Is Auto-Scaling the Solution for Pinpointing Underused Cloud Resources?
While auto-scaling helps to adjust cloud spend, IT administrators should be using all-purpose tools that work for their entire infrastructure.
Efficient cloud computing requires investment in the just the right number of servers for your company’s needs. Too few, and performance drops; too many, and valuable resources are wasted. Unfortunately, 60% of the cloud servers currently in use by enterprises are completely unnecessary, draining IT budgets and harming bottom lines. Considering how easy it is to invest far too much in cloud computing, IT leaders should make sure to foster scalability within their existing infrastructure.
Scaling cloud investment isn’t as easy as it’s often made out to be: while some optimistic observers compare controlling your cloud usage to shutting a water tap on and off, properly distributing cloud resources is a bit more difficult. Knowing when to scale down cloud spend requires insight into usage trends.
TechTarget recently spoke to these issues and recommended auto-scaling as a way to identify and minimize inefficient cloud usage. However, while auto-scaling offers some definite advantages, it remains a limited solution. For IT leaders looking to optimize their resource distribution across their entire IT infrastructure, it is more efficient to search for underused resources with a holistic IT cost optimization suite, rather than one dedicated simply to cloud resources.
Many public cloud providers, including Google Cloud Platform, Microsoft Azure, and Amazon Web Services, provide monitoring, scaling, and load balancing services that combine to largely automate cloud workload scaling. Together, these services take much of the pressure off administrators to ramp up and decrease cloud spend.
Even still, these services don’t run fully autonomously: administrators need to determine the policy that sets expiration dates for unused workloads, so that unnecessary applications don’t continue to run in the cloud. Additionally, administrators should use cloud tagging to identify and name resources, making it easier to remove them when they are no longer necessary.
Keeping an Eye on Auto-Scaling
Despite the push toward automation, IT administrators still need to monitor their infrastructure and spend to ensure that all components are running smoothly and that resources are allocated appropriately.
One way to do this is by relying on the cloud provider's monitoring service. Such services allow administrators to oversee key metrics and determine the health of their cloud computing setup. However, this solution is also an inherently limited one. Administrators are reliant on their cloud service provider to make performance data public, and some providers like AWS are less than completely transparent. Additionally, new cloud service providers may not not have adequate performance management systems in place. Put it all together, and this makes performance monitoring more difficult on the cloud.
Holistic Scaling and Monitoring Solutions
Luckily, there are other solutions to your scaling and monitoring needs. Capacity planning and performance monitoring software like TeamQuest Predictor can optimize resource allocation across your entire IT infrastructure, not just the cloud. That way, you can know when cloud servers should be increased or decreased and still gain valuable insight into your in-house server needs.
Additionally, an infrastructure monitoring service like Vityl Monitor can provide holistic health reports on your entire IT infrastructure. Monitor accumulates different data sources into one central location, so that cloud insights can be located on the same screen as in-house server metrics. Rather than relying on the cloud server provider’s platform, Vityl Monitor allows full customization of your window into infrastructure performance.
While auto-scaling does help to adjust cloud spend, IT administrators should push for tools that work with their entire infrastructure. When it comes to IT, your problems are complicated enough — that’s why your solutions should be one-size-fits-all.