With the rapid development of cloud computing and container technology, more and more applications are deployed to the cloud, and the scale of cloud platform is expanding. Due to the large number of container instances running in the platform, complex dependency relationship, fast version iteration and other characteristics, the update of business can often cause the change of the whole cloud resource environment, which triggers the repetitive scheduling problem of related tasks and affects stability of the business. In this paper, we propose a self-adapting task scheduling algorithm (ADATSA) using learning automata to solve these problems. Firstly, we design a learning automata model and objective function for the system on task scheduling problem. Then, we realize an effective reward-penalty mechanism for scheduling actions in combination with the idle state of resources and the running state of tasks in the current environment. Meanwhile, the environment is modeled by cluster, node and task, and the probability of action selected is optimized by scheduling execution, thus enhancing the adaptability to the cloud environment of the scheduling and accelerating convergence. Finally, we construct a framework of task load monitoring with buffer queue to achieve dynamic scheduling based on priority. The experimental part verifies the effectiveness of proposed algorithm with different angles such as resource imbalance degree, resource residual degree and QoS. Compared with other learning automata scheduling models such as LAEAS, non-automata technology based algorithms such as PSOS and K8S scheduling engine, ADATSA shows the better performance of environment adaptability, resource optimization efficiency and QoS in dynamic scheduling. The theoretical analysis was consistent with the experimental results.

Chagan Lake is located downstream of the Second Songhua River basin in Northeast China. It is one of the top ten inland freshwater lakes, and an important aquatic farm in China. The lake has been receiving large amounts (currently at 1.5 × 10(8) m(3)/a) of water from the river since 1984. This would pose a threat to the aquatic system of the lake because the river was seriously polluted with mercury in 1970s-1980s. The current study is the first to report the total mercury concentrations in fish found in the lake. Mercury concentrations in seven fish species collected from the lake in January 2009 were determined. The related human health risk from fish consumption was also assessed. The average concentration of mercury in the fish was 18.8 μg/kg of wet weight, ranging from 4.5 to 37.6 μg/kg of wet weight. A large difference in the mercury concentrations among the fish species was found. The mercury concentration was found to be higher in carnivorous species and lower in omnivorous and herbivorous species. This demonstrates greater mercury bioaccumulation in fish species at higher trophic levels. Mercury concentrations in fish showed significant positive correlations with age, length, and weight. No significant relationship was found between mercury concentrations in fish and the habitat preferences. Mercury concentrations in fish from the lake were within the limits of the international and national standards of China established for mercury. According to the reference doses established by the United States Environmental Protection Agency, the maximum safe consuming quantity considering all the fish was 297.3 g/day/person, which was more than five times as much as the current quantity (50 g/day/person) consumed by the local residents. This investigation indicates that the historical pollution of the Second Songhua River has not caused mercury bioaccumulation in fish muscle tissue of Chagan Lake. The present consumption of fish from the lake in the local area does not pose a threat to human health.

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