CSR: Small: Timely Power-Aware Data Management in Embedded Systems

在智能汽车、微电网和国土安全等新兴的嵌入式和网络物理应用中，数据量正在快速增长。数据库支持在数据密集型嵌入式应用程序中是必不可少的，因为在没有数据库支持的情况下开发这些应用程序非常困难，而且容易出错。理想情况下，数据库系统需要及时处理实时数据服务请求，例如驾驶路线推荐和电力需求/供应分析，使用代表当前现实世界状态的新数据，例如当前交通或电网状态。考虑到嵌入式系统中严格的功耗限制，数据库应该消耗最小的功耗，这一点也很重要。由于以下几个原因，实现这一愿景是具有挑战性的，包括根据当前现实世界状态随时间变化的动态工作负载、严重的数据/资源争用以及更新临时数据的计算成本。此外，时效性、数据新鲜度和功率效率可能会相互竞争。如果为用户查询提供更高的优先级，则可以以降低数据新鲜度为代价提高查询的时效性，反之亦然。简单地消耗更多的功率来支持所需的时效性和数据新鲜度是不可取的。不幸的是，最先进的数据库系统可能无法提供功率感知的实时数据服务。不知道时间和数据新鲜度要求的非实时数据库在这些应用程序中可能表现不佳。大多数现有的实时和嵌入式数据库不能保证所需的时效性和数据新鲜度。它们也没有功率意识。这可能是一个严重的问题，因为无限制的延迟或数据过期可能导致交通堵塞、停电或国土安全问题。虽然最近开始了主要在数据中心环境中节能数据库的研究，但没有考虑实时截止日期和数据新鲜度要求。尽管电源管理很重要，但主要针对嵌入式应用的实时数据库电源管理方面的相关工作却少得惊人。由于对功率的无知，部署实时数据库可能会变得越来越困难和昂贵。为了弥补这一差距，将在本工作中进行深入研究，以调查新兴嵌入式和网络物理系统中功率感知实时数据服务的有效基本方法，这些方法具有重大的广泛影响。该项目将研究新的方法，以支持所需的时效性和数据新鲜度，即使在存在动态工作负载的情况下，同时大大降低实时嵌入式数据库（rtedb）的功耗。这将涉及探索1)应用自适应控制理论技术，在必要时优雅地适应RTEDB系统行为，以支持闭环系统中所需的时效性和数据新鲜度，同时降低功耗；2)前馈方法，通过考虑RTEDB语义来避免数据/资源争用带来的性能和功率损失；3)多核RTEDB系统架构，其中核心支持所需的时效性和数据新鲜度，从而自主和协作地降低功耗；4)将在测试平台上进行广泛的模拟和实验，使用真实世界的数据跟踪来测量及时性、数据新鲜度以及功率和能源消耗。

In emerging embedded and cyber physical applications like smart cars, micro grids, and homeland security, data amounts are increasing fast. Database support is essential in data-intensive embedded applications, because developing them without database support is hard and error-prone. Ideally, a database system needs to process real-time data service requests, such as driving route recommendations and electricity demand/supply analysis, in a timely manner using fresh data representing the current real-world status, e.g., the present traffic or electric grid status. It is also important the database should consume minimal power considering stringent power constraints in embedded systems. Achieving this vision is challenging for several reasons, including dynamic workloads varying in time depending on the current real world status, severe data/resource contention, and computational costs for updating temporal data. Moreover, the timeliness, data freshness, and power efficiency may compete with each other. If higher priority is given to user queries, their timeliness can be improved at the cost of the decreased data freshness or vice-versa. Simply consuming more power to support the desired timeliness and data freshness is not desirable. Unfortunately, state-of-the-art database systems may fall short of power-aware real-time data services. Non-real-time databases unaware of timing and data freshness requirements may perform poorly in these applications. Most existing real-time and embedded databases can provide no guarantee on the desired timeliness and data freshness. Neither are they power-aware. This can be a serious problem, since the unbounded tardiness or data staleness may result in a traffic jam, power outage, or homeland security problem. Although research on energy-efficient databases mainly in data center contexts has recently begun, real-time deadlines and data freshness requirements are not considered. Despite the importance, related work on power management in real-time databases, chiefly targeting embedded applications, is surprisingly scarce. Due to the power ignorance, deploying real-time databases may become increasingly difficult and costly. To bridge the gap, in-depth research will be performed in this work to investigate effective fundamental approaches for power-aware real-time data services in emerging embedded and cyber physical systems with significant broader impacts. This project will investigate novel methods to support the desired timeliness and data freshness even in the presence of dynamic workloads, while substantially decreasing the power consumption in real-time embedded databases (RTEDBs). This will involve exploring 1) application of adaptive control theoretic techniques to gracefully adapt the RTEDB system behavior, if necessary, to support the desired timeliness and data freshness for less power in the closed-loop system; 2) feedforward methods to avoid data/resource contention incurring performance and power penalties by considering RTEDB semantics; 3) a multicore RTEDB system architecture, in which the cores support the desired timeliness and data freshness, decreasing the power usage both autonomously and collaboratively; and 4) extensive simulations and experiments in a testbed will be performed using real-world data traces to measure the timeliness, data freshness, and power and energy consumption.