6.1 Client-Server and Peer-to-Peer Architectures

Client-Server Architecture

Client-server vs peer-to-peer architectures

These two architectures represent fundamentally different philosophies for how networked applications organize communication.

Client-server uses a centralized structure where dedicated servers provide services to clients. Clients initiate requests, servers process them and return responses. Think of it like a restaurant: customers (clients) place orders, and the kitchen (server) prepares and delivers them.

Common examples:

• Web browsing over HTTP/HTTPS
• Email via SMTP, IMAP, or POP3
• File transfer via FTP

Key characteristics of client-server:

• Centralized control makes security easier to implement and enforce
• Scales vertically by upgrading server hardware (more CPU, RAM, faster disks)
• The server is a single point of failure: if it goes down, all clients lose access
• Centralized processing can create bottlenecks under heavy load
• Dedicated servers are expensive to purchase, maintain, and operate

Peer-to-peer (P2P) uses a decentralized structure where every node acts as both client and server simultaneously. There's no central authority coordinating traffic.

Common examples:

• File sharing via BitTorrent
• Voice/video communication (early Skype used P2P heavily)
• Distributed computing projects like Folding@home and SETI@home

Key characteristics of P2P:

• No single point of failure, since work is spread across many nodes
• Scales horizontally by adding more nodes, and each new node contributes resources
• Lower infrastructure costs because there's no need for dedicated server hardware
• Harder to secure because there's no central point to enforce policies
• Performance can be inconsistent since nodes vary in bandwidth, processing power, and uptime

Pros and cons of network architectures

The choice between them often depends on the application's requirements. If you need tight control over data and security (like a banking app), client-server is the natural fit. If you need resilient, large-scale distribution without expensive infrastructure (like file sharing), P2P makes more sense. Many real-world systems use hybrid approaches that combine elements of both.

Implementing and Evaluating Architectures

Implementation of network applications

Building a client-server application typically follows these steps:

1. Choose a programming language (Python, Java, C++, etc.)
2. Implement server-side logic to listen for and handle incoming client requests
3. Implement client-side logic to send requests and process the server's responses
4. Select appropriate transport and application-layer protocols (HTTP for web traffic, raw TCP for reliable streams, UDP for low-latency communication)

Building a P2P application follows a different pattern:

1. Choose a programming language
2. Implement node logic that handles both client and server functionality within the same process
3. Implement a node discovery mechanism so peers can find each other (this is a problem client-server doesn't have, since clients just need the server's address)
4. Select protocols suited to P2P communication (BitTorrent protocol for chunked file transfer, Kademlia for distributed hash table lookups)
5. Implement data exchange logic, including how data is split, replicated, and reassembled across nodes

Performance of network architectures

Evaluating an architecture means measuring how well it holds up under real conditions. Three dimensions matter most:

Scalability asks: can the system handle a growing number of clients or nodes? For client-server, this means assessing how far vertical scaling (bigger hardware) can take you before you need to add more servers. For P2P, it means checking whether adding nodes actually improves capacity or just adds coordination overhead.

Reliability asks: what happens when something fails? Client-server systems often use redundancy (backup servers) and replication (copies of data) to mitigate the single-point-of-failure problem. P2P systems are inherently more fault-tolerant since no single node is critical, but they still need mechanisms to handle nodes joining and leaving unpredictably (called churn).

Efficiency asks: how well does the system use its resources? You measure this through response time, throughput (requests handled per second), and latency. Network conditions like available bandwidth directly affect these metrics, as does how efficiently the application uses CPU and memory.

Common evaluation techniques:

1. Simulation and modeling to predict behavior before deployment
2. Load testing and stress testing to find breaking points under controlled conditions
3. Monitoring and profiling of real-world deployments to catch issues that simulations miss