Service resources allocation and scheduling is one of the challenging and complex problems in computation-economy-driven open grid service architecture. This paper proposes a time-cost tradeoff workflow scheduling algorithm in which cost is optimized for schedules with the expectation to minimize workflow duration. Dynamic service selection strategy is adopted to adapt to dynamic shared and autonomous resources in grids. Simulation results show that the algorithm can achieve less completion time and lower cost which can meet requirements in practical applications

The selection of initial clustering centers of traditional K-means algorithm is random and sensitive to outliers, which leads to unstable clustering results and low accuracy. To solve the above problems, the NDK-means algorithm based on neighbourhood density is proposed. Firstly, the grid distribution characteristics of samples are obtained by multi-dimensional grid division. Then, by defining the grid density and the grid neighbourhood density, several local high-density grids are determined. At the same time, the iteration factor is introduced to merge adjacent high-density grids to obtain a candidate set of initial clustering centers. Finally, combined with grid density and distance, K initial clustering centers are obtained by using Max-Min-distance algorithm. Experiments on UCI dataset show that compared with k-means algorithm and literature algorithm, the accuracy of NDK-means algorithm is improved by nearly 11% and 4%, and the iteration speed is improved by 70% and 60% respectively. The algorithm improves the accuracy of clustering and the results have good stability.