50 Essential Load Balancing Interview Questions

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1. Define load balancing in the context of modern web services.

Load balancing is about evenly distributing incoming network traffic across a group of backend servers or resources to optimize system performance, reliability, and uptime.

This distribution increases the throughput of the system by minimizing the response time and maximizing the resources' usage.

Key Objectives

• High Availability: Ensuring that the provided service is robust and uninterrupted, even in the face of server failures.
• Scalability: Accommodating varying levels of traffic without forfeiting performance or reliability.
• Reliability: Consistently providing high-performing and equitable access to resources or services.

Load Balancing Strategies

Round Robin

• Mechanism: Requests are allocated to a list of servers in sequential order.
• Pros: Simple to implement; Equal distribution under normal operating conditions.
• Cons: Does not take server load or other performance metrics into account; Effectiveness can vary.

Weighted Round Robin

• Mechanism: Servers still rotate in a sequence, but each has a specified weight, influencing how many requests it's assigned.
• Pros: Allows for rough load level management without more complex metric tracking.
• Cons: Inadequate granularity and adaptability for dynamic workloads.

Least Connections

• Mechanism: Channels traffic to the server with the fewest existing connections.
• Pros: Can lead to more balanced server loads in many cases.
• Cons: Not always effective with persistent connections or when requests vary significantly in resource requirements.

Least Response Time

• Mechanism: Routes new requests to the server with the most efficient and fastest response time.
• Pros: Optimizes for real-time system efficiency.
• Cons: Can become unreliable if server speeds fluctuate or if connections exhibit latency or instability.

Advanced Load Balancing Strategies

IP Hashing

• Mechanism: Uses a hash of the client's IP address to decide the server to which it will be sent.
• Pros: Useful for session-specific apps and databases; Can maintain consistency of stateful connections.

Content-Based Routing

• Mechanism: Analyzes specific attributes of the incoming request, such as the URL or HTTP header content, to strategically dispatch the traffic.
• Pros: Valuable for multifaceted architectures or when particular requests need to be managed differently than others. Can be combined with other methods for nuanced traffic control.

Health Monitoring and Adaptive Routing

• Mechanism: Actively monitors server health using various criteria and dynamically adjusts routing based on the assessments.
• Pros: Crucial for maintaining system reliability and performance, especially in highly dynamic and volatile environments.

Load Balancing Algorithms

• Adaptive Algorithms: Utilize real-time data to make traffic distribution decisions.
• Non-Adaptive Algorithms: Rely on predefined parameters to allocate traffic consistently.

Basics of Adaptive and Non-Adaptive Algorithms

• Adaptive: Assessments of traffic and server performance are periodic or continuous, using dynamic data to make informed routing choices.
• Non-Adaptive: Traffic and server performance are evaluated based on fixed parameters, making routing choices consistent over time.

Web Service Example

In the context of a modern web service, imagine a popular e-commerce website that uses load balancing. The site operates multiple backend servers, distributing the traffic using a round-robin approach. Each server is designated a specific weight, and the server with the least number of connections receives the next incoming request.

The service also employs adaptive routing algorithms. Regular health checks and performance assessments are conducted, and servers that display signs of deterioration, such as a sudden increase in response time, are temporarily removed from the pool of active servers to ensure the utmost reliability for incoming client requests.

2. What are the primary objectives of implementing load balancing?

Load balancing primarily aims to achieve optimal resource utilization, enhanced reliability, and improved performance across a network.

Key Objectives

• Optimizing Resources: Evenly distributing workload across servers to prevent individual servers from becoming overloaded, and resources from being underutilized.
• Enhancing Reliability: Minimizing service downtime and ensuring high availability by rerouting traffic to healthy servers in case of failures.
• Improving Performance: Reducing response times and ensuring smooth user experience by directing requests to servers that can handle them most efficiently.

How Does it Work?

• Load Distribution: Incoming traffic is efficiently distributed among multiple servers, ensuring no single server is overwhelmed.

• Health Monitoring: Systems continuously evaluate server health, removing or redirecting traffic from failing or slow servers to healthy ones.

Load Balancing Algorithms

1. Round Robin: Cyclically routes traffic to each server sequentially. While simple, it doesn't consider real-time server load.

2. Least Connections: Routes traffic to the server with the fewest active connections, ensuring a more balanced workload.

