AWS Aces 'Stern'

Test With Flying Colors

L3Harris Geospatial Application Gets Optimized After Migration From Azure 

 Migration  Modernization  Microsoft Environment


For more than four decades, L3Harris Geospatial has been a leader in innovation and developing scientifically-proven solutions using cutting-edge technologies through its proprietary software and technology. L3Harris Geospatial's products help customers explore space and earth, and see the human body in new ways. 

L3Harris Geospatial first met Cloud303 through a Well-Architected Review - a full analysis and diagnosis was conducted of its cloud-based infrastructure. As a result, Cloud303 was able to identify and recommend a series of cost-optimizing AWS products and consolidate the L3Harris' workloads. L3Harris' Stern application,  consisting of a number of microservices using Kubernetes, was successfully migrated from Azure to AWS Elastic Kubernetes Service (EKS). Cloud303 implemented auto-scaling both at the pod and cluster levels, allowing for fast, dynamic scaling of resources in response to spikes in traffic/demand. 



Melissa Jackson: Chief Software Engineer

Defense Contractor
AWS Segment: 

Our Customer

L3Harris Geospatial develops products for the visualization, analysis, and management of geospatial imagery and scientific data for a long list of top tier customers, including the US Defense Department. Some of its larger products include: i) Interactive Data Language (IDL) - a scientific programming language used in particular areas of science (e.g: astronomy, meteorology, and medical imaging); ii) ENVI - an off-the-shelf software program used to visualize, process and analyze geospatial imager; and iii) Jagwire - a data ingest, management, image exploitation, and information dissemination tool.

The Challenge

L3Harris Geospatial's Information Technology (IT) infrastructure was integrated into multiple platforms, including Google Cloud (GCP), Microsoft Azure and VMware, causing it to be both uneconomical and highly inefficient. The company sought to optimize costs as its labor and financial resources were stretched thin due to the management overhead resulting from being on multiple cloud providers. The distributed application also struggled to cope with the scaling of resources in response to spikes in traffic/demand.


Ryan Doyle: Account Manager AWS

Why L3Harris Chose AWS?

In order to increase efficiency, reduce costs, and drive performance, L3Harris Geospatial worked with Cloud303 to develop a single-cloud vendor strategy and consolidate the company's workloads from GCP, Azure, VMware (on-prem) into a commonly managed workload on AWS.

Why L3Harris Chose Cloud303?

Cloud303 is a rapidly growing AWS consulting partner who has shown an aptitude for taking on large engagements covering an array of industries. Furthemore, Cloud303 was uniquely qualified to work with L3Harris Geospatial given its team of core engineers who are experts in all things AWS and have extensive experience in large migration projects. 

      Phil Supinski     Sujaiy Shivakumar
CEO/Solutions Architect      CTO/Solutions Architect

AWS Services Employed:
 Amazon EKS Amazon EC2 VPC

Cloud303's Solution

 Coming from a different cloud provider, L3Harris wanted its infrastructure deployed using a cloud-agnostic Infrastructure as Code. Cloud303 leveraged Terraform, using CI/CD automation to deploy the infrastructure on AWS by implementing centralized CICD pipelines (with development, staging and production environments) with robust manual approval stages to ease the management overhead of the application's development. Every modification to the infrastructure must pass through a CI/CD pipeline that applies quality, security, and policy checks. 

When geospatial data is uploaded into the Stern application, the request is processed and it hits the load balancer, which then proxies the request over the relevant ports to the EKS cluster residing in subnets spanning multi-AZs. Stern APIs orchestrate the application functionalities. Microservices are split between master and worker pods. Master pods listen to incoming requests while the workers' process requests based on messages in the RabbitMQ (switched to AmazonMQ recently). These APIs do many processes - including creating new accounts - and run processes requiring GPU support, etc. All microservices/pods are routed to and orchestrated by Kong in conjunction with Network Load Balancers (NLBs). 


As a result of migrating to AWS, L3Harris Geospatial's Stern application was successfully implemented in the AWS platform resulting in increased savings, improved efficiency, and a tailored cloud-based workload unique to the company's needs. A TCO analysis was conducted to make a compelling case for L3Harris to move their workload from on-premises to the cloud. The TCO analysis estimated that L3Harris would spend $17,816 a month on AWS as opposed to the $64,090 on-premises plus Azure expenditures. The estimates took into account the reduced cost as a result of a reduced workforce by being AWS-centric. This amounted to $46,274 in savings per month. Over the span of 5 years, it was estimated that L3Harris' AWS expenses would be around $1,086,947 at peak utilization. This was a significantly lesser (72%) than the on-premises costs ($3,845,403) over the same period, resulting in a total savings of $2,776,456 over 5 years. 

AWS Programs/Funding Used:
Partner Opportunity Acceleration Funding"MAP" Migration Acceleration Program