Improving Research and Education of Big Data and Cloud Computing at Winston-Salem State University

历史上的黑人学院和大学-本科生计划为解决非裔美国学生在本科阶段计算机科学中严重代表性不足的项目提供支持。该项目在温斯顿塞勒姆州立大学将建立教师和学生在计算机科学系的研究能力，通过检查云基础设施上的大数据应用程序的资源管理。这项工作将与杜克大学合作完成。计算机科学专业的本科生将获得研究经验，这可能会提高他们攻读该学科学位的兴趣，从而有助于扩大该学科中代表性不足的群体的参与。该项目的目标是：在温斯顿-塞勒姆州立大学建立大数据和云计算研究能力;让本科生参与前沿科学研究;并加强计算机科学专业的教育经验。该项目正在探索围绕云基础设施中并发MapReduce工作负载的全球高效和成本效益资源分配的估计进行研究，以便配置确保满足各种工作负载的服务水平目标（SLO），同时最大限度地减少实现这一目标所需的资源。具体的研究目标是：利用一个剖析和预测子系统，以表征和建模的MapReduce工作负载的性能，并将它们相应地识别为小作业，周期性作业和延迟作业;为了研究要包括在效用函数中的参数，这些参数最好能够捕获优先级，截止期限，MapReduce工作负载的性能表征和异构性以及底层云基础设施的异构性和动态性;设计和开发基于实用程序的资源管理算法和算法，利用特定于工作负载的特性，并以满足SLO和最大化云利用率为目标进行部署;并使用实际大数据工作负载评估所提出的方法，并研究其性能、云租赁成本和云资源的总体利用率。

The Historically Black Colleges and Universities - Undergraduate Program provides support for projects that offer solutions to the severe underrepresentation of African American students in computer science at the undergraduate level. The project at Winston Salem State University will build research capacity among faculty and students in the Department of Computer Science by examining resource management of big data applications on cloud infrastructure. This work will be done in collaboration with Duke University. Undergraduate computer science majors will gain research experiences that may further their interest in pursuing a degree in the discipline, thereby contributing to broadening the participation of underrepresented groups in the discipline.The project goals are: to build the big data and cloud computing research capacity at Winston-Salem state university; to involve undergraduate students in cutting-edge scientific research; and to enhance educational experiences for computer science majors. The project is exploring research centered around the estimation of a globally efficient and cost effective resource allocation for concurrent MapReduce workloads in a cloud infrastructure so that the configuration ensures meeting service level objectives (SLOs) of various workloads and at the same time minimizing the resources required to achieve this goal. The specific research aims are: to leverage a profile and prediction subsystem in order to characterize and model the performance of a MapReduce workload and identify them accordingly as small jobs, periodic jobs, and delayed jobs; to investigate the parameters to be included in the utility function that best can capture the priority, deadline, performance characterization and heterogeneity of MapReduce workloads and the heterogeneity and dynamics of underlying cloud infrastructure; to design and develop algorithms and heuristics for utility-based resource management that take advantage of the workload-specific characteristics and deploy them with the goal of meeting SLOs and maximizing cloud utilization; and to evaluate the proposed approach with real-life big data workloads and study their performances, cloud rental costs, and the overall utilization of the cloud resources.