System Design - Scalability: Load Balancing Test 2

Load Balancing Fundamentals

• Layer 4 load balancers are less CPU-intensive and offer higher performance because they operate at the transport layer and do not inspect packet contents.
• Layer 7 load balancers operate at the application layer, making routing decisions based on detailed application-level data, enabling advanced features like SSL termination and content-based routing.

Consistent Hashing

• Consistent hashing minimizes the re-distribution of requests when servers are added or removed, reducing the impact on the system and maintaining a stable distribution of requests.

Connection Draining

• Connection draining allows existing connections to continue until they complete while preventing new connections from being established, ensuring a graceful shutdown of backend servers and maintaining service continuity during maintenance or scaling operations.

Anycast Network

• An anycast network allows multiple servers in different locations to share the same IP address, routing traffic to the nearest server based on the shortest path determined by the network.
• Anycast networks improve latency, provide redundancy, and enhance fault tolerance.

Stateful Applications

• Load balancers handle stateful applications by using techniques like session persistence (sticky sessions) or storing session state in a shared database or distributed cache.
• Techniques include IP hashing, cookies, or application-level tokens to ensure requests from the same client are routed to the same server.

SSL/TLS Offloading

• SSL/TLS offloading improves backend server performance by offloading the CPU-intensive task of encrypting and decrypting SSL/TLS traffic to the load balancer.
• This reduces the load on backend servers, allowing them to handle more application-specific processing, but increases the load on the load balancer.

Scaling Strategies

• Horizontal scaling involves adding more servers to distribute the load, enhancing redundancy and fault tolerance.
• Vertical scaling involves increasing the resources (CPU, memory) of existing servers.
• Horizontal scaling is more flexible and can handle larger increases in load, while vertical scaling is limited by the capacity of individual servers.

Rate Limiting and Traffic Shaping

• Rate limiting controls the rate at which requests are processed to prevent overload, while traffic shaping adjusts the flow of traffic based on predefined policies.
• These techniques can be implemented using rules and algorithms at the load balancer to ensure fair usage and protect backend servers from spikes in traffic.

Split-Brain Syndrome

• Split-brain syndrome occurs when network partitions cause multiple load balancers or clusters to operate independently, leading to data inconsistency and service disruption.
• It can be mitigated using quorum-based algorithms, distributed consensus protocols like Raft or Paxos, and network redundancy to ensure only one active partition.

Observability and Telemetry

• Observability and telemetry provide insights into system performance, traffic patterns, and server health.
• By collecting and analyzing metrics, logs, and traces, administrators can optimize load balancing strategies, detect anomalies, and make data-driven decisions to improve system efficiency and reliability.

SSL/TLS Re-Encryption

• SSL/TLS re-encryption involves decrypting incoming traffic at the load balancer and re-encrypting it before sending it to backend servers.
• This approach ensures secure communication within the internal network, providing an additional layer of security for sensitive data.

Service Mesh

• A service mesh provides fine-grained traffic management, observability, and security for microservices.
• Benefits include enhanced traffic control, improved security, and easier management of service-to-service communication.
• Challenges include increased complexity, potential performance overhead, and the need for robust infrastructure to manage the mesh.

Network Latency

• Network latency affects load balancing decisions by influencing response times and user experience.
• It can be minimized by using geographically distributed servers, edge load balancing, optimizing routing algorithms, and employing latency-aware load balancing techniques.

Elastic Load Balancer (ELB)

• An ELB in AWS is a managed service that provides automatic scaling, health checks, and integration with other AWS services.
• It simplifies load balancing by handling infrastructure management, scaling, and maintenance, allowing users to focus on application logic.

Health Probes

• Health probes monitor the health of backend servers by sending periodic requests to endpoints.
• If a server fails to respond or returns an error, the load balancer removes it from the pool, ensuring only healthy servers receive traffic.

Load Balancer Types

• Static load balancing uses predefined rules and configurations, suitable for stable and predictable environments.
• Dynamic load balancing adapts to real-time changes in traffic and server performance, ideal for environments with fluctuating loads and varying server conditions.

CI/CD Pipelines

• Load balancers integrate with CI/CD pipelines by providing automated deployment, scaling, and rollback capabilities.
• They can route traffic to different application versions for canary releases, A/B testing, and blue-green deployments, ensuring seamless updates and minimizing downtime.

Fault Tolerance

• Fault tolerance ensures system reliability and availability during failures.
• Strategies include using multiple load balancers in active-active or active-passive configurations, employing health checks, implementing automatic failover mechanisms, and distributing traffic across multiple geographic locations to avoid single points of failure.

Backpressure

• Backpressure is a flow control mechanism that prevents overwhelming a system by slowing down the production of requests when the system is under heavy load.
• In load balancing, backpressure can be implemented by adjusting traffic distribution based on server load and capacity, ensuring stability and preventing resource exhaustion.

Hybrid Cloud Environment

• In a hybrid cloud environment, considerations include seamless integration between on-premises and cloud resources, consistent security policies, efficient traffic routing, and cost optimization.
• Load balancers must handle traffic distribution across different environments while maintaining performance, availability, and compliance with regulatory requirements.

Machine Learning

• Machine learning in predictive load balancing uses historical data and real-time metrics to predict traffic patterns and optimize resource allocation.
• Benefits include improved accuracy in traffic distribution, proactive scaling, reduced latency, and enhanced user experience by anticipating and mitigating potential bottlenecks.

Multi-Tenancy

• Load balancers support multi-tenant architectures by isolating traffic and resources for different tenants, using techniques like rate limiting, traffic shaping, and priority-based routing.
• This ensures fair resource allocation, prevents noisy neighbor issues, and maintains consistent performance for all tenants.

Client-Side vs. Server-Side Load Balancing

• Client-side load balancing involves distributing traffic at the client level, typically using DNS-based or client library approaches.
• Server-side load balancing occurs at the server or proxy level, managing traffic distribution centrally.
• Client-side load balancing is suitable for simple use cases with limited control, while server-side offers more advanced features and centralized management.

Real-Time Applications

• Load balancers handle real-time applications by prioritizing low-latency routing, using techniques like anycast, edge load balancing, and adaptive bitrate streaming.
• They ensure minimal latency, high availability, and consistent performance by distributing traffic efficiently and dynamically adjusting to network conditions.

Blue-Green Deployments

• Blue-green deployments involve running two identical production environments (blue and green) to minimize downtime during updates.
• Load balancers facilitate this strategy by routing traffic to the active environment (blue), while the other (green) is updated.
• Once validated, traffic is switched to the green environment.