【摘要】 现实的通信网络由多重网络组成,构成具有多维任务分配的复杂网络结构,在任务处理中会产生并行振荡,对复杂网络中的振荡抑制是提高复杂网络并行处理的重要因素。传统的并行振荡方法采用奇异值分解降维的特征匹配算法,在面对大规模复杂任务求解时产生大量的内存需求和时间损耗。提出一种基于隐性群体双模分解的并行振荡抑制算法,首先进行复杂网络多维业务并行处理模型设计,得到了复杂网络多维业务并行处理模型的指标参量体系,采用隐性群体并行特征匹配方法实现双模特征匹配并行处理。仿真实验表明,采用该算法进行复杂网络隐性群体的并行特征匹配,实现并行处理和串行处理,双模分解的时间成本及空间成本大幅降低,加速比提高2倍,有效抑制网络振荡。算法在进行复杂网络多任务并行处理中发包数量,时延和能量效率等方面具有优越性能。更多还原

【Abstract】 The reality of the communication network composed of multiple network,complex network structure with multi task allocation,in task processing will produce parallel oscillation,the oscillation in complex networks is an important factor to improve the inhibition of complex network parallel processing.Parallel oscillation by using the traditional method of singular value decomposition to reduce the dimensionality of the matching algorithm,the memory requirement and the time loss a lot of in the face of large-scale complex task is solved.This paper proposes a parallel oscillation suppression algorithm based on dual decomposition of hidden groups,first carries on the complex network of multi dimension business parallel processing model design,the index system of multi dimension business parameters of complex network parallel processing model,implicit parallel feature matching method to realize the double feature matching parallel processing.Simulation results show that,the parallel feature matching by using the algorithm of hidden groups of complex network,to realize the parallel processing and serial processing,the least square singular value decomposition time cost and space cost is reduced greatly,improve the speedup of 2 times,effectively inhibit the network oscillations.Algorithm in the complex network of multi task parallel processing in the number,it has the superior performance of delay and energy efficiency.更多还原