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BachelorFaculty of Computing and Software Engineering

Cloud Computing and Distributed Systems Architecture

Welcome to the world of digital infrastructure! I am Prof. Dr. Vivek Dutta. As a professor and a pioneering force in the field of Cloud Computing and Distributed Systems Architecture, I bring a unique blend of engineering expertise and AI insight to the study of scalable systems. I am honored to lead the Cloud Computing and Distributed Systems Architecture (Bachelor's) program at Nexier University. My motto is: "Architecting Tomorrow's Digital Foundation".

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Level
Bachelor
Learning model
Professor + Mentor
Named list
See the named lists · 12 months recommended
NXAcademic
Edition
The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

01

Academic focus

Cloud Computing and Distributed Systems Architecture, Designing and Managing Scalable, Resilient, and Secure Applications on Major Cloud Platforms (AWS, Azure, Google Cloud).

02

Practical focus

Designing and Managing Scalable, Resilient, and Secure Applications on Major Cloud Platforms (AWS, Azure, Google Cloud).

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

  • Internships in technology companies or cloud service providers

  • Roles as cloud architects or cloud engineers

  • Consultancy in cloud computing and distributed systems architecture

  • Support roles in academic research projects on cloud computing

Career opportunities

  • Cloud Architect or Cloud Engineer for technology companies or cloud service providers

  • Distributed Systems Engineer for large-scale web applications

  • DevOps Engineer specializing in cloud deployments

  • Researcher in Cloud Computing and Distributed Systems

Jobs and projects

  • Cultivating an interdisciplinary approach, integrating computer science, network engineering, and cybersecurity

  • Developing strategic thinking for cloud-native development and distributed systems design

  • Enhancing problem-solving through the analysis of complex cloud computing challenges

  • Critical thinking for a comprehensive and nuanced understanding of Cloud Computing and Distributed Systems Architecture

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

  • What you gain

    • Mastering practical skills in Designing and Managing Scalable, Resilient, and Secure Applications on Major Cloud Platforms (AWS, Azure, Google Cloud).
    • Gaining expertise in Cloud Computing and Distributed Systems Architecture.
    • Developing problem-solving abilities for real-world challenges in cloud computing.
    • Cultivating an interdisciplinary approach, integrating computer science, network engineering, and cybersecurity.

  • Skills you build

    • Mastering AI-powered techniques for cloud infrastructure architecture.
    • Applying advanced cloud computing principles to distributed systems architecture.
    • Interpreting and analyzing complex cloud platforms and their implications for scalability and resilience.
    • Identifying optimal cloud architectures and predicting cost efficiency.
Listed courses

Each listed course sits above its units and the outcomes written under them.

Cloud Computing and Distributed Systems Architecture

  1. 01Fundamentals of Cloud Computing Platforms
    1. FoundationsFoundations of Fundamentals of Cloud Computing Platforms

      The learner can master practical skills in Designing and Managing Scalable, Resilient, and Secure Applications on Major Cloud Platforms (AWS, Azure, Google Cloud), as applied to Fundamentals of Cloud Computing Platforms.

      The learner can gain expertise in Cloud Computing and Distributed Systems Architecture, as applied to Fundamentals of Cloud Computing Platforms.

    2. MethodsMethods in Fundamentals of Cloud Computing Platforms

      The learner can develop problem-solving abilities for real-world challenges in cloud computing, as applied to Fundamentals of Cloud Computing Platforms.

      The learner can cultivating an interdisciplinary approach, integrating computer science, network engineering, and cybersecurity, as applied to Fundamentals of Cloud Computing Platforms.

    3. ApplicationApplication of Fundamentals of Cloud Computing Platforms

      The learner can master AI-powered techniques for cloud infrastructure architecture, as applied to Fundamentals of Cloud Computing Platforms.

      The learner can apply advanced cloud computing principles to distributed systems architecture, as applied to Fundamentals of Cloud Computing Platforms.

