The Defense Logistics Agency (DLA), a vital component of the U.S. Department of Defense, plays a critical role in supplying the nation’s military forces and supporting their operations. DLA ensures the availability of necessary resources and materials, spanning across a vast range of logistical needs.

Customer Challenge

The DLA faced a significant challenge in its defense procurement operations due to the lack of comprehensive contract writing software. The absence of such a solution led to inefficient contract processes, including awards, solicitations, Requests for Proposals (RFP), and Requests for Quotations (RFQ). Frequent downtime and suboptimal uptime of legacy systems further exacerbated the problem, resulting in operational disruptions, delayed procurement, and potential compliance issues. The burden of legacy debt and the constraints of an on-premises solution hindered the agency’s agility in adapting to evolving procurement demands. Without addressing these challenges, DLA risked prolonged inefficiencies, operational bottlenecks, and a potential compromise in the delivery of critical resources to the armed forces.

Credence Solution

The Army Contract Writing System (ACWS), emerged as a dynamic solution driven by a low-code platform on Amazon Web Services (AWS). Guided by Credence’s expertise, the project seamlessly integrated ACWS CON-IT, a customization of contract writing source code provided by the Air Force, into the DLA Enterprise Service Management Cloud (ESMC) AWS environment, augmenting DLA capabilities.

By architecting a DoD Secure Cloud Computing Architecture (SCCA) compliant landing zone extension, the project ensured a smooth transition of ACWS into the ESMC AWS Cloud. Credence built out the solution leveraging AWS services including Amazon EC2, Amazon RDS, ELB, Amazon Cloudwatch, AWS Lambda, and multiple other AWS building blocks integrated with the Appian COTS product. Appian and other middleware, database, and application configuration components were migrated from the USDA cloud to the DLA ESMC AWS boundary. This culminated in ACWS going live in July 2023. Today, Credence continues to manage, monitor, and enhance ACWS, delivering an end-to-end solution that revolutionizes DLA’s contract writing processes.

In summary, the partnership between the Army, USDA, DLA, Credence, and Amazon Web Services yielded a transformative solution that unifies efficiency and innovation. By deftly navigating deployment challenges and embracing full life-cycle managed services, ACWS ensures DLA’s procurement operations are optimized, bolstering operational agility and reliability in the ESMC AWS Cloud.

Results and Benefits

The successful delivery of ACWS under a stringent timeline stands as a testament to the partnership’s resilience. Leveraging reusable AWS CloudFormation code and Credence’s robust hosting platform, the solution transcended expectations, offering more than just infrastructure provisioning. It encompassed thorough troubleshooting of niche application issues, ensuring a seamless user experience.

ACWS’s impact was far-reaching. The solution empowered Combat personnel and contractors with modernized capabilities to efficiently source, award contracts, and procure essential combat goods and services. This streamlined approach translated into quantifiable benefits, including a roughly 40% reduction in contract processing time, leading to expedited procurement and swift response to mission-critical requirements. Additionally, the utilization of reusable code and Credence’s hosting platform led to a remarkable reduction in IT operational costs, maintainability, and reuse value, further amplifying the solution’s value proposition.

About Credence

Credence Management Solutions LLC is a prominent leader in the technology and consulting landscape, renowned for its transformative prowess across a multitude of domains. Specializing in business process analysis, the company employs data-driven insights to orchestrate efficient and optimized workflows. Credence’s versatile offerings extend to Business Intelligence (BI), encompassing powerful data visualization and analysis capabilities that empower informed decision-making.

With a strong foothold in Data Engineering, Credence leverages cutting-edge technologies to curate, transform, and deliver structured and unstructured data, unlocking actionable insights. As a trailblazer in innovation, the company offers bespoke Artificial Intelligence (AI) and Machine Learning (ML) solutions that catalyze automation and predictive capabilities.

Credence further distinguishes itself in the realm of IT operations through AI Ops, GitOps, and DevOps. Its proficiency in microservices architecture and DevSecOps principles assures the development and deployment of scalable, secure, and agile applications. Credence’s mastery of cloud infrastructure optimization further showcases its comprehensive capabilities.

Evidenced by the prompt and successful delivery of ACWS within a demanding timeline, Credence’s industry best practices shine through. As an esteemed AWS Managed Service Provider, the company’s commitment to optimizing IT landscapes is unparalleled, and its standing in the AWS Partner Network (APN), as a Consulting Partner, reaffirms its excellence. Credence’s holistic approach, spanning business analysis, data engineering, AI, BI, and operations, establishes it as a trusted partner in driving organizations toward innovation and success in the digital era.

Problem Solution/Definition:
DLA hosted DAAS suite of applications in DLA managed data centers in Dayton and Tracy and had issues monitoring resource utilization, virtual machine provisioning, and meeting the needs of Agile software development teams including issues with application uptime and costs.

Proposed Solution & Architecture:
Applications from the DLA Data Centers to the AWS GovCloud utilizing a multiple availability zone architecture to provide redundancy and failover leveraging multi-AZ RDS Oracle databases and blue-green deployment architecture.

