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Why Network Load Balancer Monitoring is Critical

Your networks are the highways that enable data transfers and cloud-based collaboration. Like highways connect people to physical locations, networks connect people to applications and databases. As you would look up the fastest route between two physical locations, your workforce members need the fastest connectivity between two digital locations. Network load balancers enable you to prevent and identify digital “traffic jams” by redistributing incoming network requests across your servers.

 

Monitoring network load balancers is critical to ensure continued business productivity and continuity.

What is a network load balancer?

Network load balancers (NLB) distribute incoming network traffic across multiple servers to prevent any single backend resource from becoming overwhelmed. To intelligently route inbound traffic to the appropriate server or resource, IT teams set criteria based on several factors, including:

  • Server health
  • Load distribution
  • Traffic patterns

 

For networks with volatile traffic patterns or high traffic volumes, NLBs enable IT teams to optimize performance and reduce service outages, especially when they include features like:

  • Fault tolerance
  • Health checks
  • Support for static and elastic IP addresses

 

A network load balancer’s main components are:

  • Listeners: checking client connection requests on specific ports, like TCP or UDP, then forwarding to target groups
  • Target groups: backend servers handling incoming traffic, like EC2 instances, IP addresses, or Lambda functions

 

Since load balancers distribute workloads across multiple targets, they allow millions of concurrent requests, enabling high availability and scalability.

 

What are the different types of load balancer configurations?

As with everything else in technology, no single method for configuring a load balancer exists. However, depending on your organization’s needs, you should understand the benefits and drawbacks of the different options.

Round-robin

As a simple and effective method, round-robin is often the default method load balancers use to distribute incoming client connections to backend servers. This method gives each server a “turn” using a sequential order so that no single server becomes overwhelmed with fault tolerance. Easily implemented, the round-robin method only requires a basic understanding of server load and responsiveness.

 

Since this method only focuses on which server’s “turn” is next, it may not consider actual workload or performance, leading to an uneven distribution.

Weighted round-robin

The weighted round-robin compensates for the round-robin’s potential to distribute traffic unevenly. This method assigns each server a weight the IT team can customize according to an application’s specific requirements. Servers with higher weights can handle more traffic, so they receive more incoming connections. With a weighted round-robin, administrators can optimize resource allocation and use servers efficiently.

 

However, weighted round-robin faces challenges handling:

  • Incoming requests with extensive service time
  • Requests with different service times

Least connections

Working primarily the way it sounds like it works, this method directs incoming requests by forwarding them to the server with the fewest current connections to prevent any single one from becoming overwhelmed or leaving others underutilized. Optimizing resource allocation across backend servers enables:

  • Efficient resource utilization
  • Application performance and availability
  • Network reliability and responsiveness

 

However, the least connections method has some drawbacks, including the following:

  • Often difficult to troubleshoot
  • Requires more processing
  • Fails to consider server capacity when assigning requests

IP Hash

This method maps incoming connections to specific backend servers using a unique identifier, like source or destination IP. By routing all requests from a particular client to the same server, this method is suitable for:

  • Maintaining session persistence
  • Applications requiring an affinity to a specific server

 

However, the IP hash method has the following drawbacks:

  • High resource consumption
  • Lacks awareness of the actual load
  • Requires making changes on the physical network
  • Difficult to troubleshoot

 

The IT and Security Benefits of Network Load Balancers

Since network load balancers provide visibility into your network and application health, they offer several benefits enabling IT operations and security teams.

 

Optimize resource allocation

Monitoring NLB metrics lets you gain insights into network load and identify potential bottlenecks. When you compare the following application load balancer (ALB) metrics with your NLB, you can determine whether your infrastructure effectively distributes traffic:

  • Active connections
  • Incoming network traffic
  • TCP connections

Once you understand normal incoming traffic patterns, you can more easily identify spikes or unusual traffic patterns.

Scale infrastructure as necessary

Since load balancing spreads traffic across multiple servers, you can scale your server infrastructure on demand, preventing downtime. Scaling ensures fault tolerance by adding more backend servers to increase capacity or additional NLBs to handle the load.

Monitor reset packets

By correlating NLB and system data, you gain visibility into your load balancer’s performance. Since reset packets provide visibility into whether the NLB is terminating TCP connections prematurely, monitoring for abnormal increases can alert you to issues so you can respond to them quickly and engage in proactive maintenance.

Insights into host health

NLBs send periodic requests to check host status. You gain insights into your infrastructure’s health by monitoring these requests and corresponding responses. For example, you may want to use visualizations that enable you to:

  • Display healthy and unhealthy hosts
  • Gain visibility into trends over time
  • Detect anomalies indicating potential issues
  • Identify potential bottlenecks

Detect potential security incidents

A Distributed Denial of Service (DDoS) attack occurs when malicious actors send high volumes of network requests to overwhelm network and cause downtime. Network load balancers give you a way to mitigate risks by rerouting traffic to distribute the request across multiple servers, ultimately removing any host from being a single point of failure that leads to a service outage.

Graylog: Security Analytics for High-Fidelity Alerts that Enable IT Operations and Security Teams

Graylog Operations and Graylog Security give you the comprehensive network load balancer monitoring necessary to maintain service and mitigate security risks. With comprehensive network monitoring, your teams can coordinate their activities more efficiently. You can create high-fidelity alerts streamlining your network performance investigations by aggregating and correlating log data across your entire environment. By incorporating our security analytics, you can create high-fidelity alerts that ensure the right team has the necessary visibility to investigate issues quickly.

 

Combining our powerful, lightning-fast investigation features with the increased visibility across your IT environment means you can search volumes of log data in seconds, improving key operations and security metrics like Mean Time to Investigate (MTTI) and Mean Time to Remediate (MTTR). Further, with our cloud-native capabilities and out-of-the-box content, you can reduce total cost of ownership (TCO) while increasing productivity, ultimately gaining immediate value from your logs.

 

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