🔄 Switching Mechanisms & Distributed Computing Systems

🔄 Switching Mechanisms & Distributed Computing Systems

Course Code: 27015-COs-CS
Academy: Engineering Office for Technology and Software Services

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🎯 Target Audience

• Computer Science and Software Engineering Students

• Network and Cloud Engineers

• Developers interested in distributed and parallel computing systems

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🕒 Course Duration

• 8 Weeks

• 16 Sessions

• 1.5 Hours per Session

• Total: 24 Hours

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✅ Course Objectives

• Understand distributed and parallel computing systems

• Learn switching techniques in communication and distributed networks

• Explore cloud-native tools like containers and orchestration

• Apply hands-on experience using simulation and real deployment environments

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📚 Course Modules

🔹 Module 1: Introduction to Distributed Systems

• What is a Distributed System?

• Comparison: Centralized vs Distributed vs Parallel Systems

• Real-world Examples: Google Cloud, Netflix, Blockchain

• Core Components: Clients, Servers, Middleware

🔹 Module 2: Switching Mechanisms in Distributed Systems

• Importance of Switching in Networking and Distributed Computing

• Packet Switching: Principles, Pros & Cons

• Circuit Switching: When and Why

• Message Switching: Characteristics & Comparisons

• Comparison of Switching Techniques

• Hands-on: Network simulation tools for switching

🔹 Module 3: Distributed Computing Systems

• What is Distributed Computing?

• Architecture Models: Client-Server, Peer-to-Peer

• Distributed System Architectures

• Communication Protocols: RPC, REST, gRPC

• Hands-on: Build a simple distributed application

🔹 Module 4: Parallel vs Distributed Computing

• Key Differences between Parallel and Distributed Computing

• Examples of Parallel Computing: Multi-threading, GPU

• Role of Parallel Computing in Distributed Systems

• Tools: OpenMP, MPI

🔹 Module 5: Advanced Parallel & Distributed Computing

• Designing Parallel & Distributed Algorithms

• Challenges: Synchronization, Consistency, Scalability

• Fault Tolerance and Replication Techniques

• Practical Exercises using MPI and Hadoop

🔹 Module 6: Security in Distributed & Parallel Systems

• Security Challenges in Distributed Environments

• Data Encryption and Secure Communication

• Authentication & Authorization Mechanisms

• Hands-on: Securing a distributed system application

🔹 Module 7: Cloud Computing and Containers

• Integration between Cloud and Distributed Systems

• Importance of Containers in scalable architectures

• Tools: Docker and Kubernetes

• Hands-on: Deploy a microservices-based distributed app with Kubernetes

🔹 Module 8: Capstone Projects and Real-World Applications

• Design a scalable distributed system with parallel components

• Build applications using Microservices Architecture

• Performance monitoring and optimization

• Final Presentation and Evaluation

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🛠 Tools & Platforms Used

• Docker & Kubernetes – Containerization and orchestration

• Apache Kafka, RabbitMQ – Distributed messaging

• MPI, OpenMP – Parallel programming

• Hadoop, Spark – Big Data distributed computing

• Wireshark – Network analysis

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📌 Prerequisites

• Solid understanding of TCP/IP and networking fundamentals

• Basic knowledge of databases and data structures

• Programming experience (preferably in Java or Python)

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🏅 Deliverables

• Certificate of Completion

• Final Capstone Project

• Source code repository and documentation

• Deployment guides and performance reports