Five negative factors in server utilization
Companies around the world are now using cloud services for demanding applications. However, as cloud storage grows in popularity, so do the workload demands on the servers being used. Too little server storage in the cloud can then quickly lead to slowed processes. There are five main factors that negatively affect server workload performance. This article explains how to avoid them.
Server memory is a component that either does its job adequately or not. Those who have sufficient memory for their workloads probably don’t even think about RAM anymore and focus their energies on other problem-solving. In contrast, insufficient RAM means that the company’s servers and productivity are running at a snail’s pace because the CPUs are powered by DRAM.
This is also why, in a survey of more than 350 IT decision-makers by Spiceworks, 47 percent mentioned that they plan to increase memory for servers in the coming year, even though half of their servers are already operating at maximum memory capacity. This is not surprising, as RAM helps compensate for the top five workload constraints.
Top five constraints
In the Spiceworks survey, IT professionals and data center managers were able to select up to three factors that they believe pose the biggest challenges in terms of their IT job performance and potentially negatively impact it.
1. Limited budget
When IT managers want to make the most of a dwindling budget, they often do so by comparing procurement costs to the total cost of ownership. When server efficiency is increased, the total cost of ownership is reduced because an increase in performance is achieved over the same period of time. Since memory powers the processor cores, it is the more effective solution in increasing the performance and productivity of a CPU because it impacts the workload without the need to purchase new servers.
In other words, more memory is a more effective system resource in transferring data to the CPU. The faster data arrives at the CPU, the less idle it is, consuming energy but providing little or no performance. Also, because memory is closer to the CPU, the time it takes to transfer data from DRAM to CPU is less than from memory to CPU. For example, data transfer from a hard disk to the CPU usually takes milliseconds, unlike SSDs where data reaches the CPU in microseconds.
This is definitely an elementary improvement, but at a higher latency than DRAM, where data reaches the CPU in nanoseconds. If you take into account the millions of instructions that reach the processor every day, a data transfer from the RAM to the CPU means a significant performance difference.
2. Unexpected demands
Virtualized workloads maintain consistent quality of service (QoS) and eliminate problems caused by unpredictable workload fluctuations. Overall, more RAM helps prevent service deviations by providing virtual applications with additional resources for active data and its usage (which are resident in memory). As spikes in unpredictable workloads quickly consume RAM, the system tries to find free resources and performance degradation occurs when the server has to switch to the slower storage drive. Increasing the amount of memory can solve this problem by giving applications more flexibility to work around rising and falling and potential peak workloads.
3. Limited space
Space is a problem that wants to be solved by design: what is the minimum number of servers needed to handle the workload? This way of thinking helps to optimize space requirements in the enterprise, as money is spent unnecessarily on each server that is not fully utilized. So, for example, if five servers are equipped with maximum memory to handle the workload of ten half-loaded servers, then power, cooling, and software licensing fees can be saved. When space is limited, there’s only one solution: scale vertically. Vertical scaling almost invariably increases a server’s available memory to optimize performance and serve as many virtual machines (VMs) as possible.
4. Rapid increase in the number of users
Hosting more users requires more memory to maintain the same quality of service. This is similar to the problem of unexpected demands. If the system has more RAM, it can gain greater flexibility and increase its ability to handle unpredictable workload demands created by a sudden increase in the number of users.
5. High power and cooling costs
Although fully populating a server with memory also causes an increase in overall power consumption, the total power consumed is often less than that of multiple servers not fully populated, at a comparable performance level. With more DRAM, servers can be more efficient with their power requirements from a workload perspective (powering and running the CPU). In addition, by reducing the physical servers, the overall cost of power and cooling will tend to decrease.
Working memory as fuel for the CPUs
A RAM is like fuel for CPUs as long as there is enough of it, they perform their tasks smoothly. However, there is a significant difference between having enough RAM and truly increasing the effectiveness of a workload. With just enough RAM, applications can run, of course, but with RAM capacity maxed out, fewer servers typically need to be deployed and are still more productive and at a lower total cost of ownership.
Conclusion
So not only can server memory upgrades improve CPU performance and efficiency, they can directly and unceremoniously address the five biggest workload constraints: limited budget, unexpected or unforeseen workload demands, limited space, rapid user growth, and power or cooling costs.
