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李琳出访美国的出访报告

发布时间:2017-06-20     字体:[增加 减小]

     李琳老师作为合作者的论文“Heuristic Attribute Reduction and Resource Saving Algorithm using Spark”(基于Spark 的启发式属性约减和资源节约算法研究)被ICDCS2017录用并应邀做口头报告。

 

李琳老师于201764日-2017610日出席在美国亚特兰大市的ICDCS2017会议(分布式计算机系统国际学术会议)。 ICDCS是中国计算机学会推荐的B类会议(CCF B)。会议每年在世界不同地方召开,与会者讨论分布式计算系统和应用领域的研究和发展。议题包括大数据系统、分布式数据管理和分析、云计算与数据中心系统、分布式操作系统和中间件、边缘计算和雾计算等多个前沿话题。

 

论文摘要如下:

Abstract

Energy data, which consists of energy consumption statistics and other related data in green data centers, grows dramatically. The energy data has great value, but many attributes within it are redundant and unnecessary. Thus attribute reduction for the energy data has been conceived as a critical step. However, many existing attribute reduction algorithms are often computationally time-consuming. T o address these issues, firstly, we extend the methodology of rough sets to construct data center energy consumption knowledge representation system. Energy data will occur some degree of exceptions cause by power failure, energy instability or other factors, hence we design an integrated data preprocessing method using Spark for energy data, which mainly includes sampling analysis, data classification, missing data, outlier data prediction and data discretization. By taking good advantage of in-Memory Computing, an attribute reduction algorithm for energy data using Spark is proposed. In this algorithm, we use a heuristic formula for measuring the significance of attribute to reduce search space, and an efficient algorithm for simplifying energy consumption decision table, which further improve the computation efficiency. The experimental results show the speed of our algorithm gains up to 1.8X performance improvement over the traditional attribute reduction algorithm using Hadoop, and 0.28X performance improvement over the algorithm using Spark. Besides, our algorithm also saves much computation resources

                                                                       计算机科学与技术学院

                                                                         2017年6月20日                                                

                                            

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