Resource management strategies for many-core systems need to enable sharing of resources such as power, processing cores, and memory bandwidth while coordinating the priority and signiicance of system-and application-level objectives at runtime in a scalable and robust manner. State-of-the-art approaches use heuristics or machine learning for resource management, but unfortunately lack formalism in providing robustness against unexpected corner cases. While recent eforts deploy classical control-theoretic approaches with some guarantees and formalism, they lack scalability and autonomy to meet changing runtime goals. We present SPECTR, a new resource management approach for many-core systems that leverages formal supervisory control theory (SCT) to combine the strengths of classical control theory with state-of-the-art heuristic approaches to eiciently meet changing runtime goals. SPECTR is a scalable and robust control architecture and a systematic design low for hierarchical control of many-core systems. SPECTR leverages SCT techniques such as gain scheduling to allow autonomy for individual controllers. It facilitates automatic synthesis of the high-level supervisory controller and its property veriication. We implement SPECTR on an Exynos platform containing ARM's big.LITTLE-based heterogeneous multi-processor (HMP) and demonstrate that SPECTR's use of SCT is key to managing multiple interacting resources (e.g., chip power and processing cores) in the presence of competing objectives (e.g., satisfying QoS vs. power capping). The principles of SPECTR are easily applicable to any resource type and objective as long as the management problem can be modeled using dynamical systems theory (e.g., diference equations), discrete-event dynamic systems, or fuzzy dynamics. ACM Reference Format:
SPECTR REF is a new management approach for many-core processors that leverages formal supervisory control theory to decompose complex control problems into sub-problems that are solved by local controllers.
SPECTR: Formal Supervisory Control and Coordination for Many-core Systems Resource Management
ASPLOS '18
Computer Science