Saturation of server capacity at the most inopportune moment can cripple your web site performance, exasperate the users and put a significant chunk in to your revenue. The good news? There is no need to wait until your server begins to give error messages to realize that you are in the danger zone.
There are science and experience behind the prediction of server capacity limits that are, nevertheless, the mandatory part of any person who manages business-critical applications. Being the owner of a proliferating e-commerce site, or scaling a SaaS, knowing when and how your system will run out of capacity will make sure that you are many steps ahead of disaster.
Understanding What Server Capacity Actually Means
You must know what you are really measuring before you can make any prediction of capacity limits. The capacity of the servers is not a single number on a screen, but a combination of multiple important resources that interact.
Your dedicated server is making four main resources run at the same time. Processing power defines the speed of your server at which it can do its job and number of simultaneous requests. Memory is a factor that determines the amount of data that is in active use by your applications at a given time. Storage will regulate the amount of data that you will be able to store and the speed at which you can retrieve it. Network bandwidth determines the amount of data that could be in and out of your server within one second.
The trick is that these resources do not go dead on their own. The overload of one resource will lead to a bottleneck that will influence all the other resources. Your processor may be able to spare you, but once your RAM is full the rest of the system locks up as it begins paging to disk.
Establishing Your Baseline Performance Metrics
Without knowing about the present, you cannot determine the future. Setting up baseline measures will provide you with the point of reference that you require to observe in terms of the trends before they become emergencies.
Begin by monitoring your server in the normal operating conditions. Monitor the CPU usage of each of the cores, not only the average. Even with the multi-threaded applications appearing to be reasonable in terms of total CPU usage, one core being maxed can become a bottleneck. Monitor total memory usage and available memory, some operating systems are aggressive caches meaning they do not necessarily run out of RAM.
Operations per second Disk I/O operations per second tell you the degree to which your storage system is busy. This measure is important in an application which is database intensive and where slow disk response brings about a ripple effect of delay across all the layers of your application stack. Network throughput tracking must be able to record the inbound and outbound traffic patterns and assist you in determining the high usage periods and baseline traffic.
Record these measures in the varied situations. Prime business hours do not represent early morning numbers. The patterns of weekend traffic will barely be the same as weekday traffic. Promotional campaigns generate totally different usage profiles as compared to normal operations. The more situations you baseline the more you are able to predict.
Identifying Growth Patterns and Trends
Only raw metrics make you know where you are at the given moment. It is necessary to know where you are travelling to predict the capacity limits using past trends of growth.
The analysis of your resource use over time monthly views shows that there are different patterns compared to daily or weekly views. Perhaps you have a five percent growth of your CPU utilization every month as your number of users expands. It may be that you are adding 20GB of database every month as more transaction history is stored. When you extrapolate these trends, you know what to expect sometime in the future when you will run out of capacity.
Look out the seasonal changes which could distort simple linear projections. This is common with retail business that experiences huge traffic especially during holiday seasons. B2B applications could slow down predictably during vacation times and peak at the end of a quarter. Developing these patterns into your capacity forecasts will ensure that you do not over-prepared either year-round or find yourself caught with your pants down during season times.
There is not much in the way of a straight line of growth. Find inflection points at which there was a drastic change in the usage pattern. Have you doubled the number of database queries per request? Did this add a new customer segment that had a fundamental change in your traffic patterns? Knowing these step changes can allow you to foresee these kinds of impacts of future product changes.
Setting Realistic Capacity Thresholds
When to act is as important as getting to know what is in your way. The establishment of appropriate thresholds helps to avoid untimely scaling as well as hazardous delays.
A majority of the seasoned administrators begin to plan the capacity upgrade whenever any of the resources is always at 70-75% utilization level. This deadline provides you with time to strategize, test and implement changes before you are forced to work under the crisis mode. A wait of 90 percent utilization leaves almost no allowance to unforeseen traffic bursts or hiccups within the operation of the equipment.
Various resources need varying threshold techniques. CPU and network can safely overload during short bursts that your server will not implode should the CPU reach 95 percent during a burst of traffic a few minutes. The memory works the other way. When all the RAM is used, performance goes dead and quickly as the system goes into swap space. Avoid this cliff by setting more conservative memory thresholds, say of the order of 80%.