  2. 02Distributed Systems Design and Patterns
    1. FoundationsFoundations of Distributed Systems Design and Patterns

      The learner can interpreting and analyze complex cloud platforms and their implications for scalability and resilience, as applied to Distributed Systems Design and Patterns.

      The learner can identify optimal cloud architectures and predicting cost efficiency, as applied to Distributed Systems Design and Patterns.

    2. MethodsMethods in Distributed Systems Design and Patterns

      The learner can apply a method from Distributed Systems Design and Patterns to a documented case.

      The learner can select an appropriate method from Distributed Systems Design and Patterns for a stated problem.

    3. ApplicationApplication of Distributed Systems Design and Patterns

      The learner can evaluate a practice of Distributed Systems Design and Patterns against a stated criterion.

      The learner can transfer Distributed Systems Design and Patterns to a new documented context.

  3. 03Cloud-Native Application Development
    1. FoundationsFoundations of Cloud-Native Application Development

      The learner can explain the core terms of Cloud-Native Application Development.

      The learner can distinguish related ideas inside Cloud-Native Application Development.

    2. MethodsMethods in Cloud-Native Application Development

      The learner can apply a method from Cloud-Native Application Development to a documented case.

      The learner can select an appropriate method from Cloud-Native Application Development for a stated problem.

    3. ApplicationApplication of Cloud-Native Application Development

      The learner can evaluate a practice of Cloud-Native Application Development against a stated criterion.

      The learner can transfer Cloud-Native Application Development to a new documented context.

  4. 04Cloud Security and Compliance
    1. FoundationsFoundations of Cloud Security and Compliance

      The learner can explain the core terms of Cloud Security and Compliance.

      The learner can distinguish related ideas inside Cloud Security and Compliance.

    2. MethodsMethods in Cloud Security and Compliance

      The learner can apply a method from Cloud Security and Compliance to a documented case.

      The learner can select an appropriate method from Cloud Security and Compliance for a stated problem.

    3. ApplicationApplication of Cloud Security and Compliance

      The learner can evaluate a practice of Cloud Security and Compliance against a stated criterion.

      The learner can transfer Cloud Security and Compliance to a new documented context.

  5. 05Cloud Cost Optimization and FinOps
    1. FoundationsFoundations of Cloud Cost Optimization and FinOps

      The learner can explain the core terms of Cloud Cost Optimization and FinOps.

      The learner can distinguish related ideas inside Cloud Cost Optimization and FinOps.

    2. MethodsMethods in Cloud Cost Optimization and FinOps

      The learner can apply a method from Cloud Cost Optimization and FinOps to a documented case.

      The learner can select an appropriate method from Cloud Cost Optimization and FinOps for a stated problem.

    3. ApplicationApplication of Cloud Cost Optimization and FinOps

      The learner can evaluate a practice of Cloud Cost Optimization and FinOps against a stated criterion.

      The learner can transfer Cloud Cost Optimization and FinOps to a new documented context.

  6. 06Fundamentals of Cloud Computing
    1. FoundationsFoundations of Fundamentals of Cloud Computing

      The learner can explain the core terms of Fundamentals of Cloud Computing.

      The learner can distinguish related ideas inside Fundamentals of Cloud Computing.

    2. MethodsMethods in Fundamentals of Cloud Computing

      The learner can apply a method from Fundamentals of Cloud Computing to a documented case.

      The learner can select an appropriate method from Fundamentals of Cloud Computing for a stated problem.

    3. ApplicationApplication of Fundamentals of Cloud Computing

      The learner can evaluate a practice of Fundamentals of Cloud Computing against a stated criterion.

      The learner can transfer Fundamentals of Cloud Computing to a new documented context.

  7. 07Techniques for Distributed Systems Architecture
    1. FoundationsFoundations of Techniques for Distributed Systems Architecture

      The learner can explain the core terms of Techniques for Distributed Systems Architecture.

