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🎙 Audio Overview, 1-7

🎧 Distributed Systems (to spice up🧂your studies)

🎙 Chapter 1

🎙 Chapter 2

🎙 Chapter 3

DISTRIBUTED SYSTEMS 🌐
Maarten Van Steen ✍️
Andrew S. Tanenbaum ✍️
4th Edition 📖
Version 02 🔢

🛑 Before you delve into the textbook, take a look at these precise notes 👇—just to see what you're about to sign up for 📚. You can either treat it as an appetizer 🍤 or a dessert 🍰, your choice! 🍽

Chapter 1️⃣: Introduction to Distributed Systems 🌐

Overview 📖

Distributed systems consist of independent computers that collaborate to appear as a single coherent system.

Analogy: Think of it as a symphony orchestra where different instruments play in harmony to produce a unified melody. 🎻🎺

Key Concepts 🔑

Evolution of Distributed Systems 📜

🔹Early Systems: Before 1980, computers were large, expensive, and operated independently.
🔹Technological Advancements:
⚙️ Microprocessors: Transition from 8-bit to 64-bit CPUs; modern systems have the power of mainframes from decades ago.
🌐 Networking: LANs and WANs allowed high-speed communication, connecting millions of devices globally.
📱 Miniaturization: Smartphones and nano-computers, like Raspberry Pi, are powerful yet compact.
🔹Integration: Systems like cloud computing and IoT showcase distributed systems managing diverse tasks across various devices.

Distributed vs. Decentralized Systems 🔄

🔘Distributed: Resources are sufficiently spread for efficiency and reliability (e.g., Google Mail servers).
🔘Decentralized: Resources are necessarily spread, often driven by administrative boundaries or lack of trust (e.g., blockchain systems).

Misconception: Centralized systems are not inherently bad—they can be robust and scalable when designed well (e.g., DNS root servers).

Design Goals 🏗

1. Resource Sharing 🌍:
- Canonical Examples: Cloud storage, multimedia streaming, email services.
- Quote: “The network is the computer” – John Gage.

2. Distribution Transparency 🕵️‍♂️:
- Hides complexity of physical distribution via middleware layers.
- Types: Access, Location, Replication, Migration, Concurrency, Failure.
- Challenge: Full transparency may reduce performance and increase latency.

3. Openness ⚙️:
- Interoperability and extensibility via well-defined interfaces.
- Example: Systems supporting multiple programming languages.

4. Dependability 🔒:
- Metrics: Availability ( 𝐴 = MTBF / MTBF + MTTR), Reliability, Safety, Maintainability.
- Fault handling strategies: Prevention, Tolerance, Removal, Forecasting.

5. Scalability 📈:
- Types: Size (users/processes), Geographical (distances), Administrative (domains).
- Example: CDNs efficiently distribute content for performance and fault tolerance.

Classification of Distributed Systems 📊

⚡️High-Performance Computing
- High-performance systems for intensive tasks.
- Clusters and grids for intensive tasks like scientific simulations.

📂 Distributed Information Systems:
- Data sharing/processing.
- Database management and web services.

🌐 Pervasive Systems:
- Small, self-organizing, sensor-rich systems .
- IoT devices embedded in everyday environments.

Pitfalls ⚠️

▪️Heterogeneity: Managing diverse hardware and software.
▪️Scalability Issues: Unanticipated growth can overwhelm resources.
▪️Partial Failures: Some nodes may fail without affecting the whole system, leading to complex recovery mechanisms.
▪️Security Risks: Networked systems are vulnerable to attacks.

🌐 Summary ✨

🔅A distributed system is a network of computers where processes and resources are spread across multiple machines. 🤝💻

🔅Key distinction:
👉🏻Sufficiently spread: Focused on improving efficiency. ⚡️
👉🏻Necessarily spread: Decentralized systems for operational or trust-based reasons. 🌍

🔅Why distribute?
- Enhance reliability 🔒, scalability 📈, and efficiency ⚡️.
- Not an end goal but a solution to improve performance.

🔅Challenges:
▫️Centralized systems are easier to manage 🛠 but may not meet all needs.
▫️Distribution is necessary when:
- Connecting systems across different organizations 🏢.
- Supporting geographically separated devices (e.g., mobile computing). 📱


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Chapter 2️⃣: Architectures 🏗

Overview 📖

❇️ Architectures define how distributed systems are logically and physically structured, focusing on the arrangement of components, their interactions, and system-wide performance.

Analogy: Architectures are like blueprints for a building, ensuring all rooms (components) are connected effectively and serve their purpose. 🏠

Key Architectural Styles 🔑

1. Layered Architectures 🏢
Concept: Divide components into layers where each serves the one above it.
Analogy: Like stacking pancakes, each layer builds on the previous one. 🥞
Benefits: Encapsulation, modularity, and ease of maintenance.
Example: OSI model and network protocol stacks.

2. Service-Oriented Architectures (SOA) ⚙️
Definition: Organize systems into loosely coupled services communicating over standardized interfaces.
Applications: Microservices, cloud-based APIs.
Example: Amazon S3 uses RESTful operations like GET, POST, DELETE.

3. Publish-Subscribe Architectures 📬
Definition: Decouple publishers (message producers) and subscribers (message consumers).
Key Mechanism: Event-driven communication, where consumers subscribe to specific events.
Use Cases: Stock price notifications, IoT device updates.

Middleware 🧩

Role: Acts as a bridge between distributed components, providing distribution transparency.
Features:
- Transparency: Access, location, migration, replication.
- Flexibility: Supports heterogeneous systems.

