Scalable Parallel and Distributed Expert Database Systems

可扩展的并行分布式专家数据库系统

• 批准号: 9313847
• 负责人: Salvatore Stolfo
• 金额: $ 8万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-01 至 1996-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9313847&HistoricalAwards=false
• 关键词: Scalable; Parallel; Distributed; Expert; Database

9313847 Stolfo We study solutions to the performance bottleneck in expert databases using the PARADISER 10 distributed rule processing environment as our testbed. We purpose an approach that combines statically computed restrictions on the rules of a rule program for partitioning the workload of rule evaluation among an arbitrary number of rule program replicas evaluated at distinct processing sites, and dynamic load balancing protocols that update and reorganize the distribution of workload by modifying the restrictions at runtime. (Rules are not redistributed.) Our techniques utilize one form of "meta-data", namely, statistics gathered on the attribute values and size of relations of the underlying database (e.g., number of distinct values of an attribute, number of tuples in a relation etc.) in the algorithms that compute the restrictions on the rules. We analyze the dynamic load balancing protocols in terms of efficiency and scalability criteria, which provide guidance on the expected behavior of the protocols with increasing database size. The proposed research falls into three main categories: (a) workload distribution paradigms among processing sites to ameliorate the performance degradation resulting from scaling up of database size, (b) a suite of load balancing protocols for dynamic "logical" reorganization of the underlying database, and their scaling characteristics, and , (c) experimental validation in the PARADISER expert database environment, using a suite of benchmark rule programs. A fully operational and transferable rule processing environment will be constructed that embodies these results.

9313847 Stolfo我们研究的解决方案，在专家数据库中使用PARADISER 10分布式规则处理环境作为我们的测试平台的性能瓶颈。 我们的目的是一种方法，结合了静态计算的限制规则程序的规则，用于分区的规则评估的工作量之间的任意数量的规则程序副本在不同的处理站点进行评估，和动态负载平衡协议，更新和重组的工作量分布，通过修改在运行时的限制。 （规则不会重新分配。） 我们的技术利用一种形式的“元数据”，即，对底层数据库的关系的属性值和大小收集的统计数据（例如，属性的不同值的数量，关系中元组的数量等）在计算规则限制的算法中。 我们分析了动态负载平衡协议的效率和可扩展性的标准，提供指导的预期行为的协议，随着数据库的大小。 拟议的研究福尔斯分为三大类：（a）在处理站点之间的工作负载分布范例，以改善由于数据库大小的按比例增加而导致的性能下降，（B）用于底层数据库的动态“逻辑”重组的一套负载平衡协议，以及它们的按比例缩放特性，以及（c）在PARADISER专家数据库环境中的实验验证，使用一套基准规则程序。 一个完全可操作的和可转让的规则处理环境将构建体现这些结果。