设施选址对提高居民生活质量至关重要,利用地理可达相似性聚类对空间元素进行分类是求解此类问题的重要方法。然而,现有的应用于地理可达性分析的聚类算法存在地理可达性测度不准确、不涉及簇中心选取或簇中心不可达等缺陷,不能有效求...设施选址对提高居民生活质量至关重要,利用地理可达相似性聚类对空间元素进行分类是求解此类问题的重要方法。然而,现有的应用于地理可达性分析的聚类算法存在地理可达性测度不准确、不涉及簇中心选取或簇中心不可达等缺陷,不能有效求解真实场景下的设施选址问题。基于此,本文提出一种基于可达距离的模糊C均值聚类算法(Fuzzy C-Means based on Reachable Distance,FCM-RD)。FCM-RD算法改造了经典FCM算法的目标函数、隶属度函数和簇中心函数,使其适用基于可达距离的聚类分析。其次,以沿路网的最短路径距离作为可达距离衡量元素间的地理可达相似性,将聚类元素的二维地理坐标映射为路网坐标,并以此设计簇中心迭代机制,实现在聚类过程中以可达距离迭代不受约束的可达簇中心。同时,对所提簇中心迭代机制的有效性进行理论分析和实验验证,结果表明,FCM-RD算法在每次迭代中所选的各簇簇中心唯一且为当前簇类目标函数最小值点。最后,基于真实地理场景的仿真实验表明,相比基准算法,FCM-RD不仅能获得位置不受限的可达簇中心,而且能获得更好的聚类效果,为实际场景下的地理空间聚类方案提供了有效且精准的解决方案。展开更多
The dynamics of the globalized multimedia sources and request demands, which requires high computations and bandwidths, makes the IT infrastructure a challenge for live streaming applications. Migrating the system to ...The dynamics of the globalized multimedia sources and request demands, which requires high computations and bandwidths, makes the IT infrastructure a challenge for live streaming applications. Migrating the system to a geo-distributed cloud and leasing servers is an ideal alternative for supporting large-scale live streaming applications with dynamic contents and demands. The new challenge of multimedia live streaming applications in a geo-distributed cloud is how to efficiently arrange and migrate services among different cloud sites to guarantee the distribute users’ experience at modest costs. This paper first investigates cloud leasing policies for live streaming applications and finds that there is no detailed algorithm to help live streaming applications arrange and migrate services among different cloud sites. Then, we present a quality of service（Qo S） guarantee cost-effective cloud leasing policy for live streaming applications. Meanwhile, we design a genetic algorithm（GA） to deal with the leasing policy among cloud sites of diverse lease prices. Experimental results confirm the effectiveness of the proposed model and the efficiency of the involved GA.展开更多
文摘设施选址对提高居民生活质量至关重要,利用地理可达相似性聚类对空间元素进行分类是求解此类问题的重要方法。然而,现有的应用于地理可达性分析的聚类算法存在地理可达性测度不准确、不涉及簇中心选取或簇中心不可达等缺陷,不能有效求解真实场景下的设施选址问题。基于此,本文提出一种基于可达距离的模糊C均值聚类算法(Fuzzy C-Means based on Reachable Distance,FCM-RD)。FCM-RD算法改造了经典FCM算法的目标函数、隶属度函数和簇中心函数,使其适用基于可达距离的聚类分析。其次,以沿路网的最短路径距离作为可达距离衡量元素间的地理可达相似性,将聚类元素的二维地理坐标映射为路网坐标,并以此设计簇中心迭代机制,实现在聚类过程中以可达距离迭代不受约束的可达簇中心。同时,对所提簇中心迭代机制的有效性进行理论分析和实验验证,结果表明,FCM-RD算法在每次迭代中所选的各簇簇中心唯一且为当前簇类目标函数最小值点。最后,基于真实地理场景的仿真实验表明,相比基准算法,FCM-RD不仅能获得位置不受限的可达簇中心,而且能获得更好的聚类效果,为实际场景下的地理空间聚类方案提供了有效且精准的解决方案。
基金Supported by the National Key Technology R&D Program during the Twelfth Five-Year Plan Period(2015BAK27B02)
文摘The dynamics of the globalized multimedia sources and request demands, which requires high computations and bandwidths, makes the IT infrastructure a challenge for live streaming applications. Migrating the system to a geo-distributed cloud and leasing servers is an ideal alternative for supporting large-scale live streaming applications with dynamic contents and demands. The new challenge of multimedia live streaming applications in a geo-distributed cloud is how to efficiently arrange and migrate services among different cloud sites to guarantee the distribute users’ experience at modest costs. This paper first investigates cloud leasing policies for live streaming applications and finds that there is no detailed algorithm to help live streaming applications arrange and migrate services among different cloud sites. Then, we present a quality of service（Qo S） guarantee cost-effective cloud leasing policy for live streaming applications. Meanwhile, we design a genetic algorithm（GA） to deal with the leasing policy among cloud sites of diverse lease prices. Experimental results confirm the effectiveness of the proposed model and the efficiency of the involved GA.
