通过计算自感知实现移动系统的智能管理
Intelligent Management of Mobile Systems through Computational Self-Awareness
摘要：
众核系统的可扩展资源管理越来越复杂。复杂性可能是由于需求冲突的不同工作负载特性，或有限的共享资源（如内存带宽和电源）。众核系统的资源管理策略必须在工作负载之间适当地分配共享资源，同时以可伸缩和健壮的方式在运行时协调高级系统目标。为了解决众核系统中的动态资源管理的复杂性，已经提出了使用逻辑学的最新技术。这些方法在提供针对意外运行时行为的健壮性方面缺乏形式主义。这个问题的一个常见的解决方案是部署经典的控制方法与边界和正式的保证。传统的控制理论方法缺乏适应（1）在运行时改变目标（即，自适应性），以及（2）改变建模系统的动态（即，自我优化）。在本章中，我们将探讨自适应资源管理技术，通过采用计算自我意识的原则，特别是反射，提供自优化和自适应性。通过支持这些自我意识属性，系统可以在执行不可预测的工作负载时，通过考虑竞争目标、用户需求和操作条件的重要性来推理它所采取的操作。
英文摘要：
Runtime resource management for many-core systems is increasingly complex. The complexity can be due to diverse workload characteristics with conflicting demands, or limited shared resources such as memory bandwidth and power. Resource management strategies for many-core systems must distribute shared resource(s) appropriately across workloads, while coordinating the high-level system goals at runtime in a scalable and robust manner. To address the complexity of dynamic resource management in many-core systems, state-of-the-art techniques that use heuristics have been proposed. These methods lack the formalism in providing robustness against unexpected runtime behavior. One of the common solutions for this problem is to deploy classical control approaches with bounds and formal guarantees. Traditional control theoretic methods lack the ability to adapt to (1) changing goals at runtime (i.e., self-adaptivity), and (2) changing dynamics of the modeled system (i.e., self-optimization). In this chapter, we explore adaptive resource management techniques that provide self-optimization and self-adaptivity by employing principles of computational self-awareness, specifically reflection. By supporting these self-awareness properties, the system can reason about the actions it takes by considering the significance of competing objectives, user requirements, and operating conditions while executing unpredictable workloads.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