3. Weighted Round Robin: Servers are assigned "weights" representing their capacity. Higher-weighted servers receive more traffic, suitable for disparities in server capabilities.

4. IP Hash: The client's IP address is hashed to determine which server will handle the request. It's useful in maintaining session persistence.

Implementations

• Hardware Load Balancers: Specialized devices designed for traffic management. They ensure high performance but can be costly.

• Software Load Balancers: Software-based solutions offer flexibility and can be hosted in the cloud or on-premises. Examples include Nginx, HAProxy, and AWS Elastic Load Balancing.

• Content Delivery Networks (CDNs): They use globally distributed servers to cache and deliver web content, reducing load on origin servers and improving performance. Common CDN providers include Cloudflare, Akamai, and Fastly.

3. Explain the difference between hardware and software load balancers.

Both hardware and software load balancers are used to ensure efficient traffic distribution and to offer services like SSL termination or DDoS protection.

Let's look at the Benefits and Limitations of each.

Benefits of Hardware Load Balancer

• Optimized Processing: When specialized hardware is the central controller, the distribution of tasks is generally quicker, leading to better overall performance.
• Streamlined Setup: Many hardware solutions come preconfigured, requiring minimal or no additional setup, which is beneficial in quick deployment scenarios.
• Reliability: Hardware is engineered to be robust and resilient, making it a dependable choice.

Limitations of Hardware Load Balancer

• Cost: Typically, hardware-based solutions are costlier as they involve a separate physical device.
• Scalability: Hardware load balancers can be inflexible when it comes to scaling. Expanding beyond the initial hardware's capacity can be challenging and might even require complete replacements at times. This can lead to downtime.

Benefits of Software Load Balancer

• Cost Efficiency: Software-based solutions are often more cost-effective due to their dependency on existing infrastructure, like virtual machines or containers.
• Scalability: Most software load balancers are designed with scalability in mind. They can be easily replicated or scaled up or down, catering to varying loads and traffic patterns.
• Customizability: Software solutions are flexible and can be tailored to the specific needs of an application or deployment.

Limitations of Software Load Balancer

• Learning Curve: Setting up and configuring software load balancers might require technical expertise and more time compared to hardware alternatives.
• Resource Usage: On shared infrastructure, there can be competition for resources, leading to potential bottlenecks.
• Performance: In some cases, especially if the host is under heavy load or lacks the necessary resources, the performance might be inferior to hardware solutions.

4. Can you list some common load balancing algorithms and briefly describe how they work?

Load Balancing Algorithms aim to evenly distribute incoming network or application traffic across multiple backend servers. Let's look at the some of the most common methods used for this task.

Round Robin

Overview
This algorithm sequentially distributes incoming connections to the next available server. Once the last server is reached, the algorithm starts over from the first.

Advantages

• Easy to implement.
• Fairly balanced.

Limitations

• Not effective if the servers have varying capabilities or loads.
• Doesn't consider real-time server performance.

Least Connections

Overview
Servers with the least active connections are chosen. This approach is effective in balancing traffic if servers have different capacities.

Advantages

• More effective with servers of varying capabilities and loads.

Limitations

• Can lead to overloading if connections to fast servers are prolonged.
• Requires continuous monitoring of server loads.

IP Hash

Overview
Using a hash function applied to the client's IP address, a server is selected. This ensures that requests from the same client always go to the same server.

Advantages

• Useful for stateful applications like online gaming and shopping carts.
• Simplifies client-side session management.

Limitations

• Doesn't account for server loads.
• Can pose issues for fault tolerance and dynamic scaling.

Weighted Round Robin

Overview
This is an extension of the Round Robin. Here, a weight is assigned to each server based on its capabilities or resources. The algorithm distributes connections to servers based on their weights.

Advantages

• Allows biasing the distribution based on server capabilities.
• Adaptable to server loads by adjusting weights dynamically.

Limitations

• Administrators must handle weights carefully.
• Conservative approach in dynamically adjusting weights can be a concern in rapidly changing server conditions.

Weighted Least Connections

Overview
Weighted Least Connections adaptively considers both the weights assigned to servers and their current connection loads.

Advantages

• Combines the benefits of Least Connections and Weighted Round Robin.
• Adaptable to server loads and capabilities.

Limitations

• Management of weights becomes crucial.
• Requires continuous monitoring to adjust weights and ensure load is distributed optimally.

