The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific f...The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.展开更多
在证明转换规则正确性的基础上,首先利用转换规则对AOE网进行转换,然后从两个方面对转换后的CPN(Colored Petri Nets)模型不合理的地方进行合理性的修改.再利用编写的函数求出从源点到汇点的所有的可达路径,在获得所有可达路径的同时也...在证明转换规则正确性的基础上,首先利用转换规则对AOE网进行转换,然后从两个方面对转换后的CPN(Colored Petri Nets)模型不合理的地方进行合理性的修改.再利用编写的函数求出从源点到汇点的所有的可达路径,在获得所有可达路径的同时也获取了所有可达路径所花费的时间,那么时间最大的就是关键路径.该方法不仅简便直观,而且能够在保证正确性合理性的前提下提高执行效率,减小时间复杂度.展开更多
文摘The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.
文摘在证明转换规则正确性的基础上,首先利用转换规则对AOE网进行转换,然后从两个方面对转换后的CPN(Colored Petri Nets)模型不合理的地方进行合理性的修改.再利用编写的函数求出从源点到汇点的所有的可达路径,在获得所有可达路径的同时也获取了所有可达路径所花费的时间,那么时间最大的就是关键路径.该方法不仅简便直观,而且能够在保证正确性合理性的前提下提高执行效率,减小时间复杂度.
