@PHDTHESIS{ 2018:1226059451,
 	title = {Self-adaptive QOS at communication and computation levels for many-core system-on-chip},
 	year = {2018},
 	url = "http://tede2.pucrs.br/tede2/handle/tede/7946",
 	abstract = "Many-core systems-on-chip are the state-of-the-art in processing power, reaching from a dozen to thousands of processing elements (PE) in a single integrated circuit. General purpose many-cores assume a dynamic application admission, where the application set is unknown at design-time and applications may start their execution at any moment, inducing interference between them. Some applications may have real-time constraints to fulfill, requiring levels of quality of service (QoS) from the system. Due to the high degree of resource’s utilization unpredictability and the number of components to manage, self-adaptive properties become fundamental to support QoS at run-time. The literature provides several self-adaptive QoS proposals, targeting either communication (e.g., Network-on-Chip) or computation resources (e.g., CPU). However, to offer a complete QoS support, it is fundamental to provide a comprehensive self-awareness of the system’s resources, assuming adaptive techniques enabling to act simultaneously at the communication and computation levels to meet the applications' constraints. To cope with these requirements, this Thesis proposes a self-adaptive QoS infrastructure and management techniques, covering both the computation and communication levels. At the computation level, the QoS-driven infrastructure comprises a dynamic real-time task scheduler and a low overhead task migration protocol. These techniques ensure computation QoS by managing the CPU utilization and allocation. The novelty of the task scheduler is the support for dynamic real time constraints, which leverage more flexibility to tasks to explore the CPU according to a variable workload. The novelty of the task migration protocol is its low execution time overhead compared to the state-of-the-art. At the communication level, the proposed technique is a Circuit-Switching (CS) approach based on the Software Defined Networking (SDN) paradigm. The SDN paradigm for NoCs is an innovation of this Thesis and is achieved through a generic software and hardware architecture. For communication QoS, SDN is used to define CS paths at run-time. A self-adaptive QoS management following the ODA (Observe Decide Act) paradigm controls these QoS-driven infrastructures in an integrated way, implementing a closed loop for run time adaptations. The QoS management is self-aware of the system and running applications and can decide to take adaptations at computation or communication levels based on the task feedbacks, environment monitoring, and QoS fulfillment monitoring. The self-adaptation decides reactively as well as proactively. An online application profile learning technique is proposed to trace the behavior of the RT tasks and enabling the proactive actions. Results show that the proposed self-adaptive QoS management can restore the QoS level for the applications with a low overhead over the applications execution time. A broad evaluation, using known benchmarks, shows that even under severe QoS disturbances at computation and communication levels, the execution time of the application is restored near to the optimal scenario, mitigating 99.5% of deadline misses.",
 	publisher = {Pontifícia Universidade Católica do Rio Grande do Sul},
 	scholl = {Programa de Pós-Graduação em Ciência da Computação},
 	note = {Escola Politécnica}
}