Source IP Affinity or IP Persistence

Overview
Requests from a particular source IP (like a user or a client) are persistently served by the same server. If that server is unavailable, another server is chosen.

Advantages

• Useful for stateful applications.
• Helps avoid data inconsistency or loss.

Limitations

• Can neglect server loads.
• Can cause server imbalances due to sticky sessions.

Least Response Time

Overview
This approach selects the server that has the shortest response time or latency to the client. It is an effective means for providing high-quality user experience.

Advantages

• Focuses on minimizing latency for the best UX.
• Adaptable to server performance.

Limitations

• Often requires periodic latency measurements.
• Might not always ensure fair load distribution.

Dynamic Load Balancers

While the traditional algorithms are static in nature, dynamic load balancers harness AI and machine learning to make real-time adjustments, taking into consideration factors like server health, historical performance, and demand patterns.

Advanced Routing Mechanisms

Overview
Modern load balancers employ sophisticated algorithms such as Performance-Based Routing, which direct traffic based on current server performance, offering low latency and high availability.

Code Example: Round Robin Load Balancing

Here is the Python code:

class RoundRobinBalancer:
    def __init__(self, servers):
        self.servers = servers
        self.index = 0

    def route(self, request):
        server = self.servers[self.index]
        self.index = (self.index + 1) % len(self.servers)
        return server

5. Describe the term "sticky session" in load balancing.

Sticky sessions, also known as session persistency, are a method used by load balancers to ensure that a user's subsequent requests are directed to the same server that handled their initial request.

This approach becomes necessary in cases when the user's session or client state is tied to a specific server. This commonly occurs in stateful applications or certain web frameworks where persistence mechanisms like local server storage or in-memory caching are used.

Benefits and Drawbacks

• Benefits: Simplifies session management and can enhance application performance by reducing the need for repeated session data lookups or storage updates.

• Drawbacks: Can lead to uneven server loads (if client sessions aren't perfectly balanced). It can also reduce the robustness of the system as a whole (because if a server goes down, all the clients with sticky sessions to that server will lose their session).

Implementations

Several load balancing tools and technologies, such as HAProxy, Nginx, and Amazon Elastic Load Balancer (ELB), offer sticky session support. This usually involves configuration and sometimes also specific code or architecture requirements in the backend applications.

Code Example: Using Sticky Sessions in Nginx

Here is the Nginx configuration block to use sticky session:

upstream backend {
    server backend1.example.com;
    server backend2.example.com;
    hash $request_uri consistent;
}

server {
    listen 80;
    location / {
        proxy_pass http://backend;
        proxy_http_version 1.1;
        proxy_set_header Host $host;
    }
}

The hash and consistent modifiers ensure that requests matching a particular hash value (commonly based on a client's attributes, like IP address or session ID) are consistently directed to the same server.

6. How does load balancing improve application reliability?

Load balancing can greatly enhance the reliability of applications. It achieves this through several key mechanisms, making it an integral part of high-availability infrastructures.

Key Benefits

• Increased Throughput: Load balancers can distribute incoming requests across a cluster of servers, ensuring efficient use of resources.

• Redundancy: In several real-world scenarios, multiple application servers exist to prevent a single point of failure. A load balancer directs traffic away from any failing instances.

• Resource Optimization: Load balancers enable dynamic allocation of resources, adjusting server workloads based on current traffic conditions.

• SSL Termination: Certain load balancers can offload SSL/TLS encryption and decryption work from the servers, improving their overall efficiency.

• Health Monitoring: Load balancers often perform health checks on servers in the pool, ensuring they are operational before sending traffic their way.

How Load Balancing Enhances Reliability

• SSL Termination: Instead of individual servers handling SSL/TLS connections, the load balancer does so, reducing this overhead on the server's end.

• Session Persistence: When necessary, load balancers can ensure that requests from the same client are sent to the same server. This is important for maintaining session state in stateful applications.

• Database Connection Pooling: Load balancers manage the pool of connections, providing a consistent connection experience for clients.

• Fault Tolerance With Clustering: Load balancers are often set up in clusters themselves for fault tolerance, ensuring uninterrupted service.

7. How do load balancers perform health checks on backend servers?

Health checks, also known as heartbeat monitoring, are vital for ensuring that backend servers in a load balancer pool are operational. Health checks can be either active or passive.

