Many-Core Systems-on-Chip increasingly require Dynamic Multi-objective Management (DMOM) of resources. DMOM uses different management components for objectives and resources to implement comprehensive and self-adaptive system resource management. DMOMs are challenging because they require a scalable and well-organized framework to make each component modular, allowing it to be instantiated or redesigned with a limited impact on other components. This work evaluates two state-of-the-art distributed management paradigms and, motivated by their drawbacks, proposes a new one called Management Application (MA) , along with a DMOM framework based on MA. MA is a distributed application, specific for management, where each task implements a management role. This paradigm favors scalability and modularity because the management design assumes different and parallel modules, decoupled from the OS. An experiment with a task mapping case study shows that MA reduces the overhead of management resources (-61.5%), latency (-66%), and communication volume (-96%) compared to state-of-the-art per-application management. Compared to cluster-based management (CBM) implemented directly as part of the OS, MA is similar in resources and communication volume, increasing only the mapping latency (+16%). Results targeting a complete DMOM control loop addressing up to three different objectives show the scalability regarding system size and adaptation frequency compared to CBM, presenting an overall management latency reduction of 17.2% and an overall monitoring messages’ latency reduction of 90.2%.

The workload of many-core systems includes real-time (RT) applications. Obtain energy savings while executing RT applications is a challenge due to the RT timing constraints. The techniques used to reduce the consumption, as dynamic voltage and frequency scaling (DVFS), usually delays the applications, leading to constraint violations. Most works in the literature ensure RT constraints based on design-time analysis of the expected workload by defining the voltage and frequency levels. Our main contribution is the proposal of a Runtime Energy Management for RT applications (RT-REM), using monitored data, and DVFS and task mapping as actuation policies. Supervising the application slack time enables to save energy of RT applications. Evaluations on many-core systems (up to 144 PEs), achieves 18% in energy savings, keeping constraint misses below 2.5%. Our proposal stands out from related works regarding scalability, realistic and accurate energy estimation, and absence of design-time analysis of the application set.