Distributed network architecture of heterogeneous computing faces with such problems as strict performance constraints of network control, unpredictable mapping relationship between computing data algorithms of different mobile terminals and inconsistency between computing algorithms and link control of data networks. In order to solve the above problems, we begin with software definition network architecture and load balancing algorithm for heterogeneous computing, and gradually improve the real-time and reliability of heterogeneous computing. On the one hand, the heterogeneous computing data of fog node and cloud computing system are distributed. The centralized service of software-defined network combines with distributed computing of mobile edge terminal and its subnet. On the other hand, we define the centralized information and distributed scheduler of the network. In addition, we deploy the optimal assignment of data sharing and heterogeneous computing tasks in real time with ellipse-partitioned area as the object. A series of algorithms for classifying and assigning heterogeneous computing data streams in software-defined networks are designed to achieve the optimal balance among load balancing, minimum classification of large data streams, minimum resource occupation and time constraints. Experimental comparison compared and evaluated the Load Balancing with big data stream (LBBS), Load Balancing with Heterogeneous Computing (LBHC) and the proposed LBBHD. Compared with the other two algorithms, the proposed algorithm improves workload skewness, throughput and load balancing error respectively about 2.1%, 1.96%, 2.9%, 2.2%; 5.57%. 2.51%.

中文翻译：

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软件定义网络中基于异构计算的大数据流分类负载均衡算法

异构计算的分布式网络架构面临着严格的网络控制性能约束，不同移动终端的计算数据算法之间的映射关系不可预测，计算算法与数据网络的链路控制不一致等问题。为了解决上述问题，我们从软件定义网络体系结构和异构计算的负载均衡算法入手，逐步提高异构计算的实时性和可靠性。一方面，分散了雾节点和云计算系统的异构计算数据。软件定义网络的集中式服务与移动边缘终端及其子网的分布式计算相结合。另一方面，我们定义了网络的集中信息和分布式调度程序。此外，我们以椭圆分割区域为对象，实时部署数据共享和异构计算任务的最佳分配。设计了一系列用于在软件定义的网络中分类和分配异构计算数据流的算法，以实现负载平衡，大数据流的最小分类，最小资源占用和时间限制之间的最佳平衡。实验比较比较并评估了大数据流负载平衡（LBBS），异构计算负载平衡（LBHC）和拟议的LBBHD。与其他两种算法相比，该算法分别将工作量偏度，吞吐量和负载均衡误差分别提高了2.1％，1.96％，2.9％，2.2％。5，57％。2.51％。