Case Studies > ReachForce Containerizes 200+ AWS Instances with Portworx to Reduce Data Center Footprint and CPU Utilization

ReachForce Containerizes 200+ AWS Instances with Portworx to Reduce Data Center Footprint and CPU Utilization

Customer Company Size
Mid-size Company
Region
  • America
Country
  • United States
Product
  • Portworx Enterprise
  • AWS ECS
  • Docker
  • Jenkins
  • MongoDB
Tech Stack
  • Docker
  • AWS ECS
  • Jenkins
  • MongoDB
  • etcd
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Cost Savings
  • Productivity Improvements
Technology Category
  • Infrastructure as a Service (IaaS) - Cloud Computing
  • Application Infrastructure & Middleware - API Integration & Management
Applicable Industries
  • Software
Applicable Functions
  • Sales & Marketing
  • Business Operation
Use Cases
  • Process Control & Optimization
  • Predictive Maintenance
  • Inventory Management
Services
  • Cloud Planning, Design & Implementation Services
  • System Integration
About The Customer
ReachForce is a company that operates in the marketing automation space. Their main function is to make marketing leads more valuable and actionable. Most leads are limited to business card information such as first name, last name, title, company, street address, email address, and phone number. These leads flow into marketing automation systems from a variety of sources. ReachForce is partnered with Marketo, Eloqua, and Salesforce, and their service can validate, deduplicate and enrich these leads with many more fields of information about the business. This enriched information can make a salesperson more effective and can also make sales automation much more effective.
The Challenge
ReachForce, a company in the marketing automation space, was facing several challenges with their AWS data center. The data center, comprised of four environments (Dev, QA, Performance Lab, and Production), consisted of about 215 EC2 instances. The company had re-implemented their SaaS offering in a micro-services architecture, using a separate AWS instance for each microservice. However, this led to the need to maintain over 200 Linux OS instances, and their average CPU utilization was significantly below best industry practices. Additionally, their AWS EBS storage was severely under-utilized, with only 25% of purchased capacity being used. At the same time, EC2 instances were over-provisioned by 100%, increasing the cost of operating the ReachForce SaaS platform.
The Solution
To address these challenges, ReachForce decided to re-implement their data center as a containerized architecture. They believed that they could reduce their data center footprint from 215 EC2 instances down to as few as fifteen using containers. They aimed to reach a CPU utilization of 25%. They set up the first development cluster in November and were about to roll out a preview of their first application based on containers. This application is a customer portal and consists of a database, a two-container SSO solution, and a customer web portal that reinforces the value proposition of their SaaS service. They used etcd and confd for service discovery and environment-specific configuration. The CI/CD pipeline was built out using Jenkins, containerized and with its workspace on a Portworx volume. They elected to go with AWS ECS for container orchestration. They also used Portworx Enterprise for cloud native storage and data management.
Operational Impact
  • Portworx aggregates all the EBS storage on the container hosts and thanks to thin provisioning, ReachForce was able to serve the same number of volumes and the same amount of data with half as much AWS storage.
  • Containerization, including mission-critical data services, allowed ReachForce to reduce their AWS compute footprint by 50%.
  • Because Portworx handles the connectivity between the containers and data, ReachForce’s container placement strategies can be quite simple. It is not necessary to ensure that the container is running on the same container host where the persistent volume exists in order to ensure application performance and availability.
Quantitative Benefit
  • Reduced AWS compute footprint by 50%
  • Served the same number of volumes and the same amount of data with half as much AWS storage
  • Aimed to reach a CPU utilization of 25%

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