📡Systems Approach to Computer Networks Unit 20 – Distributed Systems & Cloud Computing

Key Concepts

• Distributed systems consist of multiple autonomous computers that communicate through a computer network to achieve a common goal
• Key characteristics of distributed systems include resource sharing, openness, concurrency, scalability, fault tolerance, and transparency
• Challenges in distributed systems involve coordination, synchronization, reliability, and security due to the distributed nature of components
• CAP theorem states that a distributed system can only provide two out of three guarantees: consistency, availability, and partition tolerance
• Consensus algorithms (Paxos, Raft) enable multiple nodes in a distributed system to agree on a single value or state, ensuring consistency
• Eventual consistency is a consistency model where all nodes eventually converge to the same state, allowing for higher availability and partition tolerance
• Distributed algorithms (leader election, mutual exclusion) coordinate activities and maintain consistency across nodes in a distributed system
• Middleware abstracts the complexities of distributed systems, providing a unified programming model and hiding low-level details from developers

Distributed System Architectures

• Client-server architecture consists of clients requesting services from servers, with servers processing requests and returning results to clients
• Peer-to-peer (P2P) architecture enables nodes to act as both clients and servers, allowing direct communication and resource sharing among nodes
  • P2P systems can be structured (DHTs like Chord, Kademlia) or unstructured (Gnutella, BitTorrent)
• Three-tier architecture separates presentation, application processing, and data management into distinct layers, enhancing scalability and maintainability
• Microservices architecture decomposes applications into loosely coupled, independently deployable services that communicate through well-defined APIs
  • Benefits of microservices include flexibility, scalability, and technology diversity
• Event-driven architecture uses events to trigger and communicate between decoupled components, enabling high scalability and responsiveness
• Service-oriented architecture (SOA) organizes distributed systems as interoperable services, promoting reusability and loose coupling
• Serverless architecture relies on cloud providers to manage the underlying infrastructure, allowing developers to focus on writing and deploying code

Cloud Computing Fundamentals

• Cloud computing delivers computing resources (servers, storage, applications) over the internet on a pay-per-use basis
• Essential characteristics of cloud computing include on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service
• Service models in cloud computing:
  • Infrastructure as a Service (IaaS) provides virtualized computing resources (VMs, storage, networks)
  • Platform as a Service (PaaS) offers a development and deployment environment for applications
  • Software as a Service (SaaS) delivers software applications over the internet
• Deployment models in cloud computing:
  • Public cloud is owned and operated by a third-party provider, offering services to the general public
  • Private cloud is dedicated to a single organization, providing more control and security
  • Hybrid cloud combines public and private clouds, allowing workload portability and flexibility
• Cloud providers (Amazon Web Services, Microsoft Azure, Google Cloud Platform) offer a wide range of services and tools for building and deploying distributed systems
• Virtualization enables the creation of multiple virtual machines (VMs) on a single physical server, improving resource utilization and isolation
• Containers (Docker) provide a lightweight alternative to VMs, packaging applications and their dependencies into portable units

Networking in Distributed Systems

• Computer networks enable communication and resource sharing among nodes in a distributed system
• Network protocols (TCP/IP, UDP) define the rules and formats for data transmission and ensure reliable communication between nodes
• Remote procedure calls (RPC) allow nodes to invoke procedures on remote nodes as if they were local, abstracting network communication details
• Message-oriented middleware (MOM) enables asynchronous communication between nodes using message queues and publish-subscribe models
• Overlay networks create virtual topologies on top of physical networks, enabling efficient routing and content distribution in distributed systems
• Content delivery networks (CDNs) distribute content across geographically dispersed servers, improving performance and availability for end-users
• Software-defined networking (SDN) separates the control plane from the data plane, enabling centralized network management and programmability
• Network security measures (firewalls, VPNs, encryption) protect distributed systems from unauthorized access and ensure data confidentiality and integrity

