Key Takeaways

  • Effectual migrated a federal government client's on-premises volcano-monitoring sensor system to a centralized AWS Cloud environment.
  • The solution supports predictive analytics that help scientists forecast seismic and volcanic activity across multiple geographic locations.
  • Amazon CloudWatch monitors for traffic surges during disasters, while Kubernetes provides automated container orchestration for high availability across regions.
  • EC2 instances with automated launch configurations let the client quickly scale application servers to meet demand during a disaster event.
  • The result is a resilient, fault-tolerant infrastructure built to keep mission-critical volcanic sensor data flowing during natural disasters.

Effectual delivered a mission-critical solution to a federal government client that ensured their sensor processing software was able to predict volcanic activity through moving magma.

Predictive analytics are used to help scientists forecast seismic activity over multiple geographical locations. This could not have been done without a Cloud-based solution to ensure a resilient system.

The Challenge

Our customer required a move from its on-premises infrastructure to a centralized Cloud environment in AWS. They looked to our team to handle high availability architecture and fault tolerance to meet workloads over many geographical locations quickly after a natural disaster.

The Solution

We provided a highly available and scalable infrastructure that ensured efficiency in wake of volcanos and other natural disasters. This sensor processing solution ensured predictive analytics, resilience, and scalability.

The Benefits

Predictive Analytics

We worked with the customer to create a solution that ensured the user could collect volcano data to analyze and utilize for machine learning to better predict when volcanoes erupt.

Resilience

Our team configured Amazon CloudWatch metrics to identify a surge in traffic in the event of a disaster. Kubernetes was implemented to provide automated container orchestration and higher availability to reach across multiple regions.

Scalability

We configured EC2 instances that ensure adequate capacity to meet traffic demands and compute capacity. Our team automated launch configurations to allow the client to quickly launch and/or scale application servers in target environments in the future.

Frequently Asked Questions

What federal use case does this case study cover?

A federal government client's sensor processing software used to predict volcanic activity through moving magma, migrated from on-premises infrastructure to AWS.

Why did the client need a cloud migration?

To move from on-premises infrastructure to a centralized AWS environment with high availability architecture and fault tolerance capable of supporting workloads across multiple geographic locations after a natural disaster.

How does the AWS solution support predictive analytics?

It lets the customer collect volcano sensor data and apply machine learning to better predict when volcanoes will erupt.

What AWS services ensure resilience during a disaster?

Amazon CloudWatch metrics identify traffic surges during a disaster, and Kubernetes provides automated container orchestration for higher availability across multiple regions.

How does the architecture scale during high-demand events?

EC2 instances were configured with automated launch configurations so the client can quickly launch or scale application servers as demand increases.