Monitoring Performance, Maximizing Efficiency
Making the best use of AWS cloud resources requires IT professionals to monitor their performance with rigor and precision.
Amazon Web Services (AWS) works wonderfully for a number of business scenarios: for startups that need to scale or have uncertain demand, services that need to rapidly scale, in-house data centers that are cost-prohibitive or unwieldy to manage, and more. But with these advantages, AWS also comes with practical limitations that IT professionals need to keep in mind. Oftentimes, changing your cloud configuration, opting for a flexible hybrid environment, or even moving an application in-house is more cost-effective than running up your cloud bill.
That’s because in some ways, AWS is like the Wild West – there are only a few rules, and you can get lost spinning up new services when you should really scale back. Only by using performance monitoring tools can organizations hope to find the delicate balance where maximal efficiency in the cloud is achieved. In other words, when you treat AWS like the animal that it is – a data center with an instantaneous on/off switch – you can learn to leverage its services to your benefit.
On of the most significant challenges with AWS is that it’s incredibly easy to spin up services on any whim – companies are often surprised by the large bill that tend to come at the end of the month as a result of these practices.
A TechTarget guide argues that the trouble is that many organizations treat AWS like an on-site data center, where it’s okay to leave machines offline when unneeded. They fail to realize that cloud instances continue to incur costs until they’re deleted, whereas physical hardware will merely sit there innocently. This also makes it easy for IT departments to lose track of their resources, such as EC2 elastic containers and reserve instances, making it unclear which services are value-creating and which are simply draining budget.
To guard against these risks, Techtarget recommends that cloud buyers employ tools to track three performance-indicating areas: logs, analytics, and app availability.
Logs are useful for finding problems on both an application and system level, especially when tools are programmed to send alerts flagging certain issues (enterprise environments are usually too complex to trawl manually). Analytics give you a measure of any number of key performance indicators (KPIs), and should enable you to standardize the performance of different IT platforms with a single dashboard – ideally, for your entire enterprise infrastructure. Finally, organizations should be able to measure their app performance, both in terms of current response speeds or availability, but also for testing the impact of upgrades and ‘what-if’ scenarios.
In aggregate, these measures should give organizations a much clearer idea of whether their current cloud spend or configuration is warranted. For example, tests might reveal that your apps could run just as effectively on a less costly server instance (or simply that you’re paying for superfluous services). Alternately, an app might reveal itself to be best suited for a cloud burst-type model, where you ramp up elastic resources only for brief, high-demand periods, otherwise leaving the app to run on local equipment.
The danger in the cloud, even with this kind of testing, is that organizations become so focused on many piecemeal efforts that they neglect the big picture. The goal, then, is to gain top-down control and perspective.
Tools such as TeamQuest’s Vityl suite of products can aggregate a wide variety of performance monitoring techniques into a single location. With such a view, organizations can use the cloud when the situation calls, leveraging its real advantages while avoiding the common problem of overspending. There’s no miracle button for maximizing performance and efficiency, but it’s target that companies should be making an effort to hitting as accurately as possible.