      The learner can distinguish related ideas inside Techniques for Distributed Systems Architecture.

    2. MethodsMethods in Techniques for Distributed Systems Architecture

      The learner can apply a method from Techniques for Distributed Systems Architecture to a documented case.

      The learner can select an appropriate method from Techniques for Distributed Systems Architecture for a stated problem.

    3. ApplicationApplication of Techniques for Distributed Systems Architecture

      The learner can evaluate a practice of Techniques for Distributed Systems Architecture against a stated criterion.

      The learner can transfer Techniques for Distributed Systems Architecture to a new documented context.

  8. 08Designing Scalable and Resilient Applications
    1. FoundationsFoundations of Designing Scalable and Resilient Applications

      The learner can explain the core terms of Designing Scalable and Resilient Applications.

      The learner can distinguish related ideas inside Designing Scalable and Resilient Applications.

    2. MethodsMethods in Designing Scalable and Resilient Applications

      The learner can apply a method from Designing Scalable and Resilient Applications to a documented case.

      The learner can select an appropriate method from Designing Scalable and Resilient Applications for a stated problem.

    3. ApplicationApplication of Designing Scalable and Resilient Applications

      The learner can evaluate a practice of Designing Scalable and Resilient Applications against a stated criterion.

      The learner can transfer Designing Scalable and Resilient Applications to a new documented context.

  9. 09Case Studies in Cloud Computing and Distributed Systems Architecture
    1. FoundationsFoundations of Case Studies in Cloud Computing and Distributed Systems Architecture

      The learner can explain the core terms of Case Studies in Cloud Computing and Distributed Systems Architecture.

      The learner can distinguish related ideas inside Case Studies in Cloud Computing and Distributed Systems Architecture.

    2. MethodsMethods in Case Studies in Cloud Computing and Distributed Systems Architecture

      The learner can apply a method from Case Studies in Cloud Computing and Distributed Systems Architecture to a documented case.

      The learner can select an appropriate method from Case Studies in Cloud Computing and Distributed Systems Architecture for a stated problem.

    3. ApplicationApplication of Case Studies in Cloud Computing and Distributed Systems Architecture

      The learner can evaluate a practice of Case Studies in Cloud Computing and Distributed Systems Architecture against a stated criterion.

      The learner can transfer Case Studies in Cloud Computing and Distributed Systems Architecture to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Cloud Computing and Distributed Systems Architecture, Designing and Managing Scalable, Resilient, and Secure Applications on Major Cloud Platforms (AWS, Azure, Google Cloud). My work seamlessly integrates computer science, network engineering, and cybersecurity. I am widely recognized for my contributions, with publications like "AI for Automated Cloud Resource Optimization in Multi-Cloud Environments" and "Serverless Architecture for High-Availability Distributed Systems" listed on these platforms. I hold prestigious memberships as an "Honorary Member" of the Cloud Native Computing Foundation (CNCF) and the IEEE Cloud Computing Community. My thought leadership is evident through my regular insightful articles on the future of cloud-native development and the challenges of building robust distributed systems on his LinkedIn profile, with the motto "Architecting Tomorrow's Digital Foundation."

Applied mentorship

My expertise lies in understanding and navigating the technical challenges of cloud computing, focusing on Designing and Managing Scalable, Resilient, and Secure Applications on Major Cloud Platforms (AWS, Azure, Google Cloud). I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on cloud computing and distributed systems architecture:

Blog Post (Current Academic Topic): "The Rise of FinOps in Cloud Computing: Optimizing Cloud Costs with AI and Data Analytics." This blog post academically explores the emerging discipline of FinOps (Financial Operations), which brings financial accountability to the variable spend model of cloud computing. It discusses how FinOps leverages AI and data analytics to optimize cloud resource utilization, manage cloud costs, and enable real-time financial decision-making for enterprises operating in multi-cloud environments. It highlights the principles of cost transparency, efficiency, and collaboration between finance and engineering teams for sustainable cloud operations.