Outcomes of Projects & Success Metrics:
Migration of DAAS applications to AWS has achieved DLA’s plan to shut down the DLA datacenters to reduce operational costs. Operation is the AWS GovCloud is providing more responsive application experience for the end users and has brought down total cost of ownership of the DAAS suite of applications.

Describe TCO Analysis Performed:
The to-be architecture (EC2 & RDS instances) was analyzed and projected based on what was provisioned in the DLA Data Centers. Databases were consolidated to 5 shared database instances. Archive data was migrated from DLA and stored in the AWS Glacier and the minimum number of EC2 instances were provisioned to meet the customer’s needs.

Lessons Learned:
The DAAS Migration implemented a hybrid operational model, joining the DLA and AWS networks into one network while applications, users and databases were migrated. a network delay between DLA applications accessing the databases is AWS did show some latency, however the latency was manageable for the short term. The customers needed to be involved in every step of the migration process to ensure complete agreement and buy-in. In the future DLA can adopt this hybrid mechanism of operations to migrate mission critical applications.

Problem Solution/Definition:
Joint Contingency and Expeditionary Services (JCXS) application was hosted in DISA managed data centers and had several issues including but not limited to provisioning of infrastructure, uptime of application and lack of resource utilization metrics.

Proposed Solution & Architecture:
JCXS Production, Stage and Development environments are all behind a PaloAlto firewall with the F5 performing WAF functionality. We deployed a resilient architecture to ensure maximum uptime of the application even if one of the AWS GovCloud Regions suffers a temporary outage.

Outcomes of Projects & Success Metrics:
As a managed Service Provider, Credence has been able to successfully ensure 100% uptime of the JCXS application over the last one year with no unplanned downtime which exceeds our contractual requirement of the application uptime. With migration of JCXS to the Amazon Web Services (AWS) GovCloud environment while simultaneously sustaining ongoing operations for an innovative, and cost-effective hosting solution, Credence was able to expand capacity and memory on demand for the client to manage increased operating demands. Our engineers also adjusted the firewalls to manage specific processing requirements for the JCXS application.

Describe TCO Analysis Performed:
The to-be architecture was analyzed and projected based on the prior capacity provisioned in the DISA data centers. Current hosting uptime and performance drastically improved over the prior DISA hosting.

Lessons Learned:
DLA leadership has adopted AWS GovCloud as the go-to Cloud for mission critical and mission essential applications. DLA has enjoyed the benefits of cloud hosting as they have migrated several applications to Credence managed AWS GovCloud enclave. Credence will continue to provide our Managed Cloud Services (MCS) and provide cost efficient solutions to DLA.

Problem Solution/Definition:
DLA hosted WAWF suite of applications in DISA managed data centers and had issues monitoring resource utilization, virtual machine provisioning, and meeting the needs of Agile software development teams including issues with application uptime and costs.

Proposed Solution & Architecture:
Credence team of Cloud Engineers and Architects migrated and now manage the suite of applications with absolutely no unplanned downtime of the applications. Credence team used AWS GovCloud infrastructure and developed metrics in collaboration with PIEE (WAWF) Program Management Office (PMO) and streamed data from AWS CloudWatch to train a generative adversarial neural network. This AI then powered the knowledge of the instance utilization behavior and is used to forecast us and recommend optimal provisioning.

Outcomes of Projects & Success Metrics:
Reduced Cost by 20% within 2 weeks after the launch without application downtime. Application functions were not affected due to migration. Query performance was improved by up to 30% as a result of migration to AWS GovCloud.

Describe TCO Analysis Performed:
AWS pricing calculator was used to determine cost of infrastructure used to host and run code.

Lessons Learned:
We learned the benefits of EC2 instance type optimization beyond base utilization scaling within a provisioned instance type. This led us to apply statistical models to predict and optimize provisioning of RDS as well.

Problem Solution/Definition:
DLA hosted FedMall in IBM managed data centers and had issues monitoring resource utilization, virtual machine provisioning, and meeting the needs of Agile software development teams including issues with application uptime and costs. Our client requested insight on the future trajectory of the Fedmall application.

Proposed Solution & Architecture:
Credence migrated FedMall to AWS GovCloud infrastructure using “lift and shift” philosophy of migration. We utilized AWS CloudEndure service to migrate the virtual machines from IBM data centers to AWS GovCloud. KPI’s were calculated from S3 files, through code implemented in glue. Multiple regressions and logistic regressions were fit to the calculated KPIs compiled over years of data to create predictive models. These models were used to predict the health and use of the FedMall application months in advance.

Outcomes of Projects & Success Metrics:
The application migration was seamless to the end users with no unplanned downtime of the application. Furthermore, post migration Credence performed Well Architected Review of the application and has implemented various recommendations in rearchitecting FedMall and utilize many cloud-native services. On application side, projections of user base changes and purchases on the application were possible 3 months in advance with 95% confidence.

Describe TCO Analysis Performed:
AWS pricing calculator was used to determine cost of infrastructure used to host and run code and storage. We have successfully brought down the TCO of FedMall by adopting cloud technologies.

Lessons Learned:
Through building this application, we learned how to efficiently define advanced AI ensemble models in scalable server less AWS infrastructures, like lambda container and glue. We also learned how predictive models can be used as anomaly detectors.