Revisiting self-adaptation for efficient decision-making at run-time in parallel executions

Adriano Vogel; Marco Danelutto, Dalvan Griebler, Luiz Gustavo Fernandes

31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2023

Self-adaptation is a potential alternative to provide a higher level of autonomic abstractions and run-time responsiveness in parallel executions. However, the recurrent problem is that self-adaptation is still limited in flexibility and efficiency. For instance, there is a lack of mechanisms to apply adaptation actions and efficient decision-making strategies to decide which configurations should be conveniently enforced at run-time. In this work, we are interested in providing and evaluating potential abstractions achievable with self-adaptation transparently managing parallel executions. Therefore, we provide a new mechanism to support self-adaptation in applications with multiple parallel stages executed in multi-cores. Moreover, we reproduce, reimplement, and evaluate an existing decision-making strategy in our scenario. The observations from the results show that the proposed mechanism for self-adaptation can provide new parallelism abstractions and autonomous responsiveness at run-time. On the other hand, there is a need for more accurate decision-making strategies to enable efficient executions of applications in resource-constrained scenarios like multi-cores.