Load Balancing

In this section

• Adaptive Load Balancing: Adjusting Load Intelligently
  Adaptive Load Balancing is a design pattern that dynamically adjusts load distribution among service instances based on real-time performance metrics. It aims to optimize resource usage and improve application performance in microservices architectures.
• API Gateway Load Balancing: Efficient Traffic Management in Microservices Architecture
  The API Gateway Load Balancing pattern efficiently manages client requests within a microservices architecture by distributing traffic using an API Gateway. This pattern enhances scalability, reliability, and performance by delegating request distribution and providing a unified interface for client interactions.
• Application Layer Balancing: Balances at Layer 7
  An exploration of the Application Layer Balancing design pattern used for effectively distributing network traffic at OSI Layer 7 within a microservices architecture.
• Client-Side Load Balancing: Clients Manage Distribution
  Exploration of the Client-Side Load Balancing pattern, where clients are responsible for distributing their requests to various service instances, enhancing reliability and scalability in microservice architecture.
• Cloud Provider Load Balancing: Efficient Traffic Distribution in Cloud Environments
  Cloud Provider Load Balancing involves using services provided by cloud platforms to distribute network traffic across multiple servers, ensuring optimal resource utilization, availability, and performance.
• Consistent Hashing: Ensuring Consistent Request Distribution in Load Balancing
  Consistent Hashing is a design pattern used in distributed systems to distribute network requests or data among a large number of servers or nodes in such a way that only a small fraction of the data needs to be redistributed when a node joins or leaves the system.
• Content-Based Load Balancing: Balancing By Content
  Content-Based Load Balancing is a load balancing pattern where incoming requests are distributed to server instances based on specific data content within the request itself, optimizing resource allocation and improving processing efficiency within microservice architectures.
• Distributed Load Balancing: Efficient Traffic Management in Microservices
  Distributed Load Balancing is a design pattern that helps in managing incoming requests across multiple server nodes to ensure no single node is overwhelmed, enhancing service availability and reliability.
• DNS Load Balancing: Using DNS for Distribution
  DNS Load Balancing utilizes DNS to distribute network load across multiple servers by using techniques such as round-robin, geographic location, and failover configurations, ensuring optimal resource utilization and service availability.
• Dynamic Load Balancing: Adjusts in Real-Time
  Dynamic Load Balancing is a design pattern that focuses on distributing workload dynamically across multiple resources or nodes to optimize resource utilization and system performance.
• Edge Load Balancing: Balances at the Network Edge
  Edge Load Balancing refers to a design pattern that distributes incoming network traffic across multiple servers or resources at the network edge, enhancing performance and availability in microservices architecture.
• Failover Load Balancing: Ensuring Service Continuity
  Failover Load Balancing involves redirecting requests to an alternate server or service in case of failure, ensuring service continuity in distributed systems.
• Geographic Load Balancing: Routing to the Nearest Location
  Geographic Load Balancing is a design pattern aimed at distributing network or application traffic efficiently across multiple geographic locations to reduce latency and improve responsiveness by routing users to the nearest data center.
• Health Check-Based: Routes to Healthy Instances
  The Health Check-Based pattern enhances system reliability by regularly monitoring the health of service instances and directing traffic only to those functioning optimally.
• Hybrid Load Balancing: Combining Strategies
  A comprehensive exploration of the Hybrid Load Balancing design pattern which combines various load balancing strategies to efficiently distribute traffic and improve performance in microservices architectures.
• IP Hash: Load Balancing with Client IP Hashing
  The IP Hash design pattern distributes incoming requests among a group of servers by performing a hash on the client's IP address. This ensures consistent routing of requests from the same client IP to the same server, thus providing session persistence.
• Least Connections: Routes Based on Active Connections
  Least Connections is a load balancing strategy that routes incoming client requests to the server with the fewest active connections, optimizing resource usage and improving response times.
• Least Response Time: Chooses Fastest Instance
  The Least Response Time pattern is a load balancing strategy that directs incoming requests to the instance with the shortest active response time, ensuring efficient traffic distribution across multiple instances in a microservices architecture.
• Load Balancer as a Service: Integrating Third-Party Load Balancers with Microservices
  An exploration of the Load Balancer as a Service design pattern within Microservices Architecture. This pattern utilizes third-party load balancing services to efficiently distribute network or application traffic across multiple servers, ensuring reliability, scalability, and performance.
• Load Balancing Simulation: Testing Scenarios
  In-depth exploration of the Load Balancing Simulation design pattern to test scenario distributions across multiple nodes or services within a microservices ecosystem effectively using functional programming techniques in Clojure.
• Priority-Based Load Balancing: Prioritizing Requests in Microservices
  A detailed exploration of the Priority-Based Load Balancing design pattern, which involves prioritizing requests to optimize resource utilization and enhance performance in microservices architecture.
• Random: Distributes Requests Randomly
  The Random design pattern for load balancing distributes incoming requests randomly across multiple server nodes. This pattern is simple to implement and useful in scenarios where all nodes are identical in terms of processing power and capacity.
• Request Splitting: Splitting Requests by Criteria
  The Request Splitting design pattern involves dividing incoming requests into smaller, more manageable pieces based on specific criteria. This approach enhances system performance, scalability, and reliability, enabling efficient distribution and processing of requests across a microservices architecture.
• Round Robin: Evenly Distributes Requests
  The Round Robin design pattern is a load balancing algorithm that evenly distributes requests across a set of resources, typically servers or processes. This pattern ensures that no single server is overwhelmed, improving efficiency and reliability in distributed systems.
• Server-Side Load Balancing: Centralized Management for Efficient Traffic Distribution
  An in-depth exploration of Server-Side Load Balancing, a fundamental design pattern in distributed systems that ensures efficient traffic distribution and centralized management across multiple service instances.
• Service Mesh Load Balancing: Efficient Load Distribution in a Microservices Architecture
  Exploration of Service Mesh Load Balancing as a modern approach to managing efficient load distribution and communication between services in a microservices architecture using a dedicated infrastructure layer.
• Service Weighting: Balances by Service Weights
  The Service Weighting design pattern is a load balancing strategy that distributes requests across multiple services or nodes based on predefined weights, optimizing resource utilization and improving the performance of distributed systems.
• Session Persistence: Ensuring Client Affinity in Distributed Systems
  Session Persistence is a design pattern used in distributed systems to maintain client affinity with the same server instance to ensure consistent and stateful interactions.
• Sticky Sessions: Maintaining Session State Across Requests
  Sticky Sessions, also known as session persistence, is a design pattern used in load-balanced applications to bind a user's session to a specific server for its duration. This pattern is essential for maintaining continuity of user experience and ensuring that session data remains consistent across user interactions.
• Traffic Shaping: Controlling Traffic Volume for Optimized Load Balancing
  Traffic Shaping is a design pattern used to control the volume of traffic sent to services or applications, allowing for optimized load balancing and resource utilization in microservices architectures.
• Transport Layer Balancing: Balancing at Layer 4
  A comprehensive guide to Transport Layer Balancing, focusing on how it operates at Layer 4 to efficiently distribute network traffic.
• Weighted Round Robin: Load Balancing Design Pattern
  The Weighted Round Robin pattern is a load balancing technique that distributes requests to a series of servers based on predefined weights, allowing for efficient handling of requests by prioritizing servers with higher capacities.