Exar's data compression and security solutions address the evolving IT infrastructure needs by providing high availability and performance, workload optimization, security for data in transit or at rest, storage capacity optimization (SCO) and reduced total cost of ownership (TCO)
Mobile devices, social media, sensor-enabled e-commerce, the cloud and web 2.0 applications are transforming the global IT infrastructure. The amount and type of data or "Big Data" is changing and growing exponentially. Big Data, which now includes increasing amounts of unstructured data, needs to be available anytime, anywhere and in various formats. As a result, the IT infrastructure must be adaptive, always-on, scalable, secure and cost effective.
Large amounts of data are expensive to manage, store and backup. Exar's high performance data compression solutions can minimize the data footprint (SCO); thereby, reducing management, equipment, power and cooling costs (lower TCO). In addition, network bandwidth can increase as the amount of data moving across the network is greatly reduced.
Although general purpose CPUs have gotten significantly faster, data compression algorithms are computationally intensive and can saturate multiple multi-core CPUs. Exar's Express DX cards and devices can offload data compression algorithms and reduce CPU utilization by over 95% enabling increased system efficiency and workload optimization, higher performance, reduced power consumption and lower TCO.
Cloud storage, social media sites and web based applications contain enormous amounts of confidential information in their databases. As data moves online it becomes highly vulnerable. Exar's Express DX cards and processors can address that vulnerability by encrypting the data stored at rest in those data bases as well as data that is being sent over the global network. Encryption algorithms place the same stress on general purpose CPUs as data compression.
Exar's Express DX cards and processors can simultaneously offload the data compression and encryption algorithms and still reduce CPU utilization by over 95%, enable workload optimization, maintain high performance, reduce power consumption and lower TCO.