Enjoy scrum developing method Enjoy learning new technology. Very Self-Motivated . Know well: Ruby on Rails Specialties: 5 years experience on Java, 5 years experience on database

Web Developer @ From July 2015 to Present (6 months) Greater Seattle AreaSoftware Engineer II @ Working collaboratively with both the development team and members of the whole organization to invent, design, develop, and test solutions that help insurance agents serve people. Creating software solutions that are easy to use, test, extend, and maintain. Focusing on developing a quoting solution that helps agents quote and bind faster, using Java, ruby on rails, html, css. From July 2013 to Present (2 years 6 months) Lincoln, Nebraska AreaWeb and Database Developer @ Used Extjs(Front End), REST and Play!(Server Side) software frameworks to develop and maintain a website. Developed and maintained Access and MS SQL Server database. From July 2012 to June 2013 (1 year) Graduate Research Assistant @ MapReduce is a framework for processing huge amounts of data in a distributed environment and Hadoop is Apache’s open source implementation of MapReduce, which is widely used. However, Hadoop’s performance is currently limited by its default task scheduler, which assumes that cluster nodes are homogeneous when estimating the task progress and choosing slow tasks for re-execution. In practice, the homogeneity assumption does not always hold. Longest Approximate Time to End (LATE) is a scheduling algorithm that takes heterogeneity into account. It, however, still depends on a static method to estimate the task execution time. As a result, neither Hadoop default nor LATE schedulers perform very well. Self-adaptive MapReduce Scheduling Algorithm (SAMR) is more advantageous than LATE. It uses historical information on each node to adjust the stage weights of map and reduce tasks when estimating the task execution time. The limitation of SAMR is that it only considers node types but not any other factors, such as job types and dataset sizes, which also affect stage weights. In this thesis, we propose ESAMR: an Enhanced Self-Adaptive MapReduce scheduling algorithm to improve the speculative re-execution of slow tasks in MapReduce. In ESAMR, in order to identify slow tasks accurately, we differentiate historical stage weights information on each node and divide them into K clusters using a K-means clustering algorithm; and when executing a job’s tasks on a node, ESAMR classifies the tasks into one of the clusters and uses the cluster’s weights to estimate the execution time of the job’s tasks on the node. Experimental results show that among the aforementioned algorithms, ESAMR leads to the smallest error in task execution time estimation and identifies slow tasks most accurately. From January 2011 to March 2012 (1 year 3 months) Graduate Research Assistant @ Worked on a software for multidimensional chromatography using Java, including implementation of some algorithms to help classification of chemicals From January 2010 to December 2010 (1 year) SAP HR Implementation Manager @ Involved in the implementation of SAP HR, including SAP HR module configuration, user training, data template training, data import and checking, and function testing. Software design by Java. From June 2007 to June 2008 (1 year 1 month) System Implementation Master and System administrator @ SAP HR Implementation, Oracle database system implementation and Web developer From June 2006 to June 2008 (2 years 1 month) Application developer and System administrator @ Developed Oracle ERP BW system using PL/SQL. Web developer using Java, JSP, HTML Software developer using Java, design a software to handle more than 1,000,000 data recodes. From June 2005 to June 2008 (3 years 1 month)

Master of Science, Computer Science @ University of Nebraska-Lincoln From 2009 to 2012 Bachelor of Engineering, Electronic Engineering @ North China University of Technology From 2001 to 2005 Xiaoyu Sun is skilled in: Java, Databases, Algorithms, Ruby on Rails, JavaScript, PL/SQL, Shell, HTML, MySQL, Perl, REST, Microsoft SQL Server, Integration, Ruby, Java IText, Github