CAJ | 학술논문

智能电网环境下数据密集型应用往往涉及跨数据中心的数据传输和数据中心内的数据迁移,这对数据分布提出了新的挑战。为了充分利用计算存储资源,满足智能电网大规模数据的可靠存储和高效处理的实际需求,提出了基于云计算的数据密集型存储方法,该方法将数据集映射成数据空间的点集。设计了两阶段分类过程：第1阶段基于传统的K均值算法实现点集的初始分类;第2阶段针对各数据集与初始聚类的隶属关系,引入数据迁移的代价函数,对初始分类进行调节,实现数据集到数据中心的布局方案。实验结果表明,该算法能够有效提高数据存取效率并兼顾全局负载均衡。
지능전망배경하수거밀집형응용왕왕섭급과수거중심적수거전수화수거중심내적수거천이,저대수거분포제출료신적도전。위료충분이용계산존저자원,만족지능전망대규모수거적가고존저화고효처리적실제수구,제출료기우운계산적수거밀집형존저방법,해방법장수거집영사성수거공간적점집。설계료량계단분류과정：제1계단기우전통적K균치산법실현점집적초시분류;제2계단침대각수거집여초시취류적대속관계,인입수거천이적대개함수,대초시분류진행조절,실현수거집도수거중심적포국방안。실험결과표명,해산법능구유효제고수거존취효솔병겸고전국부재균형。
In a distributed environment of the smart grid, data-intensive applications often involve complex transmissions between and within the data centers which may have to use large amounts of datasets. An application may need several datasets located in different data centers facing great challenges including the high cost of data movement between data centers and data dependency within the same center. Considering the efficient storage and management of large scale data in the smart grid, a two-stage strategy is proposed for the data placement. In the first stage, an initial classification is achieved by the K means; while in the second, datasets are placed in different centers by a clustering scheme based on the data dependency. Simulations show that the algorithm can effectively reduce the cost of data movement while performing an even data distribution.