Active Health Checks

Active health checks involve the load balancer directly testing the server's health. Typically, it sends a small, defined request and expects a specific response to determine the server's current status. If the server meets the predefined criteria, it is considered healthy and stays in rotation. Otherwise, it is marked as unhealthy and taken out.

This process is useful in detecting a wide range of potential problems, from the server being offline to specific service misconfigurations.

Example: HTTP GET Request

The load balancer sends an HTTP GET request to a predefined endpoint, like /health. It expects a 200 OK response to consider the server healthy.

Passive Health Checks

Passive health checks rely on external signals to determine the server's state. These signals may come from several sources, such as the servers themselves reporting their status or other monitoring systems sending data to the load balancer.

Benefits of Passive Health Checks

Multiple signals indicating a problem can give a more accurate picture of the server's state, reducing false positives and negatives.

For example, a server may still be serving HTTP 200 OK, but warning logs could indicate an impending issue effectively.

Code Example: Implementing Health Checks in Node.js

Here is the Node.js code:

const http = require('http');

// Define a health-check endpoint
const healthCheckPath = '/_healthcheck';

// Set up a simple HTTP server
const server = http.createServer((req, res) => {
    if (req.url === healthCheckPath) {
        res.writeHead(200);
        res.end();
    } else {
        // Process normal traffic here
    }
});

// Start the server
server.on('listening', () => {
    console.log(`Server is listening on port ${server.address().port}`);
});

server.listen(3000);

// Gracefully handle server shut down events
process.on('SIGINT', () => {
    server.close(() => {
        console.log('Server gracefully shut down.');
    });
});

8. What are the advantages and disadvantages of round-robin load balancing?

Round-robin load balancing distributes traffic evenly across servers, and is especially effective when servers exhibit homogenous performance.

Advantages

• Simplicity: The round-robin algorithm is straightforward to implement and manage.

• Equal Distribution: It ensures that all servers receive an equivalent number of requests, promoting balance in the system.

Disadvantages

• No Benefit from Caching: The lack of session affinity, or sticky sessions, might hinder the advantages of caching, instead leading to cache misses.

• Performance Variability: Servers' differing performance or computational loads might not be optimally accommodated, potentially leading to latency or throughput issues.

9. In load balancing, what is the significance of the least connections method?

Least Connections is a key method for load balancing, ensuring that traffic is consistently directed to the server with the fewest active connections.

Importance of Load Balancing

• Efficient Resource Usage: By evenly distributing incoming traffic, the system strives to prevent any single application or server from being overburdened, leading to optimal resource employment.
• High Availability: In the event of malfunctions or traffic spikes, a load balancer can redirect requests away from problematic servers to those better equipped to handle the load.
• Improved Scalability: Load balancers are a pivotal tool in auto-scaling scenarios as they can seamlessly distribute incoming traffic among a larger number of instances.
• Enhanced Security: Certain load balancing strategies enhance security, such as SSL offloading, which allows the load balancer to manage encryption and decryption, alleviating this task from backend servers.

The Significance of "Least Connections" Method

• Fair Request Allocation: This method aims to manage server workload effectively by directing traffic to the least busy server, ensuring servers aren't overloaded.
• Optimum Throughput: As stated above, this method helps make sure connections are distributed evenly among available servers, thereby avoiding server bottlenecks and maintaining optimal performance.
• Consistent Response: Even though perfect balance might be challenging in practice, load balancer algorithms like "Least Connections" ensure attempts are made to keep response time and throughput consistent across servers.

Code Example: Least Connections Algorithm

Here is the Python code:

def least_connections_server(servers):
    return min(servers, key=lambda server: server.active_connections)

In the above example:

• servers is a list of available server objects.
• active_connections represents the current number of active connections on a server.
• The min function, with key set to a lambda function, returns the server object with the minimum active connections.

10. Explain how a load balancer might handle failure in one of the servers it manages.

Load Balancers manage multiple servers. When one fails, the Load Balancer no longer directs traffic to it, ensuring seamless functionality. Here is a list of mechanisms it uses.

Server Health Checks

The load balancer continuously monitors server health, often through periodic checks. If a server misses several health checks, it's presumed unavailable and taken out of rotation.

