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-本科生计划为一些项目提供支持，这些项目提供了解决非裔美国学生在本科水平的计算机科学中代表性严重不足的解决方案。温斯顿·塞勒姆州立大学的这个项目将通过研究云基础设施上的大数据应用程序的资源管理，在计算机系的教职员工和学生中建立研究能力。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，这些经验可能会增强他们在该学科攻读学位的兴趣，从而有助于扩大该学科中代表性不足的群体的参与。项目目标是：在温斯顿-塞勒姆州立大学建立大数据和云计算研究能力；让本科生参与尖端科学研究；并提高计算机科学专业的教育经验。该项目正在探索围绕云基础架构中并发MapReduce工作负载的全球高效和经济高效的资源分配的估计进行的研究，以便配置确保满足各种工作负载的服务级别目标(SLO)，同时将实现这一目标所需的资源降至最低。具体的研究目标是：利用配置文件和预测子系统来描述和模拟MapReduce工作负载的性能，并相应地将它们识别为小作业、周期性作业和延迟作业；调查效用函数中要包含的参数，以最好地捕捉MapReduce工作负载的优先级、截止日期、性能表征和异构性以及底层云基础设施的异构性和动态；设计和开发基于效用的资源管理的算法和启发式算法，该算法和启发式算法利用工作负载特有的特征，并以满足SLO和最大化云利用率为目标进行部署；并使用真实大数据工作负载对提出的方法进行评估，研究其性能、云租赁成本和云资源的整体利用率。

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.