Wi-Fi Enterprise Network Analysis

THE CLIENT

Headquartered in Santa Clara, California, Aruba is the industry leader in wired, wireless, and security networking solutions.

business challenges

This network performance management startup was looking for developing a solution that could improve the operation of wireless networks through cloud-based analytics and data science. They wanted to enable an inventory of Wi-Fi clients, capabilities and location, present real-time geo-location enabled Wi-Fi client mapping, monitor Wi-Fi networks, and flows between clients and APs. The solution needed to be able to analyze real-time operational flows, faults, and performance to generate insights that could reduce connectivity issues. Through these solutions, Aruba wanted to gain deep insights, share recommendations for solutions, and even self-heal Wi-Fi networks. Zymr was chosen to develop this solution as we hold deep domain expertise in Wi-Fi networking, SDN, and big-data analytics.

business impacts/key
results achieved

Aruba developed a Wi-Fi cloud-controller for providing real-time analytics using Wi-Fi probes. Zymr helped develop a cloud-based multi-tenant wireless network management and orchestration solution with a visualization tier consisting of secure admin console and mobile apps. The solution was capable enough to gather operational data from Wi-Fi network elements in real-time through sensors and analyze it for actionable operational intelligence. Later, the Aruba network was subsequently acquired by HP Enterprise, a market leader in enterprise wireless network access solutions.

Strategy and solution

We provided a full-stack CloudTech SDN solution to develop Aruba’s Wi-Fi cloud-controller to deliver real-time analytics using Wi-Fi probes. Aruba defined a multi-tiered implementation architecture comprising four tiers. We helped in enabling a cloud-based Wi-Fi analytics service for multi-tenancy and the locational hierarchy of campus, building, floor, and zone. We developed a common management ontology with the DMTF CIM model and other DMTF profiles as the guides. An analytical framework was created using Hadoop infrastructure.Our team also worked on a comprehensive fault, system, and audit logging. We created Jenkins and GitFlow based CI process with nightly builds, automated unit-testing, and system testing, code-reviews, and regression testing, and Git was used as a repository. We formed a comprehensive test plan for portions of the system for which it was responsible, such as FCAPS, Analytics, and Correlations of faults and created parts of the automated testing, primarily using Python, and enabled integration with the client’s automation framework that was build using TestNG and Selenium. Amazon AWS was used to create testing, staging, and production cloud deployment DevOps. We automated the push of new builds and instantiation using Chef.