What is Scalability in Cloud Computing

What is Scalability in Cloud Computing

One of the primary reasons enterprises move their business-critical processes to the cloud is to save money. Around 44% of IT professionals say they migrated to the cloud because of the general cost savings cloud vendors promised them. However, many are still going over budget. Why?

While cloud computing certainly helps businesses perform very heavy computing tasks, many enterprises are running their cloud infrastructures using cloud vendors’ default configurations. This means many of the processes and workloads they run in the cloud are unoptimized.

When enterprises fail to optimize their workloads, their infrastructure tends to either consume more compute resources than needed to prevent lag in performance or they overprovision compute resources and create waste.

Simply put, in the world of cloud computing, compute resources = money. When organizations overprovision or underutilize compute resources, they’re wasting money and are at risk of performance issues that can ruin the user experience.

In one of our recent studies, we discovered that businesses are spending as much as 40% beyond their initial cloud budgets. One in 12 companies goes over budget even more than that.

Nearly 40% of businesses cite cost management, cost containment, and increased complexity as their biggest cloud computing challenges, according to our 2021 Big Data Cloud Technology survey.

cloud computing costs

Looking to further reduce your cloud computing costs? Pepperdata has good news for you: Pepperdata Capacity Optimizer includes enhanced managed autoscaling. This feature can help your organization reduce up to 50% of your Amazon EMR, Google Dataproc, and Qubole costs.

Autoscaling with Capacity Optimizer

While existing forms of autoscaling provide the elasticity customers need for their big data workloads, they can still lead to over-provisioning. This equates to extremely high cloud computing costs.

Take a look at how autoscaling typically happens in the image below, which includes two charts. The first chart shows that autoscaling grew the cluster to 100 nodes for the entire runtime duration.

However, the second chart shows what the cluster actually ran during the runtime duration. Sometimes it was just one task, other times no tasks were running. The 100 nodes autoscaling provided for the entire duration was, in fact, overkill.

managed autoscaling screenshot to lower cloud computing cost

Capacity Optimizer v6.3 fixes this issue. By intelligently augmenting autoscaling, it ensures all nodes are fully utilized before additional nodes are created. This eliminates waste and reduces your Amazon EMR, Google Dataproc, Qubole, and other cloud computing costs.

While making thousands of decisions per second, Capacity Optimizer analyzes the resource usage of each node in real time. This allows the solution to optimize the utilization of CPU, memory, and I/O resources on big data clusters.

Automated deployment options like these from Pepperdata can seamlessly be added to your EMR, Dataproc, and Qubole deployments. Moreover, aside from automatically tuning cloud deployments for optimal performance and reduced cloud computing cost, Pepperdata:

  • Leverages targeted performance insights, reducing troubleshooting time by 90%
  • Tunes app resources for peak efficiency through prescriptive recommendations
  • Automatically detects and alerts users on SLA-impacting bottlenecks

Pepperdata CEO Ash Munshi has this to say about managed autoscaling:

Even with the best cloud migration strategy and dedicated attempts to curb costs, the cloud makes managing resources more difficult. But, by leveraging machine learning and managing infrastructure in real time, IT operations teams automatically recapture wasted capacity and significantly reduce their costs.

Making Autoscaling Better

Rising cloud computing costs, along with other cloud migration challenges, have forced as much as 85% of companies to repatriate their workloads from the cloud. On top of that, 72% of enterprises have paused their cloud migration and decided to partially or completely scale back to their prior computing configurations.

Capacity Optimizer with managed autoscaling has helped customers lower their cloud costs across various industries. Join them. Register for a free trial of our enhanced managed autoscaling feature today.