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RISS 인기검색어
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- 저자 : Tianshu Wu (검색결과 4 건)
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An Efficient Job Scheduling for MapReduce Clusters
Jun Liu,Tianshu Wu,Ming Wei Lin,Shuyu Chen 보안공학연구지원센터 2015 International Journal of Future Generation Communi Vol.8 No.2
The job scheduling for Map Reduce clusters has received significant attention in recent years, because it plays an important role on Map Reduce clusters. Traditional job scheduling performs poorly in assigning a task to appropriate nodes, and can not predict the resource utilization of the unexecuted tasks. To address the problems, an efficient job scheduling for Map Reduce clusters is proposed in this paper. The job scheduling introduces dynamic priority scheduling and real-time prediction model. Dynamic priority scheduling introduces the minimum cost data locality algorithm with a weight to deal with different size jobs, and real-time prediction model can predict the resource utilization of unexecuted tasks by calculating the running tasks. The resource utilization contains CPU, memory, and network. Experimental results prove that the proposed job scheduling is able to perform well in Map Reduce clusters.
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The vertical distribution and temporal occurrence of three types of rice planthoppers in Shanghai
Wang Dongsheng,Wu Xiangwen,Yuan Yongda,Zhang Tianshu,Shen Huimei,Du Xingbin,Teng Haiyuan,Chang Xiaoli 한국곤충학회 2022 Entomological Research Vol.52 No.1
In this study, we investigated the dynamic occurrence and vertical distribution of three types of rice planthoppers (Nilaparvata lugens, Sogatella furcifera,and Laodelphax striatellus) in Shanghai, China. Our results showed that S. furcifera and L. striatellus infested the lower part of rice plants in the early development stage, S. furcifera and L. striatellus inhabited every part of rice in the middle development stage, and L. striatellus and N. lugens formicated in the upper part of the rice plant in the posterior development stage. The populations of rice planthoppers were larger in July and September, Where more than 600 adult and nymphal planthoppers, per a hundred hills of rice, were found in late July, and out of the 600, the majority were the nymphs of S. furcifera and L. striatellus. S. furcifera was mainly found from July to October, L. striatellus during the rice development, and N. lugens after September, during which their maximal individual number, per a hundred hills of rice, was 480, 220, less than 50, respectively. In addition, our results showed that adult rice planthoppers were mostly observed from late August to mid-October with the highest population being found during mid to late September. The adult S. furcifera, L. striatellus,andN. lugens were mostly observed in August and September, from August to October, and past mid-September, respectively. Moreover, the number of adult L. striatellus that were sticking to yellow sticky card was significantly higher than that of S. furcifera and N. lugens.
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An Improved Event Scenario Correlation Method for Multi-Source Security Log
Qianyun Wang,Shuyu Chen,Hancui Zhang,Tianshu Wu 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.2
Developing computer technologies and a network of persistently growing size put massive hosts and transmission devices in a vast network at increasingly higher risks. Log information of various devices can facilitate the detection of intrusion and attacks. Log information from a single data source is, however, with limitations. The analysis results cannot precisely reflect the current network situation if log information in a single data source is analyzed without correlation to analysis of log information from different data sources. To better demonstrate network situation, this paper proposes an improved event scenario correlation method for multi-source log analysis via researching on numerous existing data fusion methods and event correlation methods as well as integration of conventional event scenario correlation (ESC) method with fuzzy reasoning. Experimental results prove that the proposed method significantly reduces the False Positive rate (FP rate) and False Negative rate (FN rate) of security logs.
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