A Design of Pipelined Architecture for on-the-Fly Processing of Big Data Streams

Abstract

Conventional processing infrastructures have been challenged by huge demand of stream-based applications. The industry responded by introducing traditional stream processing engines along-with emerged technologies. The ongoing paradigm embraces parallel computing as the most-suitable proposition. Pipelining and Parallelism have been intensively studied in recent years, yet parallel programming on multiprocessor architectures stands as one of the biggest challenges to the software industry. Parallel computing relies on parallel programs that may encounter internal memory constrains. In addition, parallel computing needs special skillset of programming as well as software conversions. This paper presents reconfigurable pipelined architecture. The design is especially aimed at Big Data clustering, and it adopts Symmetric multiprocessing (SMP) along with crossbar switch and forced interrupt. The main goal of this promising architecture is to efficiently process big data streams on-the-fly, while it can process sequential programs on parallel-pipelined model. The system overpasses internal memory constrains of multicore architectures by applying forced interrupts and crossbar switching. It reduces complexity, data dependency, high-latency, and cost overhead of parallel computing.