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

移动系统无处不在，并且已经适应了世界各地不同的计算需求。智能移动系统，具有先进的计算和连接，承诺最高级的体验，但电池的限制限制了它们的使用。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，而且人体皮肤只能忍受适度温暖的手机。移动系统的使用受到用户环境的高度影响，因此可以为主动而不是被动的设备电源、热和可靠性管理提供关键输入。即使只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之多。然而，尽管移动系统中已经有许多传感器可用，但还没有一种简单而灵活的方法将上下文纳入移动系统资源管理。为了使移动系统具有更强的环境感知能力，该项目正在设计一种名为ENACT的环境感知控制框架，该框架利用总体传感器数据进行全系统的环境感知管理。ENACT是一个轻量级框架，它使移动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件。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健壮地派生语义信息。然后，通过观察系统在环境和执行器方面的行为，计算随机模型。基于随机模型预测控制的环境感知控制服务，可以智能地设置系统的执行器，在满足可靠性、温度和性能限制的同时，最大限度地降低能耗。为了有效地满足这些需求，控制框架由在不同时间尺度上工作的层次结构支持。ENACT可以有效地将大量原始传感器数据转换为可在系统范围内访问的可用上下文信息，并利用上下文配置一组执行器，以改进移动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结合起来。该项目包括实施新的环境感知技术，用于能源、热量和可靠性管理，可以在不牺牲能源边际的情况下智能地适应动态性能要求。参与该项目的学生在机器学习、分层控制、优化和资源管理等学科方面获得了宝贵的培训。学术界和工业界都可以利用ENACT框架来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。

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.