Special consideration should be given to disk space since its full usage can lead to crashing applications and distortion of data. The sooner you are at 75% full, do what you can to increase capacity before you get into the critical limits. Also, most file systems degrade as they become full, thus ensuring that performance does not vary as a result of a full file system is advisable.
Implementing Effective Monitoring Systems
Continuous data sampling is required in prediction. Automated checks will not suffice you require manual checks that monitors your server 24/7 and issue you with warnings on unusual trends.
The contemporary monitoring solutions have real-time visibility and historical trending. Solutions Prometheus and Grafana can provide you with customizable dashboards displaying the very metrics that are important to your particular infrastructure. The Nagios and Zabbix are both very comprehensive in monitoring and have strong alerting systems. Even more basic monitors such as New Relic or Datadog offer the most premium out-of-the-box monitoring of regular server configurations.
Set meaningful alerts as opposed to alert fatigue. When every two minutes there is a paging due to CPU usage that goes above 50 percent, you will learn to disregard the warning signal. Rather, issue warnings on a sustained total exceedance CPU greater than 80% over ten minutes, memory performance exceeding 85% or disk performance increasing more rapidly than normal according to historical data.
Retention is important in making proper predictions. Retain specific measurements at least six months or preferably a year. Longer terms are useful because they allow you to test the growth over the years and reveal minor long-term tendencies that would not be detected using shorter intervals.
Analyzing Resource Usage Patterns
It is of no use collecting the measures at all, unless you are going to analyze what they are telling you. The raw data is converted to actionable capacity information through regular analysis sessions.
Review monthly capacity reviews where you look at trends of all resources. Plot your CPU, memory, disk and network usage in the last month and compare it to the level in the past month(s). Get growth rates and estimate at what time every resource will reach your predefined limits. This is proactive and you are never caught off guard by capacity constraints.
Find correlation among various measures. Will more traffic necessarily result in more proportional CPU resource usage or does a certain type of traffic consume more resources than others? These relationships can help you estimate the effect of business changes on the resources. In case you are aware that API traffic consumes three times as much CPU time per request as page views do, you can correctly estimate how many infrastructure resources will be affected when you launch a new mobile app.
Detect abnormalities that could be the signs of issues other than mere capacity limits. The possible causes of sudden increases in disk I/O are inefficient database query that needs to be optimized and not an addition of hardware. The usage of memory increasing steadily without decreasing at all is an indication of a memory leak that should be addressed rather than the addition of more RAM.
Calculating Time-to-Capacity Estimates
When you know your growth patterns, you can estimate when you will run out of available capacity of a given resource.
The easiest method is the linear projection. Assuming that your CPU utilization is increasing at a rate of three percent per month, and at present, your CPU utilization is at 60 percent utilization, then about three to four months would be the period before you reach your 75 percent planning level. This simple mathematics will provide you with a rough time estimate of capacity planning.
More advanced analysis takes into consideration acceleration of growth rates. Perhaps you are expanding at three percent a month, but that is also growing as your business grows. The inclusion of this acceleration in the predictions gives more realistic predictions of long-term predictions, but the model will be less reliable to model without more past data.
You should always create more than calculated buffer time. Your mathematical projection projects that you will run out of capacity in three months, then you need to begin thinking of how to grow after two months. There is always a delay in the migration of servers, modification of configurations and capacity upgrades. It is better to be early than to be late and be scrambling to increase capacity as your server is struggling with the load.
Planning for Traffic Spikes and Peak Loads
Even average growth is half a story. The knowledge of peak loads and their planning will save you capacity catastrophes at your busiest times.
Examine past maximum utilization over the various periods of time. So what is your maximum simultaneous user load? Maximum requests per second? Highest database query rate? These are not average levels but rather the peaks which define whether your server is able to support such critical situations as product launches, breaking news or seasonal sales.
Divide peak usage by average usage to find out what your peak to average is. When your peaks are three times your average load that your load must be planned accordingly. A 50 percent average utilization appears to be conservative but as you find out, your peaks are often at 150 percent capacity, something impossible that occurs in crashes and timeouts.
