How L3Harris Shifted from Multi-Cloud Chaos to AWS Efficiency



  • 2 October 2023
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AWS Funding Secured by Cloud303
  • Well-Architected
  • Migration Acceleration Program 2.0

About the Customer

L3Harris Geospatial is a leading player in the geospatial analytics domain, specializing in resource-intensive applications that handle complex data analysis and visualizations. Their Stern application, central to their operations, required a cloud solution that could offer scalability, cost-effectiveness, and a seamless user experience.

Executive Summary

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.

With L3Harris Geospatial dealing with large and complex workloads, transitioning their infrastructure from Azure to AWS posed significant challenges. These included potential downtime, loss of data, and a steep learning curve for the team. AWS' Migration Acceleration Program (MAP) was instrumental in mitigating these challenges and facilitating a seamless migration.

The program provided a holistic approach to the migration, offering a proven methodology, tooling, and expert consulting to support and accelerate the migration process. The MAP model consists of three phases: assessment, mobilization, and migration and modernization. Each phase plays a key role in facilitating a successful migration.

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.

Why Cloud303?

  • Automation Expertise Cloud303 excels in automating tedious and complex tasks, making development and operations more efficient. Our expertise in CI/CD pipelines, Infrastructure as Code, containerization and automated testing ensures a faster time-to-market and more robust DevOps strategy.
  • Scalability and Performance With a deep understanding of microservices, containerization, and orchestration, Cloud303 provides scalable solutions that can handle varying workloads without sacrificing performance, ensuring that your systems can handle future demands.
  • Collaboration and Culture Recognizing that DevOps is as much about people and culture as it is about tools and processes, Cloud303 helps foster a culture of collaboration between development and operations teams to facilitate better teamwork and collective ownership of projects.
  • Proven Track Record Cloud303 has a strong history of successful partnerships within the Microsoft industry. Our commitment to excellence, reliability, and client-focused solutions have made us a trusted partner.

Engagement Overview

Cloud303's engagements follow a streamlined five-phase lifecycle: Requirements, Design, Implementation, Testing, and Maintenance. Initially, a comprehensive assessment is conducted through a Well-Architected Review to identify client needs. This is followed by a scoping call to fine-tune the architectural design, upon which a Statement of Work (SoW) is agreed and signed.

The implementation phase kicks in next, closely adhering to the approved designs. Rigorous testing ensures that all components meet the client's specifications and industry standards. Finally, clients have the option to either manage the deployed solutions themselves or to enroll in Cloud303's Managed Services for ongoing maintenance, an option many choose due to their high satisfaction with the services provided.

The Solution

In the assessment phase, MAP 2.0 helped L3Harris and Cloud303 evaluate the readiness of the existing infrastructure for the migration, outlining the potential costs, benefits, and risks. This process provided a clear roadmap, enabling the teams to define a comprehensive migration strategy that aligned with L3Harris' business objectives.

During the mobilization phase, AWS MAP provided the necessary tools and training to prepare the L3Harris team for the migration. This ensured a smooth transition, reducing the chances of disruptions during the actual migration process.

Finally, in the migration and modernization phase, AWS MAP's resources and best practices played a crucial role in ensuring the seamless migration of the Stern application to AWS EKS. AWS MAP's tools aided in the process of re-platforming, re-hosting, and re-architecting the application.

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).

Engineer Quote

The experience of working with L3Harris was rewarding. We overcame migration challenges and implemented robust autoscaling features, improving both efficiency and reliability.

Sujaiy Shivakumar Co-founder & CEO, Cloud303


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.

Post-migration, Cloud303 assisted L3Harris Geospatial in optimizing their new environment, leading to significant cost savings and performance improvement.

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.