Example: Nginx Configuration

Here’s a Nginx configuration using the upstream module for load balancing and server monitoring:

http {
    upstream myapp1 {
        server backend1.example.com weight=5;
        server backend2.example.com;
        server backend3.example.com;

        # Health check
        check interval=3000 rise=2 fall=3 timeout=1000;

        # Backup server
        backup;
    }

    ...
}

Load Balancing Algorithms

Load balancers deploy various algorithms to route traffic to available servers:

• Round Robin: Cyclically directs traffic to servers in a queue.
• Least Connections: Sends traffic to the server with the fewest active connections, promoting efficiency.
• Weighted Round Robin: Accounts for server capacity by assigning weights. Servers with higher weights handle more traffic.

SSL Termination

For secure connections using HTTPS, the load balancer might employ SSL Termination, decrypting incoming requests and re-encrypting responses. This can create performance discrepancies.

Sticky Sessions

When a user establishes a session, the load balancer routes subsequent requests from that user to the same server. This ensures session state is maintained.

Redundant Load Balancers

To avert potential load balancer failures, a backup or secondary load balancer might be deployed, ensuring no single point of failure.

Elastic Scalability

Modern load balancers, especially in cloud environments, support elastic scalability. This means they can quickly adapt, managing more servers in response to increased traffic.

Ensuring Data Consistency

With multiple servers catering to database operations, maintaining data consistency is crucial. Load balancers may use techniques such as server affinity or database locking to ensure this.

Service Health Metrics

Additionally, a load balancer might record various metrics of server health, such as response times and error rates.

Deploying Backup Servers

Some load balancers are designed to have a pool of backup servers, which only become active when the primary servers fail. This setup can be particularly useful for managing unexpected spikes in traffic or cloud infrastructure issues.

11. How does a load balancer distribute traffic in a stateless vs stateful scenario?

Load balancers handle traffic distribution differently in stateless and stateful scenarios, often using different algorithms for each.

Stateless Behavior

In a "stateless" setup, each user request is independent.

Traffic Distribution

• Algorithm: Round Robin or IP Hash
• Mechanism: The load balancer selects the next available server, bearing in mind server weights if applicable.

Stateful Behavior

In a "stateful" setup, there is a persistent connection between a user and a server due to ongoing session data.

Traffic Distribution

• Algorithm: Least Connection or Session Stickiness
• Mechanism: To maintain session continuity, the load balancer consistently directs a user to the server where the session was initially established. Common methods for achieving this include source-IP affinity and HTTP cookie-based persistence.

12. What is the concept of session persistence, and why is it important?

Session Persistence, often called Session Stickiness, is a technique used in load balancing to ensure that all requests from a single client are directed to the same server.

Importance of Session Persistence

• User Experience: Many applications, like e-commerce platforms or social media sites, tailor user experiences based on session-state information. For instance, a shopping cart typically requires persistence.

• Database Consistency: To maintain integrity, certain operations must be carried out consistently on a single server, especially in multi-tier architecture and stateful applications.

• Security: Ensuring client-server proximity can minimize potential security risks, like cross-site request forgery (CSRF).

How Load Balancer Manages Flexibility and Statelessness

Modern web architectures, favoring statelessness and flexibility, resolve these issues primarily through intelligent design and session storage.

Techniques

• Round-Robin: This technique rotates request distribution among servers in a circular manner. It's simple but doesn't guarantee that all client requests will go to the same server.

• Sticky Sessions: Also known as Session Affinity, this method uses mechanisms, such as HTTP cookies or dynamic rule-designators, to direct a client to the same server for the duration of its session.

• Load Balancer Persistence Modes: Some advanced load balancers offer specific algorithms to maintain session stickiness. Common modes include:

  • Source IP Affinity
  • Cookie-Based Persistence
  • SSL Persistence

• Shared Session State Across Servers: By centralizing session data in a shared database or cache, all servers under the load balancer can access this data, ensuring session uniformity.

When to Avoid Session Persistence

While it can help with efficiency in certain scenarios, it's essential to recognize when session persistence might not be the best choice:

• Server Overload: If a particular server is overwhelmed with session-bound traffic, session persistence can exacerbate the problem.

• Scalability: As your traffic grows, session persistence can lead to scalability challenges since it restricts client-server flexibility.

• Operational Challenges: Tools like content caching or load balancer alterations can become less effective or difficult to manage with session stickiness in place.

13. Discuss the role of DNS in load balancing.

While not a direct load balancer, Domain Name System (DNS) skillfully complements load balancing to distribute traffic among multiple servers.

