Abstract: Various embodiments of the disclosure described a system and method that implements Hi-PHY operations in a mobile network using a microservices-based architecture across a variety of heterogeneous multi-core processing nodes. Further, the system and method facilitate the optimal mapping of microservices to each element (e.g., hardware processing element or the like) of the processing node(s) based on defined optimization targets associated with deployment constraints. The system and method described may enable the portability of Hi-PHY operations by separating the functionality and implementation aspects of each microservice. Further, the system and method enable the scalability of Hi-PHY operations by creating multiple instances of microservices to distribute the processing load efficiently. The system and method described further enable the dynamic implementation of Hi-PHY processing chains through the utilization of microservices.

Abstract: System and method for managing hardware containerization in a wireless network architecture, are described. In one aspect, capability information of a plurality of hardware units is transmitted to a host processor to select a set of hardware units in a system-on-chip (SoC). Configuration information that includes the selection of one or more hardware allocation parameters and one or more types of statistics for one or more processing flows is received by the selected set of hardware units. A hardware container is configured to enable SoC resource allocation related to a control group and a namespace of SoC resources via a plurality of hardware modules in the SoC. The hardware container is managed based on periodic collection and analysis of the statistical data via a plurality of instrumentation modules in the SoC, and the impact on the one or more processing flows is tracked at the host processor.

Abstract: Configurations of a system and a method for implementing a framework that optimizes the execution and deployment of operations in a computationally intensive software applications including varying complexity workloads, are described. In one aspect, a portable framework (PF) may transform an algorithmic routine developed via an IDE into an intermediate form. The PF may enable adding constraint definitions to the intermediate form of the algorithmic software routine. The PF may further enable or provision including constraint definitions, hardware architecture description and multiple optimization metrics to the intermediate form. Based on the constraint definitions, the hardware architecture description, and the multiple optimization metrics the PF may determine computing resources from multiple heterogenous hardware resources deployed on a hardware platform. The execution of the operations may be optimized by deploying the operations to be executed on the determined computing resources.