Challenges to Cloud Computing Resource Planning
Cloud computing has transformed how enterprises think about infrastructure. A decade ago, IT professionals had to make server projections months or years in advance, deciding on the types of servers they’d need. That would in turn be largely determined by the services required for their business needs, and expected loads those servers would bear.
Cloud computing has changed the game. Enterprises can focus on what resources and services are needed in the moment, and the need to forecast workloads is greatly reduced. However, cloud computing is no cure-all for IT challenges, and presents problems unique to the new IT landscape. For instance, underprovisioning and overprovisioning are still problem areas for IT professionals. While the former can reduce data availability and slow important workloads to a crawl, the latter can make costs skyrocket. Proper provisioning is often complex; and while automation tools help, they can be expensive.
Money spent in general on cloud infrastructure is a problem all its own. With so many options available, it can be difficult to find the optimal purchasing decision for the task at hand. Additionally, because of the high availability of servers and environments, it can be tempting to purchase small chunks of time, or micro-purchases. Giving into that temptation too often, though, can boomerang: these purchases add up quickly across an entire enterprise.
When it comes to compute instances, it can be hard to decide between a small number of large instances and a large number of small instances. Understanding the characteristics of your workload is the first step here; knowing the technical limitations of certain instance types, as well as business policies enacted by your organization, will also help whittle down the options.
Another challenge is optimally configuring databases. Like deciding on a compute instance, it all depends on the job: you must consider the load placed on your database, what kinds of databases will fit your needs best, and how to format and configure the data you’ll be storing there. Other potential issues include properly configured horizontal autoscaling; when and how to use services like RDS; and when to make long-term commitments to services and resources, like reserved instances.
One of the tools that brings the most flexibility in the new infrastructure landscape is the container. Containers are lightweight, deployable units of software that are self-sufficient, in that they do not require shared environments or a personal OS. As such, they’re more efficient than VMs and you can fit more of them onto a single physical server. While their use allows for efficient use of computing resources, they’re notoriously difficult to manage. Kubernetes was created for this exact reason, although it too can be hard to manage.
The flexibility of containers means properly deploying and allocating resources to them can be difficult. And although DevOps engineers are able to control exactly how much of a resource is given to the container, this provides ample opportunity for inefficient and wasteful resource allocation.
DevOps professionals need fine-grained insight into a workload’s performance and resource consumption in order to make educated decisions about resource allocation. Along with this visibility, finding ways to automate resource allocation, such as through infrastructure as code (IaC), can greatly help with this problem.
While there are many benefits to the new landscape of Infrastructure-as-a-Service (IaaS), plenty of new challenges come with it, too. Fortunately, help is available. Densify has developed the next generation of cloud resource management tools to alleviate the all-too-common problems outlined here. To learn more about how to optimize resource allocation in the cloud, download the Densify ebook on automated resource allocation.