DNS Mechanisms for Load Balancing

1. Round Robin (RR): DNS servers traditionally leverage RR to cycle through multiple IP addresses that correspond to various servers in a load balancing pool. While it's simple and easy to administer, RR can't dynamically adjust traffic based on server loads.

2. Weighted Round Robin (WRR): Builds on RR by assigning each IP address a weight based on server capabilities or capacities, thereby directing more or less traffic to each server.

3. Least Connections: More advanced load balancing mechanisms, like Weighted Least Connections, incorporate intelligence about the number of active connections on servers to route traffic effectively.

4. Geographical Load Balancing: DNS can also be optimized to manage traffic based on global geography, directing users to the closest server to minimize latency.

Code Example: Simple Round Robin DNS

Here is the Python code:

from itertools import cycle

# Replace with actual server IPs
server_ips = ["192.168.1.1", "192.168.1.2", "192.168.1.3"]

# Use cycle for round-robin behavior
server_cycle = cycle(server_ips)

# Simulate DNS query
def get_next_server():
    return next(server_cycle)

# Test with multiple queries
for _ in range(6):
    print(get_next_server())

14. In what scenarios would you use weighted load balancing?

Weighted Load Balancing adjusts server traffic in a manner that doesn't merely distribute load evenly across all servers. Instead, weighted balancing allows for load assignment depending on server capacities and resource allocation levels.

Common Use-Cases

1. Performance Optimization: In instances with heterogeneous servers, you might benefit from routing more traffic to more capable servers.

2. Cost-Effective Scaling: In cloud or virtual environments where servers are billed based on resource usage, weighted balancing can be used to minimize costs.

3. Task Segregation: For unique server tasks, you can distribute load based on task requirements, leveraging weighted balancing.

4. Disaster Recovery Preparedness: In setups with dedicated backup systems, such as active-passive configurations, keeping a portion of the server capacity unused is crucial for swift transitions in case of calamities.

Code Example: Round-Robin with Weights

Here is the Python code:

from itertools import cycle

class WeightedRoundRobinBalancer:
    def __init__(self, servers):
        self.servers = servers
        self.weights = {server: servers[server]['weight'] for server in servers}
        self.cycle = cycle(self._expand_servers())

    def _expand_servers(self):
        expanded = [[key] * self.weights[key] for key in self.weights]
        flattened = [item for sublist in expanded for item in sublist]
        return flattened

    def next_server(self):
        return next(self.cycle)

Code Example: Testing the Round-Robin Weighted Balancer

Here is the Python code:

servers = {
    'Server1': {'ip': '192.168.1.1', 'weight': 5},
    'Server2': {'ip': '192.168.1.2', 'weight': 3},
    'Server3': {'ip': '192.168.1.3', 'weight': 2}
}

# Create and initialize WeightedRoundRobinBalancer
wrr = WeightedRoundRobinBalancer(servers)

# Test the Balancer by firing 10 requests
results = {server: 0 for server in servers}
for _ in range(10):
    server = wrr.next_server()
    results[server] += 1

# Print the results
for server, count in results.items():
    print(f'{server} received {count} requests. (Weight: {servers[server]["weight"]}, IP: {servers[server]["ip"]})')

15. How can load balancers help mitigate DDoS attacks?

Load balancers are essential in distributing traffic across servers to optimize performance. They also play a crucial role in mitigating DDoS attacks by detecting and filtering malicious traffic.

How Load Balancers Mitigate DDoS Attacks

1. Traffic Distribution: Load balancers ensure equitable traffic distribution across multiple servers, which prevents the overloading of a single server and distribution of attack traffic.

2. Layer 4 SYN Flood Protection: Modern load balancers can mitigate SYN flood attacks, which flood servers with TCP connection requests, by employing intelligent connection tracking and state management.

3. Layer 7 Application DDoS Protection: Advanced load balancers can detect application layer (Layer 7) DDoS attacks by monitoring HTTP and HTTPS requests. They can also identify and filter out malicious traffic patterns targeting specific URLs or application endpoints.

4. Behavior-based Detection: Some load balancers leverage real-time traffic analysis that can identify abnormal behavior, such as excessive requests from a single source, and dynamically adjust traffic flow accordingly.

Code Example: SYN Flood Protection with iptables

Here is the iptables configuration

sudo iptables -A INPUT -p tcp --syn -m limit --limit 1/s --limit-burst 3 -j ACCEPT

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