What techniques can you use to balance loads in distributed systems?

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Distributed systems are composed of multiple nodes that communicate and cooperate to achieve a common goal. However, as the system grows in size and complexity, it may face challenges such as uneven work distribution, network latency, resource contention, and failure recovery. To ensure high performance and availability, distributed systems need to balance the loads among the nodes and adapt to changing conditions. In this article, you will learn about some of the techniques that can help you achieve load balancing in distributed systems.

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1 Static load balancing

Static load balancing is a technique that assigns tasks to nodes based on a predefined or fixed criteria, such as node capacity, location, or priority. Static load balancing is simple and efficient, but it does not account for the dynamic nature of distributed systems, such as variations in workload, node failures, or network congestion. Static load balancing can lead to underutilization or overloading of some nodes, resulting in poor performance and reliability. Static load balancing is suitable for systems that have stable and predictable workloads and node configurations.

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2 Dynamic load balancing

Dynamic load balancing is a technique that assigns tasks to nodes based on the current state of the system, such as node availability, workload, or performance. Dynamic load balancing is more flexible and responsive, but it also requires more communication and coordination among the nodes, which can incur overhead and complexity. Dynamic load balancing can be centralized or decentralized, depending on whether there is a single node or multiple nodes that are responsible for making load balancing decisions. Dynamic load balancing is suitable for systems that have variable and unpredictable workloads and node configurations.

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Pavan Sai Akula

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In contrast to static load balancing, dynamic load balancing adjusts the distribution of tasks in real-time based on the current state of the nodes. This method continuously monitors the workload and performance of each node and redistributes tasks to optimize performance and efficiency. It's particularly effective in environments where the workload is unpredictable or highly variable.

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3 Hash-based load balancing

Hash-based load balancing is a technique that assigns tasks to nodes based on a hash function that maps each task to a node identifier. Hash-based load balancing is simple and consistent, but it does not account for the heterogeneity or availability of the nodes. Hash-based load balancing can lead to uneven load distribution or node failures, resulting in performance degradation and data loss. Hash-based load balancing is suitable for systems that have uniform and independent tasks and node identifiers.

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4 Round-robin load balancing

Round-robin load balancing is a technique that assigns tasks to nodes in a circular order, such that each node gets an equal chance of receiving a task. Round-robin load balancing is fair and easy to implement, but it does not account for the differences or changes in the node capacities or workloads. Round-robin load balancing can lead to underutilization or overloading of some nodes, resulting in performance inefficiency and latency. Round-robin load balancing is suitable for systems that have homogeneous and similar tasks and nodes.

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Shreeson Shrestha

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Further, Variations to Round robin providing equal chances can be Weighted Load Balancing which configures the server to allocate requests based on predefined weights assigned to each resource. To exemplify, if server A is configured with weight 3 and server B as no weight then for every 4 request first 3 is served by server A and later by Server B. Some of the use cases: 1. Service Allocation: Different services in a microservices may require different amounts of resources or loads. By allocating weight to service instance weighted load balancing makes sure of this. 2. Version Rollouts: To move traffic from older to newer versions for testing purposes, weights can be changed when new versions of services are rolled out.

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5 Random load balancing

Random load balancing is a technique that assigns tasks to nodes randomly, such that each node has a certain probability of receiving a task. Random load balancing is simple and scalable, but it does not account for the correlation or dependency of the tasks or nodes. Random load balancing can lead to unpredictable load distribution or node failures, resulting in performance variability and inconsistency. Random load balancing is suitable for systems that have diverse and independent tasks and nodes.

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6 Feedback-based load balancing

Feedback-based load balancing is a technique that assigns tasks to nodes based on the feedback from the nodes or the tasks, such as node utilization, task completion, or quality of service. Feedback-based load balancing is adaptive and intelligent, but it also requires more information and computation among the nodes or the tasks, which can incur overhead and complexity. Feedback-based load balancing can be proactive or reactive, depending on whether the load balancing decisions are made before or after the tasks are executed. Feedback-based load balancing is suitable for systems that have complex and interdependent tasks and nodes.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What techniques can you use to balance loads in distributed systems?

1

2

3

4

5

6

7

1 Static load balancing

2 Dynamic load balancing

3 Hash-based load balancing

4 Round-robin load balancing

5 Random load balancing

6 Feedback-based load balancing

7 Here’s what else to consider

Computer Engineering

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What techniques can you use to balance loads in distributed systems?

1

2

3

4

5

6

7

1 Static load balancing

2 Dynamic load balancing

3 Hash-based load balancing

4 Round-robin load balancing

5 Random load balancing

6 Feedback-based load balancing

7 Here’s what else to consider

Computer Engineering

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