A Practical Analysis of Load Balancing Algorithms for Web Developers

Discover the advantages and disadvantages of load-balancing algorithms and best practices for web development.

Photo by Stephen Leonardi on Unsplash

As the number of users accessing web applications grows, it becomes increasingly important to ensure that the application can handle the load. Load balancing is the process of distributing the incoming traffic across multiple servers to avoid overloading any one server. This can improve the performance and availability of the application. In this article, we will discuss load balancing, common load balancing algorithms, and their respective advantages and disadvantages.

What is Load Balancing?

As mentioned above, Load balancing is the process of distributing incoming network traffic across multiple servers to avoid overloading any one server. The goal of load balancing is to improve the performance and availability of an application. Load balancing can be achieved through hardware or software solutions. Hardware load balancers are dedicated devices that perform the task of distributing traffic, while software load balancers are typically implemented as part of the application itself.

Load balancing is important because it helps to ensure that an application can handle the load when there is a surge in traffic. Without load balancing, the server can become overwhelmed, which can lead to slow response times, downtime, and other performance issues.

Common Load Balancing Algorithms:

There are several load-balancing algorithms, and each algorithm has its own advantages and disadvantages.

Round Robin:

Round Robin is a simple algorithm that distributes requests evenly across all servers in the pool. With this algorithm, each server is used in turn, and the next request is sent to the next server in the pool. Round Robin is easy to implement and works well in situations where all servers are equal. However, it can be problematic if one server is much slower than the others or has a higher load.

Simple Code Example:

function roundRobin(servers, current) {
  const next = (current + 1) % servers.length;
  return servers[next];
}

Pros:

• Simple to implement
• Fair distribution when servers have equal capacity

Cons:

• Not suitable for servers with varying capabilities
• Round-robin doesn’t take into account the server’s current load or its ability to handle the request.

Least Connections:

Least Connections is an algorithm that directs traffic to the server with the fewest active connections. With this algorithm, incoming requests are sent to the server with the least amount of traffic. This algorithm works well in situations where some servers are faster than others or have a higher load. However, it requires tracking the number of active connections on each server, which can be difficult to implement.

Simple Code Example:

function leastConnections(servers) {
  let minConnections = Infinity;
  let chosenServer = null;
  for (const server of servers) {
    if (server.connections < minConnections) {
      minConnections = server.connections;
      chosenServer = server;
    }
  }
  return chosenServer;
}

Pros:

• It considers the server’s current load, ensuring that requests are sent to the least loaded server.
• It can handle long requests or large files, as it distributes requests based on the number of active connections rather than the request size.

Cons:

• It requires continuous monitoring of the servers to accurately determine the number of active connections.
• It may not work well for systems with short requests, as there may not be enough time for the algorithm to accurately determine the server’s current load.

Least Response Time

Shortest Response Time (SRT) is a load-balancing algorithm that directs traffic to the server with the shortest response time. With this algorithm, incoming requests are sent to the server that can handle them the fastest, resulting in faster response times and reduced latency for end-users. This algorithm is suitable for situations where servers have different processing capabilities, and some may be faster than others.

To implement the SRT algorithm, the load balancer must first measure the response time for each server. This can be done by sending a test request to each server and measuring the time it takes to receive a response. Once the response times have been determined, incoming requests are sent to the server with the shortest response time.

Here’s a simple code example:

function shortestResponseTime(servers) {
  let minResponseTime = Infinity;
  let chosenServer = null;
  for (const server of servers) {
    const responseTime = server.getResponseTime();
    if (responseTime < minResponseTime) {
      minResponseTime = responseTime;
      chosenServer = server;
    }
  }
  return chosenServer;
}

Pros:

• It directs traffic to the server with the shortest response time, resulting in faster response times for end-users.
• It is suitable for situations where servers have different processing capabilities.

Cons:

• It requires measuring the response time for each server, which can be resource-intensive.
• It may not work well for situations where server response times are highly variable, such as when servers have a high load or are geographically distributed.

IP Hash:

IP Hash is an algorithm that uses the client’s IP address to determine which server to send the request to. With this algorithm, each request is assigned to a server based on the client’s IP address. This algorithm works well in situations where the client’s IP address remains the same for each request. However, it can be problematic if the client’s IP address changes frequently.

Simple Code Example:

function ipHash(servers, ip) {
  const hash = hashIp(ip);
  const index = hash % servers.length;
  return servers[index];
}

Pros:

• It provides session persistence, ensuring that requests from the same client are always sent to the same server.
• It distributes requests evenly across all servers in the cluster.