System Architectures 🖥

1. Client-Server Model 🤝
- Concept: Centralized interaction where clients request services from servers.
- Example: Traditional websites like Wikipedia.
- Challenge: Single point of failure without replication or failover strategies.

2. Peer-to-Peer (P2P) Systems 🔄
- Concept: Nodes are equal participants, acting as both clients and servers.
- Example: File-sharing networks like BitTorrent.
- Strengths: Decentralization, fault tolerance, and scalability.

3. Hybrid Systems ⚡️
- Definition: Combine centralized and decentralized elements for better performance and reliability.
- Example: Cloud systems with edge computing.

Modern Architectures 🌟

1. Cloud Computing ☁️
Layers: Infrastructure (IaaS), Platform (PaaS), Software (SaaS).
Benefits: On-demand scalability, cost-efficiency, and global accessibility.

2. Edge Computing 📶
Concept: Push computation closer to data sources (e.g., IoT).
Benefit: Reduced latency and bandwidth usage.

3. Blockchain Architectures 📦🔗
Definition: Decentralized systems for secure and immutable transaction records.
Types:
- Permissioned: Private networks (e.g., Hyperledger).
- Permissionless: Public networks (e.g., Bitcoin).

Architectural Comparison

🖥 Centralized: One server managing all requests.
🌐 Decentralized: Multiple interconnected servers sharing responsibility.
🧩 Distributed: Processes and data spread across multiple nodes for reliability.

Challenges

⚖️ Trade-offs between consistency, availability, and partition tolerance (CAP theorem).
🟰 Balancing transparency with system performance.

🗺 Summary ✨

Distributed system architectures guide the design of scalable, efficient, and robust systems. From layered designs to modern blockchain solutions, these architectures address diverse challenges and use cases. 🛠

Analogy: They’re the map that ensures all parts of the system work in harmony!


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Chapter 3️⃣: Processes 🚦

Overview 📖

❇️ Processes are independent executing entities that may consist of one or more threads, forming the foundation for distributed systems.
- They enable multitasking, virtualization, and effective resource management.

Threads 🧵

What Are Threads?
- Threads are the smallest units of CPU scheduling and execution within processes.
- They allow parallelism and efficient utilization of resources.

🔘Processor: Executes instructions in a predefined sequence.
🔘Thread: Minimal software processor for executing instructions in a process.
🔘Process: A container for one or more threads.

Thread Contexts 🔧
▪️Processor Context: Register values for executing instructions (e.g., program counter, stack pointer).
▪️Thread Context: Includes processor context and additional state.
▪️Process Context: Adds memory management info (e.g., MMU registers).

Why Use Threads ❔
✔️Parallelism: Exploit multi-core processors for concurrent operations.
✔️Reduced Blocking: Avoid halting operations during I/O.
✔️Lightweight Switching: Thread switches are cheaper than process switches.

⚖️ Trade-Offs:
- Threads share an address space, leading to potential errors.
- Faster context switching vs. OS-managed memory protection.

Virtualization 🖥

Virtualization separates the interface from the underlying physical implementation, allowing abstraction and flexibility.

Principle of Virtualization:
🪞Mimics hardware/software interfaces for portability, migration, and failure isolation.
🔑 Key Solutions:
📦 Containers: Share OS resources, use namespaces for isolation.
🛡 Virtual Machines (VMs): Full OS and resource replication, suited for strong isolation.

Clients and Servers 💻🌐

Multithreaded Clients 🧵🌐
🚀 Enhance performance by fetching resources (e.g., files, web content) concurrently with threads.
🔄 Improves Thread-Level Parallelism (TLP): A measure of how well threads overlap in execution.

Server Models 🖥⚙️
🛑 Single-threaded Server: Blocks on requests, limiting scalability.
⚡️ Multithreaded Server: Handles multiple clients using separate threads, hiding latencies and enhancing performance.
🕹 Dispatcher-Worker Model:
🎯 Dispatcher assigns tasks to workers.
🛠 Worker threads handle individual tasks, enhancing modularity and speed.

Code Migration 🚀

Why Migrate Code?

- Minimize data transfer by moving computation closer to resources.
- Load distribution: ensuring that servers in a data center are sufficiently loaded (e.g., to prevent waste of energy)
- Privacy and security: in many cases, one cannot move data to another location, for whatever reason(often legal ones). Solution: move the code to the data.
- Adapt to environment constraints like hardware or network limitations.

🔹Object components
• Code segment: contains the actual code
• Data segment: contains the state
• Execution state: contains context of thread executing the object’s code

🫤 Weak Mobility: Move only code and data segment (and reboot
execution)
• Relatively simple, especially if code is portable
• Distinguish code shipping (push) from code fetching (pull)

💪Strong Mobility: Move component, including execution state
• Migration: move entire object from one machine to the other
• Cloning: start a clone, and set it in the same execution state.

Migration in Heterogeneous Systems 🚚🌍

🚫 Main Challenges
- Target machine incompatibility: Migrated code may not execute properly on different hardware or OS.
- Process/Thread/Processor context is deeply tied to local hardware and runtime systems.

💡Solution
Abstract machines implemented across platforms:
🖥 Interpreted Languages: Utilize their own virtual machines (e.g., Java).
🔒 Virtual Machine Monitors: Abstract execution layers across platforms.

Summary 🌟

Processes and threads form the backbone of distributed systems by enabling multitasking, virtualization, and scalable client-server interactions. Virtualization extends these concepts for robust and flexible applications.


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