CAJ | 학술논문

在模糊云计算环境下,需要对特定数据进行识别定位,实现目标数据信号的准确检测和访问.传统方法采用先分层后分支的数据目标资源识别定位算法,定位识别性能计算复杂度较大,准确度不高,提出一种基于通信开销缩减和冗余任务删除的特定数据目标资源识别定位技术.首先对DAG图中的任务进行任务归并,然后将DAG图分层,从整个任务图的全局出发考虑任务的优先级,构建模糊云计算模型,设计模糊云计算核函数,创建多个线程的信息流特征编码,考虑对整个任务图调度时间起决定作用的关键任务,设计通信开销缩减算子,将调度列表头结点分配到使其具有最小最早完成时间的处理器内核上,提高对特定数据的目标资源定位识别性能.仿真实验得出,该算法定位精度较高,对目标资源的冗余任务进行有效删除,明显提高了任务调度效率,收敛性能较好.
재모호운계산배경하,수요대특정수거진행식별정위,실현목표수거신호적준학검측화방문.전통방법채용선분층후분지적수거목표자원식별정위산법,정위식별성능계산복잡도교대,준학도불고,제출일충기우통신개소축감화용여임무산제적특정수거목표자원식별정위기술.수선대DAG도중적임무진행임무귀병,연후장DAG도분층,종정개임무도적전국출발고필임무적우선급,구건모호운계산모형,설계모호운계산핵함수,창건다개선정적신식류특정편마,고필대정개임무도조도시간기결정작용적관건임무,설계통신개소축감산자,장조도렬표두결점분배도사기구유최소최조완성시간적처리기내핵상,제고대특정수거적목표자원정위식별성능.방진실험득출,해산법정위정도교고,대목표자원적용여임무진행유효산제,명현제고료임무조도효솔,수렴성능교호.
In the fuzzy cloud computing environment, the need for recognition and localization of specific data, to realize the accurate detection and access the target data signal. The traditional method using a hierarchical algorithm first after target recognition and localization of the branch of the data resource, performance to identify the location of the computational complexity greatly, the accuracy is not high, put forward a kind of communication cost reduction and redundant task dele-tion of specific data resource positioning technology based on target recognition. The first task on the DAG tasks in the merged graph, then DAG layered graph, take the whole task graph considers the priority of the task, to construct a fuzzy cloud computing model, the calculation of cloud design fuzzy kernel function, feature coding information flow to create mul-tiple threads, considered mission critical decisive effect to the whole task graph scheduling time the design of communica-tion overhead reduction operator, the scheduling list head node assigned to enable it to have the minimum earliest finish time of the processor core, the goal of improving resource location recognition properties for specific data. The simulation experiments, the algorithm positioning accuracy of redundant tasks to target resources effectively remove, obviously im-prove the scheduling efficiency, good convergence performance.