Data Management and Storage

• Distributed databases store and manage data across multiple nodes, providing scalability, fault tolerance, and high availability
• Replication involves creating multiple copies of data across nodes to ensure data availability and fault tolerance
  • Replication strategies include master-slave, multi-master, and peer-to-peer replication
• Partitioning (sharding) divides data into smaller subsets and distributes them across nodes, enabling horizontal scalability
• Distributed file systems (HDFS, GFS) store and manage large datasets across multiple nodes, providing fault tolerance and parallel processing capabilities
• NoSQL databases (MongoDB, Cassandra) offer flexible data models and high scalability for handling unstructured and semi-structured data
• Distributed caching (Redis, Memcached) improves performance by storing frequently accessed data in memory across multiple nodes
• Distributed transaction processing ensures the consistency and integrity of data in the presence of concurrent access and failures
• Data consistency models (strong consistency, eventual consistency) define the guarantees provided by a distributed storage system regarding data updates

Scalability and Load Balancing

• Scalability refers to a system's ability to handle increased workload by adding more resources (horizontal scaling) or increasing existing resources (vertical scaling)
• Load balancing distributes incoming requests across multiple nodes to optimize resource utilization and improve performance
  • Load balancing algorithms include round-robin, least connections, and IP hash
• Elastic scaling automatically adjusts the number of resources based on the workload, ensuring optimal performance and cost-efficiency
• Caching reduces the load on backend systems by storing frequently accessed data in memory, improving response times and reducing network traffic
• Content delivery networks (CDNs) distribute content across geographically dispersed servers, reducing latency and improving scalability for content delivery
• Distributed message queues (Kafka, RabbitMQ) decouple producers and consumers, enabling asynchronous processing and improving scalability
• Autoscaling automatically adjusts the number of instances based on predefined metrics (CPU utilization, request rate) to handle varying workloads
• Capacity planning involves estimating future resource requirements and ensuring that the system can handle the expected workload

Security and Privacy Concerns

• Authentication verifies the identity of users or nodes in a distributed system, ensuring that only authorized entities can access resources
• Authorization controls access to resources based on the authenticated identity, enforcing access control policies
• Encryption protects data confidentiality by converting plaintext into ciphertext, preventing unauthorized access to sensitive information
  • Symmetric encryption uses the same key for encryption and decryption (AES, DES)
  • Asymmetric encryption uses a pair of keys: a public key for encryption and a private key for decryption (RSA, ECC)
• Secure communication protocols (HTTPS, SSL/TLS) establish secure channels for data transmission, protecting against eavesdropping and tampering
• Distributed denial-of-service (DDoS) attacks overwhelm a system with a flood of traffic, disrupting its availability
  • DDoS mitigation techniques include traffic filtering, rate limiting, and using content delivery networks (CDNs)
• Data privacy regulations (GDPR, CCPA) impose requirements on the collection, storage, and processing of personal data in distributed systems
• Secure key management involves the generation, distribution, storage, and rotation of cryptographic keys used for encryption and authentication
• Auditing and logging mechanisms track system activities and detect security breaches or unauthorized access attempts

Real-World Applications and Case Studies

• Distributed databases (Cassandra, MongoDB) are used by companies like Netflix and eBay to store and manage large-scale data across multiple nodes
• Blockchain technology (Bitcoin, Ethereum) enables decentralized applications (DApps) and secure, transparent transactions without intermediaries
• Distributed file systems (HDFS) and processing frameworks (MapReduce, Spark) power big data analytics at companies like Facebook and Uber
• Content delivery networks (Akamai, Cloudflare) optimize content delivery for streaming platforms (YouTube, Netflix) and e-commerce websites (Amazon)
• Microservices architecture is adopted by companies like Amazon, Netflix, and Spotify to build scalable and maintainable applications
• Peer-to-peer networks (BitTorrent, IPFS) enable efficient file sharing and content distribution without central servers
• Edge computing brings computation and data storage closer to the source of data, enabling low-latency applications like autonomous vehicles and IoT devices
• Distributed machine learning frameworks (TensorFlow, PyTorch) allow training and deployment of machine learning models across multiple nodes