Cloud computing environments facilitate applications by providing virtualised resources through the network and serve the clients by the pay‐as‐you‐go mechanism. It is based on the rapid development of the network. Normally, economic cost is the most important factor of providing cloud services. However, under some special conditions, the objectives may change. In this scenario, it is impractical to reset the scheduling mechanism only for an occasional incident. So an adaptive scheduling mechanism is needed to address the issues of scheduling under different conditions. To the best of our knowledge, no works have well solved this problem. In this paper, we convert the problem to a multi‐objective scheduling problem with varying objective weights and propose a two‐phase algorithm, which is called adaptive priority‐based workflow scheduling (DRAWS) algorithm. The algorithm will self regulate the priorities of tasks to adapt to different objectives. In the experimental part, simulation environments are set up and three classic workflows are used to evaluate the performance. Four comparing algorithms of resource sensitive scheduling algorithm, Best Fit‐millions of instructions per second, Best Fit‐random‐access memory and Best Fit‐Bandwidth are used to evaluate the performance of DRAWS. It is demonstrated that each phase of our algorithm would optimise the scheduling. Furthermore, we obtain the conclusion that DRAWS algorithm shows superior than the comparing algorithms. Copyright © 2015 John Wiley & Sons, Ltd.

No abstract

No abstract

Space-based Earth observation is now developing rapidly because of the advantages in coverage, convenience, and flexibility. However, it is difficult for one single satellite to realize the quick observation of wide areas. In order to catch all the significant information of a wide area in a short time, multi-satellite observation mission would be proposed. In this article, a mission planning method for online multi-satellite wide area observation is established to serve future multi-satellite observation missions. Firstly, a method for area division is proposed, and the whole area is divided into subareas. Then the multi-satellite observation path planning is realized by a strategy of path deduction. After that, a remaining time allocation method to maximize the observation gazing time of each subarea is proposed. Finally, the algorithms for the whole mission planning process are provided. Numerical simulations show that the mission planning method is able to ensure the complete coverage of different wide target areas, with high reliability and low computational complexity.

With the development of mobile telecommunication technology, mobile phones have become a necessary tool in daily life and provided us many conveniences. Meanwhile, the huge number of cell phones constitute a potential high performance data processing system, called mobile cloud computing, to strengthen capacity for individual devices. Many researchers have studied about the architectures and scheduling algorithms of mobile cloud computing. However, little work has been performed about how to schedule mobile application tasks in data centers to extend battery life for mobile terminals. To address this issue, we investigate agent models, mobile energy consumption models and data transmission models under different connection environments. Based on which, we propose a novel terminal energy efficient scheduling method (AGILE for short). AGILE compares energy consumption in cloud execution and mobile execution according to the actual wireless environment, then makes energy-efficient decisions. Extensive experiments are conducted to evaluate the performance of the AGILE under different wireless channels, and the performance impact on different parameters are studied. The experimental results indicate that the proposed method can save mobile devices' energy effectively.