Adaptive Resource Management for Mobile Multiprocessors through Computational Self-Awareness
Abstract
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 thesis, I 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 will reason about the actions it takes by considering the significance of competing objectives, user requirements, and operating conditions while executing unpredictable workloads.