The CLASS data analytics platform provides the necessary interfaces for the application programmers to develop complex big-data analytics workflows, for real-time execution across the compute continuum. The key to this novel approach is the use of an open-source serverless platform based on Apache OpenWhisk. The platform allows multiple types of big-data analytics back-ends to integrate in a uniform mesh where workloads and components can interact, be invoked or respondto events.
In CLASS, support has been provided for the following data analytics back-ends:
- The Lithops framework, which is an open-source lightweight implementation of Map/Reduce programming model over the Apache Openwhisk serverless platform, aiming to massively scale Python applications and fully support concurrent execution. In CLASS, Lithops has been used to accelerate the computation of the trajectory prediction and collision detection analytics methods.
- The COMP Superscalar (COMPSs) programming model. The COMPSs task-based programming model enables programmers to develop distributed applications following the sequential programming paradigm and using standard languages (e.g., Python, Java, C/C++), while abstracting applications from the underlying infrastructure. In CLASS, the COMPSs programming model has been employed to enable the distribution and concurrent execution of the data analytics workflow for the collision detection and air pollution use cases.
- A Deep Neural Network (DNN) - a suited and personalised version of YOLO, using the tkDNN library that exploits the capabilities of NVIDIA boards to obtain the best inference performance.
Another contribution of CLASS has been the delivery of an EXtended PREdictability ServerlesS (EXPRESS) prototype as part of the data analytics platform. Within CLASS, EXPRESS has been completely redesigned as a portable solution for predictable execution of serverless functions on top of OpenWhisk.