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R&SvPACE Enables Awareness in Cloud Environments


Rohde & Schwarz’s ipoque GmbH has announced the launch of R&SvPACE, a vector packet processing (VPP)-native deep packet inspection (DPI) engine designed specifically to meet the IP traffic visibility needs in cloud computing environments. It is capable of handling masked traffic and traffic that is delivered via CDNs and VPNs. The R&SvPACE powers virtualized and cloud-native functions. The R&SvPACE comes equipped with network functions which include policy control and traffic management along with the analytics functions with application awareness.

According to the company, R&SvPACE combines traditional DPI techniques such as statistical/heuristical and behavioral analyses with metadata extraction and encrypted traffic intelligence (ETI) to accurately and reliably identify and classify protocols, applications, and services. It leverages vector packet processing to deliver highly efficient and scalable identification and classification of IP traffic for virtualized native functions, cloud-native functions, and 5G UPFs.

The encrypted traffic Intelligence (ETI) techniques include deep learning, machine learning, and high-dimensional data analysis. This technique allows traffic inspection in the cloud even from the encrypted traffic, which includes using protocols and techniques such as TLS 1.3, TLS 1.3 0-RTT, ESNI, ECH, DoT, and DoH. It is capable of handling anonymized and obfuscated traffic that is delivered via CDNs and VPNs

For seamless integration, R&SvPACE features well-defined APIs and a frequently updated signature library. It also provides support for first packet classification using smart caching techniques. With VPP at its core, R&SvPACE pushes DPI processing speeds to the next level with an improved average clocks-per-packet ratio which leads to a speedup of up to 3 times. It also boasts a memory footprint of fewer than 400 bytes per 5-tuple connection and 700 bytes per network endpoint. Additionally, it enables thread-safe endpoint access across multiple worker cores.

“The shift towards the cloud calls for packet processing technologies capable of delivering the speeds, latency, and cost efficiency necessary to support growing traffic volumes and the breadth of applications hosted and delivered from the cloud. The adoption of VPP, which involves vector-based batch processing using a locally-stored vertex memory cache, significantly reduces CPU and energy consumption, allowing our DPI technology to deliver unrivaled performance and scalability in the cloud and virtualized environments,” said Dr. Martin Mieth, VP of Engineering at ipoque.






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