Adaptive Data Systems

自适应数据系统

• 批准号: RGPIN-2019-05630
• 负责人: Daudjee, Khuzaima
• 金额: $ 3.5万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738413
• 关键词: Adaptive; Data; Systems

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.

数据（管理）系统已成为服务科学、商业和健康等许多领域应用程序事务请求的关键部分。数据系统通常要承受事务请求和负载峰值方面高度可变的工作负载。例如，电子商务网站在假日购物销售期间会承受沉重且可变的负载，而特定商品的突然流行可能会压垮服务这些请求的数据系统。调配更多资源来提高系统性能是一个耗时的过程，并且比系统负载增加的时间长得多。为了在面对意外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不断变化的工作负载需求至关重要。  为了通过适应性提供良好的性能，数据系统需要了解它们所执行的工作负载的特征。该研究计划旨在解决这些挑战。将开发和实施动态数据复制架构，以允许灵活、动态的资源分配，而不会产生数据移动成本。对于在线事务处理 (OLTP) 应用程序，此设计采用了一些算法，可识别无需分布式协调即可执行事务的服务器或站点，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配置足够的资源。这种调整将支持数据的动态控制，同时保持整个系统的负载分布均匀。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。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，这将允许优化系统配置，为工作负载提供尽可能最佳的性能。实现这一目标将使系统能够比未优化的配置提供卓越的性能，而未优化的配置在大多数实际数据系统部署中都大量存在，并且需要昂贵且广泛的手动调整工作。