13 Load Balancing Metrics to Monitor
Is your business looking to increase reliability and efficiency for infrastructure through load balancer monitoring in 2020?
Whether its internal employees or potential customers visiting your site to find text, downloads, application data, images, or video, all of these assets are sitting on several cloud servers waiting to be requested. In some cases, websites can have dozens to millions of simultaneous users, and all of their requests must return the correct data quickly and reliably, otherwise, user confidence degrades, traffic declines, and with it goes revenue.
Load balancers are pivotal in properly distributing the requests across the servers to take the burden off of a single server and ensure faster more reliable response times. Monitoring the effectiveness of load balancers can help you improve efficiency, provide a better user experience, and perhaps find places where costs can be curtailed.
Here are certain metrics you can monitor to help determine if your load balancer is working efficiently.
Load Balancer Metrics to Monitor
Accuracy determines how precisely the task result was executed. Even though the system makespan is slightly degraded by the accuracy value, this measurement matches the actual value of the task execution. Systems accurately fulfilling user demand is important to cloud services since it links directly back to the user experience.
2. Associated Costs
Associated costs depend on resource utilization. Liquid Web can help you scale using resource provisioning, but until requests start entering the system, you won’t truly know your needs, so this metric tells you whether your costs are justified. Resource provisioning helps control the on-demand resource cost and over-subscribed resource cost of over-provisioning and under-provisioning.
3. Associated Overhead
Associated overhead measures the execution of the algorithms to determine the excess or indirect computation time required to return the request. Some overhead costs are associated with the balancing technique for the load to the system. If the load is balanced properly, then minimum overhead occurs.
4. Energy Consumption
Energy consumption of a cloud system is the amount of energy absorbed by all devices connected in the system including personal devices (PC, laptop, phone, etc.), networking devices (hubs, switches, routers, etc.), and servers. Energy use can be reduced with energy-efficient hardware, energy-aware scheduling techniques, power-minimization in the server cluster, and power-minimization in wired and wireless networks. This metric can help reduce operating costs.
5. Fault Tolerance
Fault tolerance allows the system to perform uninterrupted even if one or more system elements are failing and resolves logical error related obstacles. Although it comes at an additional cost, the metric lets you measure the level of fault-tolerance from the number of failure points (i.e., single point failure or multipoint failure).
Makespan is an excellent metric to tell you the maximum time the system requires to deliver requests. This metric is measured end to end so Liquid Web tries to retain makespan even with priority tasks. The optimal makespan results in excellent system load balancing.
7. Migration Time
Migration time is the actual time required to migrate a request or task from one resource to another, like from one virtual machine (VM) to another VM. This metric informs the effectiveness of the system since the greater number of migrations of VMs increases time, resulting in the degradation of the makespan and load balancing efficiency.
Predictability is an excellent metric to goal-set and scale task allocation, task execution, and task completion to determine load balancing efficiency. Mapping previous behavior to the allocation and execution of current tasks in the cloud system provides the predictability value. Better predictability of task allocations improves load balancing and the makespan.
Reliability improves stability. It’s not just about uptime. As a metric, reliability can be measured in many ways, including uptime or consistent performance. In the case of any request or system failure, to improve the reliability of the system, the task is transferred to any other resources (VMs).
10. Response Time
Response time is the sum of transmission time, waiting time, and service time required by the system to respond to a request. Since the system performance is inversely proportional to the response time, optimizing response time results in a better makespan value.
Scalability is important for controlling operating costs since the system must be capable of performing and adapting to the needs of the business. It means adjusting the load-balanced system to the growth of the business. Rescaling of resources should be done periodically based on metric data specifically targeted toward this purpose.
Thrashing occurs when memory and other resources are unable to perform operations on requests because VM migration is not maintaining the proper scheduling. Monitoring this load balancer metric ensures that the appropriate load-balancing algorithm is being used for the maintenance of this metric.
Throughput is the number of user requests executed per unit time by a VM. When used as a metric, the throughput value determines the system performance with high throughput indicating good system performance. The throughput of the system is inversely proportional to the makespan of the system.
Make a Load Balancer Metric Game Plan
Having a long report full of metric data is great, but when there are no benchmarks, sample periods, criteria, or quantifiable applications with which to make decisions or system adjustments, the numbers are just that; numbers. Each metric must apply directly to your load balancing needs and should serve a specific purpose. Alarms can help.
Ask your Liquid Web expert if your service is alarm-capable. Alarms track a single metric over a specified time period. By setting an expected value of the metric relative to a threshold, the alarm can send one or more notifications. You are notified when a quantified metric reaches a defined range and remains in that range for a specified period of time.
For example, an alarm can alert you when the load balancer’s latency is above 60 seconds a consecutive period of 60 minutes. A broader or aspirational metric benchmark could alert you that you have either met, over-estimated, or under-estimated your load balancer threshold. This can help you scale.
Carefully Choose Load Balancer Metric to Monitor
Monitoring performance is not just important to the user experience but also important in controlling costs. Carefully pick the correct metrics for your businesses because every metric stated here may not apply to your load balancing needs. Choosing the wrong metric might “muddy” the data or delay decision-making.
Liquid Web experts can help you choose the most applicable metrics for your business. We want to help you scale effectively and provide you with the most economical solutions. Monitoring the performance of your load balancer is one way to do that.
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Jake Fellows is the Sophisticated Hosting Product Manager for Liquid Web's Managed Hosting products and services. He has over 10 years experience involving several fields of the technology industry, including hosting, healthcare, and IT-system architecture. On his time off, he can be found in front of some form of screen enjoying movies, video games, or researching into one of his many technical side projects.
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