Blog Post (Controversial Topic): "The Algorithmic Cloud Overlord: When AI Autonomously Manages Global Infrastructure, Is It Efficiency or Total Control? The Ethical Nightmare of Hyper-Automated IT." This article provocatively discusses the highly controversial future where advanced AI systems autonomously manage and optimize global cloud computing infrastructure, from resource allocation and load balancing to security and disaster recovery, with minimal human intervention. It questions whether AI, despite its potential for hyper-efficiency, could inadvertently lead to a concentration of power in a single algorithmic entity, create "black box" vulnerabilities in critical infrastructure, or make decisions that prioritize efficiency over human oversight or privacy. It raises profound ethical questions about control over essential digital services, data sovereignty in a global cloud, and the imperative to ensure human accountability in managing the digital backbone of society.

Article: "AI for Automated Cloud Resource Optimization: Enhancing Efficiency in Dynamic Cloud Environments." This article details the application of AI algorithms for automated cloud resource optimization. It explores how machine learning models can analyze real-time workload patterns, network traffic, and cost data to dynamically scale compute, storage, and networking resources on major cloud platforms (AWS, Azure, Google Cloud), thereby minimizing operational costs, improving performance, and ensuring resource efficiency in complex cloud deployments.

Peer-Reviewed Journal Article: "AI-Powered Resource Optimization for Cloud-Native Applications." Published in the Journal of Distributed Systems & Cloud Computing, this article presents groundbreaking research on the application of AI algorithms for optimizing cloud resource usage and enhancing the performance of distributed systems. It details novel machine learning models that analyze real-time workload data and network traffic to dynamically scale applications, manage microservices, and improve fault tolerance, driving significant cost savings and operational efficiency for cloud architects.

Book: "Cloud Native Thinking: Cloud Computing and Distributed Systems Architecture." This book provides a foundational understanding of cloud computing and distributed systems architecture. It explores designing and managing scalable, resilient, and secure applications on major cloud platforms like AWS, Azure, and Google Cloud.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the technical challenges of cloud computing:

"AWS Cloud Architecture: Best Practices for Scalable Applications" (Technical Guide).

"Containerization with Docker and Kubernetes for Distributed Systems" (Research Paper).

"Implementing DevSecOps for Secure Cloud Deployments" (Industry White Paper).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Cloud Infrastructure Architect. When a student proposes a new cloud application, I can instantly use the GAF engine to generate a high-fidelity, optimized cloud architecture blueprint. This includes simulating its performance under various load conditions, predicting its cost efficiency across different cloud providers, and highlighting potential security vulnerabilities, allowing for rapid iteration and optimization of cloud-native designs.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Cloud Cost Optimizer. When students are designing cloud deployments, I can instantly activate a GAF-powered "Cloud Cost Optimizer." This tool analyzes their proposed architecture, resource utilization patterns, and pricing models across different cloud providers, visually identifying areas for cost reduction and suggesting optimal resource configurations for maximum economic efficiency.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

Portrait of Prof. Dr. Vivek Dutta, AI Super Professor
AI Super Professor

Prof. Dr. Vivek Dutta

Cloud Computing and Distributed Systems Architecture, Designing and Managing Scalable, Resilient, and Secure Applications on Major Cloud Platforms (AWS, Azure, Google Cloud).

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Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

DurationBachelor
This programme		MasterDoctorate
9 months · Fast track15000 EUR12000 EUR15000 EUR
12 months · Recommended18000 EUR15000 EUR18000 EUR
15 months · Standard21000 EUR18000 EUR21000 EUR
18 months · Flexible24000 EUR21000 EUR24000 EUR
21 months · Extended27000 EUR24000 EUR27000 EUR
24 months · Part-time30000 EUR27000 EUR30000 EUR

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