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Randomness as a Service, the Cloud computing paradigm

The cloud computing service model today is established and almost all companies are embracing a strategy of “go to cloud”, both in part and in full. Depending on a company’s requirements it could be a public, private or hybrid cloud service.

30 de agosto de 2023
3 min read
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Quside’s QRNG “as a service” for Cloud Computing

 

The cloud computing service model today is established and almost all companies are embracing a strategy of “go to cloud”, both in part and in full. Depending on a company’s requirements it could be a public, private or hybrid cloud service.

Among the key concepts of fundamental relevance of cloud computing is the democratization of the process to access computing, the platform, and software resources. This is due to the sharing of resources, a key concept in cloud. This includes the automation of several aspects and the management of the underlaying layers by the cloud provider.

Now it is easy to get access to computing capabilities as a Map-Reduce big cluster, Graphical Processing Units (GPU) or Field Programmable Gate Arrays (FPGA). Without having to manage budget, purchasing, deployment and ongoing life cycle managements. High-performance, high-quality and scalable random numbers are a widely used computing resource until now were not easy to access.

The use of random numbers is fundamental for several use cases, cybersecurity and computing tasks. It’s widely used in many industries for simulation, optimization and prediction jobs. Unfortunately, the access to this basic resource is not as easy as we thought. Current random number generators widely used in IT are slow and starve quickly. And this problem is particularly challenging in virtualized and IoT environments.

Building on our proprietary phase-diffusion quantum random number generation (QRNG) technology Quside provides randomness sources that overcome these issues. Thanks to the cloud service model now it is possible to get access to the high-speed, high-quality random numbers provided by Quside’s QRNG “as a service” such as in this success case with Telefonica Tech and Qrypt.

With this model a financial analyst, can quickly obtain Terabytes of statistically distributed random numbers to efficiently use them in their Monte Carlo simulations that evaluate the risk of taking certain market actions. Just by calling a networked function made available to them by the cloud provider.

The cloud provider can benefit from this random number as a service function as well for their own requirements (security, monitoring and analysis). In conclusion, now this special computing resource that is entropy can be made available for the users in the cloud model, on-demand, as a service.