Abstract
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%.
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Ping, Y. Load Balancing Algorithms for Big Data Flow Classification Based on Heterogeneous Computing in Software Definition Networks. J Grid Computing 18, 275–291 (2020). https://doi.org/10.1007/s10723-020-09511-5
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DOI: https://doi.org/10.1007/s10723-020-09511-5