Cloud Computing has emerged as a powerful and promising way for running high performance computing (HPC) jobs. Most HPC jobs are designed under multi-processes paradigm and involve frequent communication and synchronization among parallel processes. However, as the underlying resources of cloud data centers are always shared among multiple tenants, the competition of jobs for limited bandwidth resources lead to unpredictable completion times for jobs in the cloud, which may lead to QoS violation and inefficient utilization of resources when scheduling parallel jobs in the cloud. To tackle the issue, it is essential to provide bandwidth guarantees for parallel jobs running in the cloud. Offering a dedicated virtual cluster (VC) for running applications in the cloud is a popular way to guarantee bandwidth demands. Motivated by these problems, in this paper, we firstly design a time-aware virtual cluster (TVC) request model for parallel jobs and consider how to embed requested TVCs of jobs into cloud efficiently under parallel job scheduling framework. An adaptive bandwidth-aware heuristic algorithm, which is denoted as AdaBa, is proposed to improve the job accept rate by adjusting the priorities of servers to accommodate the VMs of TVC adaptively according to the relative size of requested bandwidth demand. Then, a bandwidth-guaranteed migration and backfilling scheduling algorithm, which is denoted as BgMBF, is designed to schedule parallel jobs and the bandwidth demands are guaranteed by AdaBa. To obtain high job responsiveness performance, a bandwidth-reserved job backfilling strategy is designed when the requested TVC for current scheduled job cannot be allocated in the cloud. The migration cost of BgMBF is also considered and an enhanced version BgMBFSDF is then proposed to minimize the number of migration when the execution time of jobs are known. Through extensive simulation experiments on popular parallel workloads, our proposed TVC embedding algorithm AdaBa achieves up to 15 percent of improvement on accept rate compared with existing algorithms such as Oktupus and greedy algorithm. Our proposed BgMBF and BgMBFSDF also significantly outperform other popular scheduling algorithms integrated with AdaBa on average response time and average bounded slow down.
Copper mine wastewater is a rather tough environmental issue. An efficient and effective method is still expected. Ozone advanced oxidation technology is applied to treat copper mine wastewater in this paper. A systematical investigation is performed and the degradation mechanism is revealed. Experimental results show ozone can effectively degrade COD meanwhile all pollutants indexes can meet the strict emission requirement. There are two interesting discoveries in copper mine wastewater treatment process by ozone advanced oxidation. One is COD change with ozone aeration time. COD rapidly decreases initially, followed by a small recovery, but then decreases slightly. The dramatical decrease in the first several minutes results from the oxidation of residual flotation reagents, humic acid-like and soluble microbial product-like. The small recovery is because these substances of protein-like and humic acid-like are oxidized to small molecules. The last slight decrease is due to the further oxidation of these small molecules. Another is ammonia nitrogen change in fluctuations with ozone aeration time. The oxidation CwHxNyOz in residual flotation reagent Xanthate leads the first ammonia nitrogen increase and the following first decrease, respectively. It is followed by another increase and decrease. The second increase is owing to these relatively stable nitrogenous organic matters of protein-like and fulvic acid-like are oxidized to ammonia nitrogen. The last reduce results from the further oxidation of ammonia nitrogen. In a word, ozone advanced oxidation is a promising technology for copper mine wastewater treatment.
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