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Since cloud computing provides computing resources on a pay per use basis, a task scheduling algorithm directly affects the cost for users. In this paper, we propose a novel cloud task scheduling algorithm based on ant colony optimization that allocates tasks of cloud users to virtual machines in cloud computing environments in an efficient manner. To enhance the performance of the task scheduler in cloud computing environments with ant colony optimization, we adapt diversification and reinforcement strategies with slave ants. The proposed algorithm solves the global optimization problem with slave ants by avoiding long paths whose pheromones are wrongly accumulated by leading ants.

In this study, we proposed the most efficient driving posture based on the analysis of quantitative muscular strength and fatigue degree according to posture. Since driving include complicated actions required by a variety of ability and cause by extremes concentration or strain, drivers tend to feel tired easily. However, drivers can't recognize the fatigue degree by themselves. Moreover, the method for measuring the quantitative fatigue degree exactly is quite difficult to be secured. 9 professional bus drivers were participated. We analyzed the quantitative legs' muscular strength when operating each pedal. And then we also analyzed the muscular strength and muscular fatigue degree according to driving pattern during bus driving. Therefore, we suggested the most efficient driving posture.