TidalScale: Pioneers in Software-Defined Server Technology

The fuel that the IT industry runs on today has also manifested as one of its headaches—Data! The challenge lies at creating value out of the abundant and rapidly proliferating data in a timely manner. The value of an organization’s data is diluted when the insights derived are delayed due to the use of traditional databases and analytics applications. Although SAP HANA has been addressing this predicament proactively by delivering insights faster, the need to procure a system that can store this data still exists. That’s where TidalScale comes in. With their pioneering work in Software-Defined Server technology, the company delivers a 100 percent software solution that enables enterprise to size systems based on any x86 server hardware to meet even their largest data storage and analysis requirements. “In the history of computing, it has always been about how one could make things fit into the given computer. We have turned this model where the computers can be sized appropriately,” says Gary Smerdon, the president & CEO of TidalScale. Through its Software Defined Server technology, TidalScale solves the major pain-point of enterprises having to size their systems on demand—either on-premise or cloud—for data computation and storage.

Certified by SUSE, TidalScale creates a system that can deliver on the memory needs of a company by tying together multiple servers and thereby enabling in-memory compute. The system uses in-built machine learning below the application layer to provide appropriate performance metrics. TidalScale’s proprietary technology provides IT organizations with the tools they need to deploy the high-performance SAP HANA in-memory database, at-scale, but still fit within increasingly aggressive IT operational and capital budgets. “TidalScale Software-Defined Servers provide the ideal platform to ensure your data does not outgrow your infrastructure or your budget,” states Smerdon. The servers aggregate the resources of multiple cost-effective, commodity servers into one or more Software-Defined Servers right-sized for enterprise needs—under a single, unmodified Linux operating system. It utilizes real-time machine learning deployed in the HyperKernel, a bare-metal distributed ‘inverse hypervisor’, to continuously optimize workload performance.