Cons:

• It doesn’t take into account the server’s current load and may send a request to an overloaded server.
• It’s not suitable for systems with a large number of clients, as it may overload a single server with requests from multiple clients.

Random:

Random is an algorithm that randomly selects a server from the pool for each request. With this algorithm, incoming requests are distributed randomly across all servers in the pool. Random is easy to implement and works well in situations where all servers are equal. However, it can be problematic if one server is much slower than the others or has a higher load.

Simple Code Example:

function random(servers) {
  const index = Math.floor(Math.random() * servers.length);
  return servers[index];
}

Pros:

• It’s easy to implement and configure.
• Works well when servers have equal capacity.

Cons:

• It doesn’t consider the server’s current load, and may send a request to an overloaded server.
• It can be unreliable since the same server can receive multiple requests, leading to an uneven distribution of requests.

Consistent Hashing Algorithm

The basic idea of the Consistent Hashing Algorithm is to map each request to a hash key and represent each server as a hash value. When a request arrives, it is mapped to a hash key, then mapped to a hash value, and finally mapped to a server. In Consistent Hashing Algorithm, the hash values of servers are evenly distributed in a circular space, and hash keys are also mapped to a point in the circular space. When a server is added or removed, it only affects the mapping of hash keys between its previous server and the next server, thereby maintaining load balance.

Simple Code Example:

function hash(str) {
  let hash = 5381;
  for (let i = 0; i < str.length; i++) {
    hash = (hash * 33) ^ str.charCodeAt(i);
  }
  return hash >>> 0;
}

class Server {
  constructor(id) {
    this.id = id;
    this.hash = hash(id);
  }
}
class LoadBalancer {
  constructor() {
    this.servers = [];
  }
  addServer(server) {
    this.servers.push(server);
    this.servers.sort((a, b) => a.hash - b.hash);
  }
  removeServer(server) {
    this.servers = this.servers.filter((s) => s.id !== server.id);
  }
  getServer(key) {
    const hashValue = hash(key);
    const server =
      this.servers.find((s) => s.hash >= hashValue) || this.servers[0];
    return server;
  }
}
const balancer = new LoadBalancer();
balancer.addServer(new Server('server1'));
balancer.addServer(new Server('server2'));
balancer.addServer(new Server('server3'));
const key1 = 'foo';
const server1 = balancer.getServer(key1);
const key2 = 'bar';
const server2 = balancer.getServer(key2);
console.log(`Key "${key1}" is mapped to server "${server1.id}"`);
console.log(`Key "${key2}" is mapped to server "${server2.id}"`);
balancer.removeServer(server1);
const key3 = 'baz';
const server3 = balancer.getServer(key3);
console.log(`Key "${key3}" is mapped to server "${server3.id}"`);

Pros:

• Minimizes the impact of adding or removing servers
• Has good scalability and fault tolerance

Cons:

• It may cause requests to be unevenly distributed among servers, as some hash keys may be mapped to the same server.
• More complex to implement

Weighted Round-Robin Algorithm

The basic idea of the Weighted Round-Robin Algorithm is to assign a weight to each server based on its capacity, and then distribute incoming requests among the servers in a round-robin fashion. The weight of each server determines how many requests it receives relative to the other servers. For example, if server A has a weight of 2 and server B has a weight of 1, then server A will receive twice as many requests as server B. The algorithm cycles through the servers, giving each server a turn to handle a request, based on its weight.

Simple Code Example:

class Server {
  constructor(id, weight) {
    this.id = id;
    this.weight = weight;
    this.currentWeight = 0;
  }
}

class LoadBalancer {
  constructor() {
    this.servers = [];
    this.totalWeight = 0;
  }
  addServer(server) {
    this.servers.push(server);
    this.totalWeight += server.weight;
  }
  removeServer(server) {
    this.servers = this.servers.filter((s) => s.id !== server.id);
    this.totalWeight -= server.weight;
  }
  getServer() {
    let maxWeightServer = null;
    let maxWeight = 0;
    let totalWeight = 0;
    for (const server of this.servers) {
      server.currentWeight += server.weight;
      totalWeight += server.weight;
      if (server.currentWeight > maxWeight) {
        maxWeightServer = server;
        maxWeight = server.currentWeight;
      }
    }
    if (maxWeightServer) {
      maxWeightServer.currentWeight -= totalWeight;
      return maxWeightServer;
    }
    return null;
  }
}
const balancer = new LoadBalancer();
balancer.addServer(new Server('server1', 3));
balancer.addServer(new Server('server2', 2));
balancer.addServer(new Server('server3', 1));

for (let i = 0; i < 10; i++) {
  const server = balancer.getServer();
  console.log(`Request ${i} handled by server "${server.id}"`);
}

balancer.removeServer(balancer.servers[0]);
for (let i = 0; i < 10; i++) {
  const server = balancer.getServer();
  console.log(`Request ${i} handled by server "${server.id}"`);
}

