Adaptive Data Systems

数据（管理）系统已成为服务来自科学、商业和卫生等许多领域应用程序的事务请求的关键部分。在事务请求和负载峰值方面，数据系统通常受到高度可变的工作负载的影响。例如，电子商务网站在假日购物销售期间承受着巨大且多变的负载，而特定商品的突然流行可能会使处理这些请求的数据系统不堪重负。配置更多资源以提高系统性能是一个耗时的过程，所花费的时间远远超过系统负载可以增加的时间范围。为了在面对意想不到的工作负载变化时提供良好的性能，数据系统自动调整其行为以满足客户的可变工作负载需求是至关重要的。为了通过自适应提供良好的性能，数据系统需要了解它们所执行的工作负载的特征。本研究计划旨在解决这些挑战。将开发和实现动态数据复制体系结构，以允许灵活、动态地分配资源，而不会产生数据移动的成本。对于联机事务处理（OLTP）应用程序，这种设计结合了识别服务器或站点的算法，这些服务器或站点可以在没有分布式协调的情况下执行事务，同时拥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，同时确保在该站点上提供足够的资源。这种适应将支持动态控制数据，同时在整个系统中保持均匀的负载分布。这个建议提倡需要理解执行数据系统工作负载的系统的行为模式。此任务需要收集和分析工作负载请求及其行为的系统资源执行数据。目标是了解系统做出特定算法和资源使用决策的条件，并了解它们对系统性能的影响，这将允许对系统配置进行优化，以便为工作负载提供最佳性能。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，这些配置在大多数实际数据系统部署中大量存在，并且需要昂贵且广泛的手动调优工作。

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.