Conduct tests on the buildings that mimic peak operations prior to occurrence in the production. Load testing tools will allow you to test your available infrastructure to its limits, simulating expected peak traffic, to find out how it performs. Identifying bottlenecks in testing is better than identifying them when thousands of actual users are attempting to buy things on your web site.
Accounting for Application-Specific Factors
The generic capacity planning is a baseline; however, your particular applications have peculiarities which influence the resource consumption.
Applications that are driven by the database can easily overload database capacity before the web server capacity. One query that is not optimized could use an incredible number of resources, which would act as a bottleneck without necessarily being reflected in any simple CPU or memory counters. Database monitoring tools also assist in finding out the slow queries, use of indexes and connection pool overload which can cripple capacity despite the hardware resources.
When there are caching layers, capacity equations are altered radically. Caching can allow a server to serve 10 times the capacity of an un-cached server; however, the problem of cache invalidation or cache miss can effectively cause resource demand to spike. Measure the cache hits as well as other metrics to verify that your caching policy is still doing as expected.
Architecture of application is crucial. The scaling of monolithic applications is different as compared to that of microservices. Stateless applications can be planned differently as compared to stateful applications. Knowing your particular architecture will allow you to know the components most likely to reach limits determines how to deal with them in the most effective way.
Making Informed Capacity Decisions
There is no use of data and predictions without taking action. The understanding of the need to scale up, scale out, or utilize the available resources wisely is what stands between efficient capacity management and firefighting.
The easiest solution to most cases of capacity constraints is vertical scaling which adds more resources to the existing servers. Capacity issues can be easily solved by upgrading the RAM or adding CPU cores or transitioning to faster storage. Vertical scaling however, has some hard limits. At some point, it is impossible to add additional resources to the same server, and at this point, a horizontal scaling will have to be considered.
Horizontal scaling loads different servers and has the capacity to potentially scale indefinitely. Load balancers spread traffic to server groups, database replication techniques replicate data to more than one instance, content delivery networks deliver the same data over the world on a global basis, and content delivery networks distribute the same data using more than one instance. This design assumes more complex architecture, but it is much larger than single-server design.
In some cases, optimization is better than scaling. Resource consumption can be dramatically minimized by refactoring of inefficient code, optimization of database queries, more effective caching, or by upgrading to more efficient software versions. These enhancements have the benefit of capacity growth with no extra cost of hardware, but it needs time and testing.
Putting Predictions into Practice
The end-test of capacity prediction is that you can be able to build infrastructure continuously without constraints affecting the users. It involves integrating business planning and technical monitoring.
Gear capacity planning to business roadmap. When marketing plans a huge campaign in the next quarter, do the computations on anticipated increases in traffic and pre-emptively provide capacity. In cases where product teams have scheduled feature launches, make an estimate of the impact on resources, and make sure the infrastructure has the capacity. This conformity ensures that capacity limitations do not act as blockers to business.
Note down your capacity planning process and predictions. You may make the prediction of additional capacity required in three months you will need and put it down with supporting evidence. Test yourself and find out whether reality was as you predicted. This is a feedback loop that will keep on improving your ability to forecast and the knowledge within an institution.
Conclusion
It becomes as important as regular backups or security updates to regularly review the capacity. Put them once a month or once a quarter with regard to rate of growth. By taking regular care of capacity, you are always ahead of the curve instead of always responding to emergencies.
Anticipating the capacity limits of the servers takes the infrastructure management to proactive rather than reactive. You do not need to struggle to fight fires when your servers run out of capacity, but rather you appeal to the servers’ weeks earlier than they will be in demand. Your users experience a steady performance, your staff does not have to deal with middle-of-the-night crises, and your company can even plan on how to grow because infrastructure will be there to back up it.
The monitoring, performance, and peace of mind are compounded with reliability, performance, and peace of mind. Baseline metrics can be used to follow the trend over the time, establish smart thresholds, and take decisive action when the prediction of impending capacity constraints appears. Get these basics straight and you will never be taken by surprise as a server collapse under the strain of excessive load.
Stay ahead of growth by accurately predicting server capacity limits, choose OffshoreDedi for infrastructure that scales with your business.