Pros:

• It allows administrators to assign more resources to servers with higher weights, ensuring that they can handle a larger number of requests.
• It distributes requests evenly across all servers in the cluster.

Cons:

• It requires manual configuration of the weights, which may be time-consuming and error-prone.
• It may not work well for systems with a large number of servers, as the weights may need to be adjusted frequently to ensure an even distribution of requests.

Peak Exponentially Weighted Moving Average(PEWMA)

PEWMA is a statistical algorithm used to calculate a moving average of a time series dataset, with an emphasis on the most recent data points. It is often used in forecasting or trend analysis, as it can help identify patterns or trends in the data over time. PEWMA is similar to the traditional exponential moving average (EMA), but with the addition of a peak factor that emphasizes the most recent data points in the time series.

PEWMA is calculated by taking a weighted average of the historical data points, with the most recent data points given more weight than older data points. The weights used in the calculation are based on a smoothing factor and a peak factor, which control the level of smoothing and the degree of emphasis on the most recent data points. The smoothing factor determines how much weight is given to previous data points, while the peak factor controls the degree of emphasis given to the most recent data points.

Here’s a simple code example:

function peakExponentiallyWeightedMovingAverage(data, smoothingFactor, peakFactor) {
  let avg = data[0];
  for (let i = 1; i < data.length; i++) {
    avg = smoothingFactor * data[i] + (1 - smoothingFactor) * (avg + peakFactor * (data[i] - avg));
  }
  return avg;
}

Pros:

• It provides a smoothed moving average of a time series dataset, with an emphasis on the most recent data points.
• It can help identify trends or patterns in the data over time.

Cons:

• It requires a smoothing factor and a peak factor to be chosen, which can be subjective and may require some trial and error.
• It can be sensitive to outliers or sudden changes in the data, which may cause the algorithm to overemphasize recent data points.

Additional Tips and Best Practices

To further optimize your load-balancing strategy, consider the following tips and best practices:

Monitor and Adjust

Continuously monitor your web application’s performance and server resources to ensure your load-balancing strategy is effective. Regularly review your server logs, response times, and server utilization, and adjust your strategy as needed.

Health Checks

Implement health checks to monitor the status of your servers. These checks can help identify potential issues before they become critical, allowing you to proactively address any problems. In addition, a load balancer that supports health checks can automatically remove unhealthy servers from the pool, further improving overall performance and reliability.

Caching

Caching is an essential technique for reducing the load on your servers and improving your application’s performance. By caching frequently accessed data, you can minimize the number of requests to your backend servers, which in turn reduces the need for load balancing.

Use Content Delivery Networks (CDNs)

CDNs are another way to reduce the load on your servers and improve your application’s performance. By distributing your content across multiple geographically dispersed servers, you can deliver it to users more quickly and efficiently. In addition, CDNs can provide additional load-balancing capabilities, such as directing users to the nearest available server.

Auto-scaling

For applications with fluctuating or unpredictable workloads, consider using auto-scaling. This feature allows your infrastructure to automatically add or remove servers based on demand, ensuring that your load-balancing strategy can adapt to changing conditions.

Test and Benchmark

Regularly test your load-balancing strategy to ensure it meets your application’s performance and reliability requirements. Use benchmarking tools to compare different algorithms and configurations, and make adjustments as necessary.

Conclusion

Load balancing is a critical component of web application infrastructure, as it helps ensure high availability, reliability, and efficient resource usage. Several load-balancing algorithms can be employed, each with its advantages and disadvantages. Choosing the right algorithm depends on your specific application requirements and server capabilities.

Level Up Coding

Thanks for being a part of our community! Before you go:

• 👏 Clap for the story and follow the author 👉
• 📰 View more content in the Level Up Coding publication
• 💰 Free coding interview course ⇒ View Course
• 🔔 Follow us: Twitter | LinkedIn | Newsletter

🚀👉 Join the Level Up talent collective and find an amazing job