Abstract:

This paper investigates the most appropriate application programming interface (API) that best accelerates the flow-based applications on the wireless sensor networks (WSNs). Each WSN include many sensor nodes which have limited resources. These sensor nodes are connected together using base stations. The base stations are commonly network systems with conventional processors which are responsible for handling a large amount of communicated data in flows of network packets. For this purpose, classification of the communicated packets is considered the primary process in such systems. With the advent of high-performance multicore processors, developers in the network industry have considered these processors as a striking choice for implementing a wide range of flow-based wireless sensor networking applications. The main challenge in this field is choosing and exploiting an API which best allows multithreading; i.e. one which maximally hides the latency of performing complex operations by threads and increases the overall efficiency of the cores. This paper assesses the efficiency of Thread, Open Multiprocessing, and Threading Building Blocks (TBB) libraries in multithread implementation of set-pruning and grid-of-tries packet classification algorithms on dual-core and quad-core processors. In all cases, the speed and throughput of all parallel versions of the classification algorithms are much more than the corresponding serial versions. Moreover, for parallel classification of a sufficiently large number of packets by both classification algorithms, TBB library results in higher throughput and performance than the other libraries due to its automatic scheduling and internal task stealing mechanism.

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