CSR: Small: ENACT: Environment-Aware Management of Mobile Systems

CSR：小型：ENACT：移动系统的环境感知管理

• 批准号: 1527034
• 负责人: Tajana Rosing
• 金额: $ 45万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527034&HistoricalAwards=false
• 关键词: CSR; Small; ENACT; Environment; Aware

Mobile systems are pervasive and have been adapted for diverse computing needs throughout the world. Smart mobile systems, with advanced computing and connectivity, promise superlative experience, yet battery constraints limit their use. Device temperatures are also a major concern as high temperatures reduce hardware reliability but also because human skin can tolerate only moderately warm phones. Mobile system usage is highly influenced by user context, hence can provide a crucial input for proactive, instead of reactive, device power, thermal and reliability management. Even with a small amount of contextual information such as user location and motion, battery lifetime can be improved by as much as a factor of five. Yet, despite the fact that there are already many sensors available in mobile systems, there have been no simple and flexible ways to incorporate context into mobile system resource management. To enable mobile systems to become more context-aware, this project is designing ENACT, an ENvironment Aware ConTrol framework that leverages overarching sensor data for system-wide context-aware management. ENACT is a lightweight framework that enables a mobile system to tap into a vast array of sensors, and leverage comprehensive context about hardware, software, and user, to control system-wide actuators. The framework has two main components. A context recognition service leverages well-established statistical techniques to robustly derive semantic information from raw sensor data. Then, by observing system behavior with respect to context and actuators, it computes stochastic models. A context-aware control service, based on stochastic model predictive control, smartly sets the system's actuators to minimize the energy consumption while meeting reliability, temperature, and performance constraints. To efficiently meet these requirements, the control framework is supported by a hierarchical structure working at different time scales. ENACT can efficiently transforming a large set of raw sensor data into usable contextual information accessible system-wide, and leverage context to configure a set of actuators to improve the way mobile system resources are managed while providing excellent user experience. This allows for accurately aligning system goals to performance expectations. The project includes the implementation of novel context-aware techniques for energy, thermal and reliability management, which can smartly adapt to dynamic performance requirements without sacrificing energy margins. Students involved in this project gain valuable training in disciplines across machine learning, hierarchical control, optimization and resource management. The ENACT framework can be leveraged in academia and industry alike to implement context-awareness in systems, and to design resource management policies that can leverage context.

移动的系统是普遍存在的，并且已经适应于全世界的各种计算需求。智能移动的系统，具有先进的计算和连接，承诺最好的经验，但电池的限制限制他们的使用。设备温度也是一个主要问题，因为高温会降低硬件的可靠性，但也因为人类皮肤只能容忍适度温暖的手机。移动的系统使用受用户环境的影响很大，因此可以为主动而不是被动的设备功率、热量和可靠性管理提供关键输入。即使有少量的上下文信息，如用户位置和运动，电池寿命也可以提高五倍。然而，尽管事实上在移动的系统中已经有许多传感器可用，但是还没有简单且灵活的方式来将上下文结合到移动的系统资源管理中。为了使移动的系统变得更加上下文感知，该项目正在设计ENACT，ENACT是一个环境感知控制框架，它利用全局传感器数据进行系统范围的上下文感知管理。ENACT是一个轻量级框架，它使移动的系统能够接入大量传感器，并利用有关硬件、软件和用户的综合上下文来控制系统范围的执行器。该框架有两个主要组成部分。上下文识别服务利用完善的统计技术从原始传感器数据中稳健地导出语义信息。然后，通过观察系统相对于上下文和执行器的行为，计算随机模型。基于随机模型预测控制的上下文感知控制服务智能地设置系统的执行器，以最大限度地减少能耗，同时满足可靠性，温度和性能约束。为了有效地满足这些要求，控制框架由在不同时间尺度上工作的分层结构支持。ENACT可以有效地将大量的原始传感器数据转换为系统范围内可访问的可用上下文信息，并利用上下文来配置一组致动器，以改善移动的系统资源的管理方式，同时提供出色的用户体验。这允许准确地将系统目标与性能期望对准。该项目包括实施用于能源、热量和可靠性管理的新型上下文感知技术，这些技术可以在不牺牲能源裕度的情况下智能地适应动态性能要求。参与该项目的学生获得了机器学习，分层控制，优化和资源管理等学科的宝贵培训。ENACT框架可以在学术界和工业界利用，以实现系统中的上下文感知，并设计可以利用上下文的资源管理策略。