains an explicit title, labeled axes, and legends whenever it is essential.
- Instance Metrics:
- Latency: This metric includes average, minimum, and maximum response times.
- Throughput: It encompasses the number of requests or transactions managed per second.
- Resource Utilization: The resource utilization involves CPU, memory, and disk utilization periodically.
- Cost Analysis: This parameter includes the comparison of cost efficacy among various arrangements or cloud suppliers.
- Summary Statistics: For every parameter, like median, percentiles, mean, and standard deviation, it offers summary statistics.
- Analysis and Discussion
- Performance Trends: The examined performance patterns have to be described. For instance, focus on describing for what reason specific arrangements worked efficiently or worse.
- Bottlenecks: It is appreciable to detect any performance blockages and their possible reasons.
- Scalability: Typically in enhanced load or resource allocation, to what extent the model adapts has to be examined.
- Cost-Performance Trade-offs: Emphasizing any efficient arrangements, describe the trade-offs among expense and performance.
- Suggestions
- Optimizations: On the basis of the analysis, aim to recommend possible improvements. Generally, suggestions for instance types, scaling policies, or certain arrangements could be encompassed.
- Best Practices: In the same cloud platforms, distribute effective approaches for attaining best efficiency and cost efficacy.
- Conclusion
- Summary: In this section, focus on outlining the major outcomes of the performance analysis.
- Future Work: For upcoming exploration or further investigation, recommend beneficial regions, involving any challenges of the recent research and in what way they might be solved.
- Appendices (if necessary)
- Raw Data: In appendices segment, encompass extensive records or raw data for reference purpose.
- Scripts and Configurations: It is appreciable to offer any scripts, arrangement files, or extensive configuration guidelines that are employed at the time of the analysis.
Instance Structure:
Introduction
Through the utilization of various instance types and arrangements, the process of assessing the cost effectiveness and scalability of implementing a web application on AWS is the main consideration of this performance analysis.
Methodology
Among various areas, we employed AWS EC2 instances such as t2.micro, t2.large, and m5.large. Employ JMeter and CloudWatch for load testing, to examine performance parameters like response time, CPU consumption, and memory utilization.
Experimental Design
As a means to simulate low, medium, and high load situations, three evaluation settings were formulated. For every setting, data was gathered over one whole day. By employing a single t2.micro instance, a model was created.
Results
Consider the performance metrics that are depicted below:
- Latency:
- Average response time for t2.micro: 200ms
- Average response time for t2.large: 150ms
- Average response time for m5.large: 100ms
- Throughput:
- micro: 50 requests/sec
- large: 150 requests/sec
- large: 300 requests/sec
Cloud Based Thesis for Engineering Students
An interesting Cloud Based Thesis for Engineering Students that are trending among scholars and used frequently are listed in this page, we work on all types of Cloud Based Projects. Work with us and experience our elite services. By utilizing our massive resources we finish of your work ontime.
- A secure and privacy protection digital goods trading scheme in cloud computing
- SAMI: Service-based arbitrated multi-tier infrastructure for Mobile Cloud Computing
- Energy-saving cloud computing platform based on micro-embedded system
- CloudAnalyst: A CloudSim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications
- Time efficient dynamic threshold-based load balancing technique for Cloud Computing
- Experimental study of Cloud Computing based SCADA in Electrical Power Systems
- Energy Efficient Task Offloading in Mobile Based Cloud Computing Environment
- On Massive Data Storage Security in Cloud Computing with RaptorQ codes
- Hybrid Provable Data Possession at Untrusted Stores in Cloud Computing
- SONA: A service oriented nodes architecture for developing Cloud Computing applications
- Research on Constructing the Standard Architecture of Educational Cloud Computing
- Design of Cloud Computing System for Homes Electric Energy Larceny Detection
- Service-Oriented Computing and Cloud Computing: Challenges and Opportunities
- Analysis of the Performance, Scalability, Availability, and Security of Cloud Computing in Different Cloud Environments
- Implementation Approach for IDS based on Risk Assessment and Attack Pattern in Cloud Computing
- DLAS: Data Location Assurance Service for cloud computing environments
- An Efficient Distributed Approach for Load Balancing in Cloud Computing
- An Energy Efficient Data Privacy Scheme for IoT Devices in Mobile Cloud Computing
- Securing user authentication using single sign-on in Cloud Computing
- A flexible distributed storage integrity auditing mechanism in Cloud Computing
