Zhangjun Wu

TL;DR: The hierarchical scheduling strategy is being implemented in the SwinDeW-C cloud workflow system and demonstrating satisfactory performance, and the experimental results show that the overall performance of ACO based scheduling algorithm is better than others on three basic measurements: the optimisations rate on makespan, the optimisation rate on cost and the CPU time.

TL;DR: Experimental results show that the proposed Revised Discrete Particle Swarm Optimization (RDPSO) algorithm can achieve much more cost savings and better performance on make span and cost optimization.

TL;DR: A novel hybrid non-linear time-series segmentation algorithm is designed to facilitate the discovery of duration-series patterns and demonstrates the excellent performance of this strategy in the forecasting of activity duration intervals for both long-duration and short-duration activities.

TL;DR: An enhanced version of the Opposition-Based PSO (OCLPSO) that is called the quasi-oppositional comprehensive learning particle swarm optimizers (QCLPSO), which uses quasi opposite particles, which is generated from the interval between the median and the opposite position of the particle.

TL;DR: A novel general automatic and cost-effective exception handling framework is proposed in this paper where fine-grained temporal violations are defined based on the empirical function for the capability lower bounds of the exception handling strategies.