Adaptive Data Systems

数据(管理)系统已成为满足科学、商业和卫生等许多领域应用程序的事务请求的关键部分。就事务性请求和负载高峰而言，数据系统通常承受高度可变的工作负载。例如，电子商务网站在假日购物期间承受着沉重和可变的负载，而特定商品的突然流行可能会淹没服务于这些请求的数据系统。调配更多资源以提高系统性能是一个耗时的过程，所需时间比系统负载可能增加的时间跨度长得多。面对意外的工作负载变化，要提供良好的性能，数据系统必须自动调整其行为，以满足客户的不同工作负载需求。要通过自适应提供良好的性能，数据系统需要了解其执行的工作负载的特征。这项研究计划提出要解决这些挑战。将开发和实施动态数据复制架构，以便在不产生数据移动成本的情况下灵活、动态地分配资源。对于在线事务处理(OLTP)应用程序，此设计结合了识别服务器或站点的算法，在这些服务器或站点中，事务可以在没有分布式协调的情况下执行，同时具有足够的资源来快速执行。这里的挑战是确定哪些站点可以为特定事务提供更好的性能，同时确保在该站点提供足够的资源。这种适应将支持数据的动态主控，同时在整个系统中保持均匀的负载分布。该提案主张需要了解执行数据系统工作负载的系统的行为模式。这项任务需要收集和分析工作量请求及其行为的系统资源执行数据。目标是了解系统作出特定算法和资源使用决定的条件，并了解它们对系统性能的影响，这将使系统配置得到优化，从而为工作负载提供尽可能好的性能。实现这一目标将使系统能够提供优于其未优化配置的性能，未优化配置在大多数真实世界的数据系统部署中随处可见，并且需要昂贵且广泛的手动调整工作。

Data (management) systems have become a critical part of serving transactional requests from applications in many sectors such as science, business and health. Data systems are typically subjected to highly variable workloads in terms of transactional requests and load spikes. For example, e-commerce sites are subject to heavy and variable loads during holiday shopping sales while the sudden popularity of particular items can overwhelm the data system serving these requests. Provisioning more resources to boost system performance is a time-consuming process and takes much longer than the timespan over which the system load can increase. To deliver good performance in the face of unanticipated workload changes, it is crucial that data systems automatically adapt their behaviour to meet the variable workload demands of their clients.  To deliver good performance through adaptivity, data systems need to understand the characteristics of the workloads that they execute. This research program proposes to address these challenges. A dynamic data replication architecture will be developed and implemented to allow flexible, dynamic, allocation of resources without incurring the cost of data movement. For online transaction processing (OLTP) applications, this design incorporates algorithms that identify servers or sites where transactions can execute without distributed coordination while having enough resources to execute quickly. The challenge here is to identify which sites can offer better performance to particular transactions while ensuring that adequate resources are provisioned at that site. This adaptation will support dynamic mastership of data while maintaining even load distribution throughout the system. This proposal advocates the need to understand the behavioural patterns of systems that execute data system workloads. This task entails the collection and analysis of system resource execution data for workload requests and their behaviour. The objective is to understand the conditions under which a system made particular algorithmic and resource usage decisions and to understand their effect on system performance, which would allow the configuration of the system to be optimized to deliver the best possible performance for workloads. Achieving this goal would enable the system to deliver superior performance over its un-optimized configurations that abound in most real-world data system deployments and require expensive, and extensive, manual tuning effort.